WO2022206978A1 - Roadside millimeter-wave radar calibration method based on vehicle-mounted positioning apparatus - Google Patents

Roadside millimeter-wave radar calibration method based on vehicle-mounted positioning apparatus Download PDF

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WO2022206978A1
WO2022206978A1 PCT/CN2022/084929 CN2022084929W WO2022206978A1 WO 2022206978 A1 WO2022206978 A1 WO 2022206978A1 CN 2022084929 W CN2022084929 W CN 2022084929W WO 2022206978 A1 WO2022206978 A1 WO 2022206978A1
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millimeter
roadside
trajectory data
calibration
wave radar
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PCT/CN2022/084929
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French (fr)
Chinese (zh)
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许军
赵聪
都州扬
陆日琪
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许军
马儒争
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Priority to CN202280026659.7A priority Critical patent/CN117836653A/en
Publication of WO2022206978A1 publication Critical patent/WO2022206978A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/003—Transmission of data between radar, sonar or lidar systems and remote stations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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Definitions

  • the invention belongs to the technical field of mobile vehicle detection and sensor detection target calibration, and relates to a method for calibrating a millimeter-wave radar installed on a roadside by using a vehicle-mounted positioning device.
  • millimeter-wave radar In the context of vehicle-road collaboration, ranging sensors such as millimeter-wave radar can keenly perceive the distance and speed of surrounding vehicles, image sensors can visually supplement the multi-dimensional information of surrounding vehicles, and the fusion of various sensors can significantly improve the perception of the environment ability.
  • roadside millimeter-wave radar has obvious advantages.
  • the significant advantage of millimeter-wave radar is that it can not be affected by weather and lighting conditions such as rain, snow, fog, etc., can work around the clock, and realize the detection of target position and speed and heading. It is an important means of environmental perception.
  • the roadside millimeter-wave radar is applied in the field of transportation, which can complete the functions of "speed measurement + distance measurement + angle measurement", realize multi-target detection and trajectory tracking, and at the same time, it can realize abnormal traffic event detection, multi-section traffic flow data statistics, intersections. Diversified functions such as holographic state perception.
  • the millimeter-wave radar installed on the roadside can assist lidar, video cameras, etc. to complete multi-sensor holographic perception, and achieve target-level scene semantic establishment of holographic fusion.
  • the original point cloud data that is, the original results of signal analysis and processing returned by the radar at different spatial positions when the radar is scanning; the other is the original point cloud data.
  • instant data which is the position, speed, heading, size of all traffic targets and the tracking number assigned to the target obtained by the radar on the basis of point cloud data analysis.
  • the calibration of the sensor refers to establishing the mapping relationship between the relative coordinate system of the device itself and the world coordinate system through certain technical means or methods.
  • the vehicle and road In the system of vehicle-road collaboration for information exchange, the vehicle and road must have a unified coordinate system to be able to accurately locate and fuse the perceived objects. Therefore, the vehicle-side and road-side sensing equipment must be calibrated. To ensure that the data obtained at both ends have a unified reference standard and can be converted to each other. At the same time, under the natural influence of roadside or vehicle-mounted sensors (wind vibration, bridge vibration), the pose of the device may undergo non-plastic shifts and changes. At this point, the external parameters of the device need to be recalibrated.
  • the millimeter-wave radar sensor maps the coordinates of the detection target in the world coordinate system to its corresponding relative coordinate system during measurement.
  • the coordinate mapping relationship is also different.
  • the external parameters of the millimeter-wave radar will change, and timely calibration is required to meet precise positioning and multi-sensor fusion.
  • One of the traditional calibration methods of roadside millimeter-wave radar equipment is to calibrate with the help of manual calibration objects.
  • the roadside camera installed in the same position is first calibrated.
  • lay the calibration chessboard diagram on the road within the common field of view of the video camera and the millimeter-wave radar and use Zhang Zhengyou's calibration method and RTK differential positioning technology to calibrate the internal and external parameters of the video camera.
  • the mapping relationship between the coordinates and the world latitude and longitude coordinates realizes the calibration of the internal and external parameters of the camera, and then completes the calibration of the millimeter wave radar by establishing the relationship between the video image pixel coordinate system and the radar coordinate system.
  • the calibration method relies too much on manual work, and is only suitable for small-scale calibration, which is difficult to apply in large-scale batches. And for the online autonomous driving system, this will reduce the autonomy of the autonomous driving system; and this solution requires other sensors to complete the indirect calibration of the millimeter-wave radar. And in the actual complex traffic environment, the sensor is vibrated and offset due to external factors such as strong wind and bridge vibration, or the attitude changes due to artificial rotation, resulting in the failure of the previously established coordinate system mapping relationship. At this time, the parameters of the sensor need to be Recalibrate.
  • Another traditional method of roadside millimeter-wave radar calibration is to select calibration objects or features in the natural environment.
  • the parameters are solved through the one-to-one correspondence between the world coordinate system of the selected calibration object or feature and the feature points in the millimeter-wave radar coordinate system.
  • This kind of method makes clear requirements on the shape, characteristics and position of the object to achieve accurate calibration.
  • This method can solve the problem that traditional calibration methods rely on manual labor, and can solve the problem of sensor re-calibration in real time. Different methods also achieve high accuracy in different specific application scenarios.
  • this method is not suitable for large-scale promotion due to the need for obvious static features or calibration objects on the roadside. Therefore, there is an urgent need for a practical method to solve the problem of roadside millimeter-wave radar calibration.
  • the on-board positioning device can achieve centimeter-level positioning accuracy, and can send its own position information to the cloud in real time. Therefore, using it as the calibration data source of roadside video and radar sensing equipment can greatly reduce the cost and difficulty of labor and positioning devices required for calibration, and effectively solve the problem of online calibration of target coordinates detected by multi-source sensing equipment.
  • the technical method adopted in the present invention is: a roadside millimeter wave radar calibration method based on a vehicle positioning device.
  • the invention uses a calibration vehicle with a vehicle-mounted positioning device, travels according to a pre-designed route and collects data, and processes and analyzes the collected data through a processing unit to realize the calibration of the roadside millimeter-wave radar.
  • the calibration vehicle with the vehicle-mounted positioning device described in the present invention can be divided into two categories according to different types of vehicles. Accurate location positioning and output real-time vehicle positioning data; one category refers to calibration vehicles with external positioning equipment.
  • the calibration vehicles are equipped with high-precision positioning devices, which can accurately locate the vehicle position and output real-time vehicle positioning data.
  • the roadside millimeter-wave radar mentioned in the present invention refers to the millimeter-wave radar that has been installed on the road.
  • the installation position of the roadside millimeter-wave radar can be divided into three categories according to different actual scenes and different installation locations. , that is, installed on the left side of the road, installed on the right side of the road, and installed in the center of the road. These three types of installation schemes are the "roadside" range described in this scheme, and have the ability to identify moving target vehicles and output detection. Target's positioning data.
  • the calibration parameter calibration mentioned in the present invention refers to the calibration of the radar calibration parameter matrix, that is, the correction and update of the conversion parameters of the millimeter-wave radar coordinate system to the coordinates of the world coordinate system.
  • the pre-designed route described in the present invention refers to the driving route designed according to the road alignment in the road, which is divided into a conventional route and a complex route: a conventional route is a route used for conventional calibration, which are respectively diagonal lines. , curve, loop and the combination of the above routes; complex route is the route used to verify the calibration result, divided into edge route, central mode, and the specific route is the combination of diagonal, curve and circular route.
  • the processing unit of the present invention is a computer with functions of data collection, data processing and data analysis.
  • the cloud processing unit can be selected to connect with the roadside millimeter-wave radar and the calibration vehicle with the vehicle-mounted positioning device;
  • the base station processing unit can be selected to be installed indoors or on the roadside as an independent base station;
  • the embedded embedded type processing unit embedded in the roadside millimeter wave radar or calibration vehicle.
  • ⁇ Resampling in the present invention refers to one of the steps of data analysis and processing performed by the processing unit.
  • the number of sampling points between the two trajectories is the same and the sampling time is aligned by methods such as interpolation or prediction. For two trajectories with the same sampling frequency and aligned sampling times, no resampling is required; for two trajectories with inconsistent sampling frequencies or different sampling times, at least one trajectory needs to be resampled.
  • the first trajectory data D 1 in the present invention refers to the original vehicle trajectory data output by a calibration vehicle with a vehicle-mounted positioning device traveling along a pre-designed conventional route, and the coordinate system of the data is the world coordinate system.
  • the resampled first trajectory data D 1 ′ in the present invention refers to trajectory data obtained after resampling the first trajectory data D 1 , and the coordinate system of the data is the world coordinate system
  • the second trajectory data D2 in the present invention refers to the trajectory data output by the roadside millimeter-wave radar to detect the target, and the coordinate system of the data is the radar coordinate system where the roadside millimeter-wave radar is located.
  • the third trajectory data D3 in the present invention refers to the trajectory data obtained by converting the second trajectory data D2 according to the calibration parameters of the roadside millimeter-wave radar, and the coordinate system of the data is the world coordinate system.
  • the resampled third trajectory data D 3 ′ in the present invention refers to trajectory data obtained by resampling the third trajectory data D 3 , and the coordinate system of the data is the world coordinate system.
  • the fourth trajectory data D4 in the present invention refers to the trajectory data output by the roadside millimeter-wave radar to detect the target in the verification step, and the coordinate system of the data is the location where the roadside millimeter-wave radar is located. Radar coordinate system.
  • the fifth trajectory data D5 described in the present invention refers to the original vehicle trajectory data outputted by the calibration vehicle with the vehicle-mounted positioning device traveling according to the pre-designed complex route in the verification step, and the coordinate system of the data is the world Coordinate System.
  • the fifth trajectory data D 5 ′ in the present invention refers to the trajectory data obtained by resampling the fifth trajectory data D 5 , and the coordinate system of the data is the world coordinate system.
  • the sixth trajectory data D6 in the present invention refers to the trajectory data obtained by converting the fourth trajectory data D4 according to the calibration parameters of the roadside millimeter-wave radar in the verification step, and the coordinate system of the data is world coordinate system.
  • the sixth trajectory data D 6 ′ in the present invention refers to the trajectory data obtained by resampling the sixth trajectory data D 6 , and the coordinate system of the data is the world coordinate system.
  • the spatiotemporal similarity value in the present invention refers to the value obtained by substituting the two trajectory data into the calculating method through the spatiotemporal similarity calculating method in the present invention.
  • the spatiotemporal similarity threshold in the present invention refers to a value obtained by means of artificial setting, expert advice or big data analysis, which can determine the accuracy of the calibration parameters of the radar.
  • the judgment in the present invention refers to the process of comparing the spatiotemporal similarity value of the present invention with the spatiotemporal similarity threshold.
  • the calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed conventional route, and outputs the first trajectory data and the second trajectory data;
  • the processing unit collects the first trajectory data and the second trajectory data generated by the calibration vehicle with the on-board positioning device and the roadside millimeter-wave radar, and obtains the third trajectory data after coordinate transformation, and compares the first trajectory data with the second trajectory data. Perform data processing and analysis on the third trajectory data, and output a spatiotemporal similarity value between the first trajectory data and the third trajectory data;
  • the processing unit judges that the parameters of the roadside millimeter-wave radar do not need to be calibrated, and the calibration ends; the processing unit judges that the parameters of the roadside millimeter-wave radar need to be calibrated, then the The parameters of the wave radar are calibrated;
  • the processing unit After calibrating the parameters of the roadside millimeter-wave radar, the processing unit performs the following verification steps:
  • the calibration vehicle with the on-board positioning device needs to drive within the coverage of the roadside millimeter-wave radar according to a pre-designed complex route or another conventional route, and output the fourth trajectory data and the fifth trajectory data;
  • step 3 For the outputted fourth track data and the fifth track data, step 3) is performed to output the spatiotemporal similarity value; for the outputted spatiotemporal similarity value, step 4) is performed.
  • the calibration parameters of the roadside millimeter-wave radar this time meet the requirements.
  • the calculated spatiotemporal similarity value is greater than the spatiotemporal similarity threshold, it can be processed according to the following three alternatives:
  • step 1) Re-execute step 1) to step 4), if the time-space similarity value obtained in step 4) is less than the space-time similarity threshold, the calibration ends; if the space-time similarity value obtained in step 4) is greater than or equal to the space-time similarity threshold, then The radar is calibrated as the fault radar reporting processing unit.
  • the installation angle of the roadside millimeter-wave radar is different according to the actual scene, and the installation position on the roadside is shown in Figure 2.
  • the on-board positioning device provided in the calibration vehicle with the external positioning device is generally a high-precision RTK differential positioning device. Mounted on the interior or roof of a calibration vehicle.
  • the sampling frequency of the calibration vehicle with the on-board positioning device is set at least not lower than the sampling frequency of the roadside millimeter-wave radar.
  • the sampling frequency is kept high, the accuracy of calibrating the roadside millimeter-wave radar is higher.
  • the sampling frequency of the vehicle positioning device is consistent with the sampling frequency of the roadside millimeter-wave radar and the sampling time is the same, no resampling process is required.
  • the sampling frequency of the vehicle positioning device is inconsistent with the sampling frequency of the roadside millimeter-wave radar or the sampling time point is different, at least one trajectory needs to be resampled. After resampling processing, the two trajectories can achieve the same number of sampling points and aligned sampling times.
  • the processing unit adjusts the time clock of the calibrated vehicle with the on-board positioning device and the roadside millimeter-wave radar so that it is strictly synchronized with the clock time of the processing unit to achieve clock synchronization.
  • the data points of the first trajectory data D 1 collected by the calibration vehicle with the on-board positioning device can be represented by the following vectors:
  • x v represents the X coordinate of the vehicle positioning device in the world coordinate system
  • y v represents the Y coordinate of the vehicle positioning device in the world coordinate system
  • z v represents the Z coordinate of the vehicle positioning device in the world coordinate system.
  • the data points of the second trajectory data D2 collected by the roadside millimeter-wave radar can be represented by the following vectors:
  • R r [x r ,y r ,z r ]
  • x r represents the X coordinate of the roadside millimeter-wave radar in the radar coordinate system
  • y r represents the Y coordinate of the roadside millimeter-wave radar in the reference coordinate system
  • z r represents the Z coordinate of the roadside millimeter-wave radar in the reference coordinate system.
  • the calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed conventional route, and generates the first trajectory data D1 and The second trajectory data D 2 .
  • the calibration vehicle with the vehicle-mounted positioning device travels according to a pre-designed conventional route, and the vehicle-mounted positioning device of the calibration vehicle continuously collects the vehicle's own positioning data to obtain the first trajectory data D 1 ; the roadside millimeter-wave radar continuously collects the road The echo data of the vehicle is calibrated in the middle to obtain the second trajectory data D 2 .
  • a conventional route refers to a diagonal straight line, a curved line, a circular route and a combination route of the above routes in the road, which are used for the conventional calibration of the roadside millimeter-wave radar in the present invention.
  • the first trajectory data D 1 is the calibration vehicle with the on-board positioning device, which travels within the coverage area of the roadside millimeter-wave radar according to a pre-designed conventional route (or complex route), and the on-board positioning device operates in different Real-time and uninterrupted collection and calibration of the vehicle's own positioning data.
  • Data can be represented by the following sets:
  • tr n indicates that the vehicle positioning device collects the nth track set at different times.
  • the second trajectory data D 2 is the trajectory echo data of the calibration vehicle continuously generated by the roadside millimeter-wave radar.
  • Data can be represented by the following sets:
  • tr m represents the m-th target trajectory set collected by the roadside millimeter-wave radar. Its expression is:
  • the processing unit obtains D 1 and D 2 generated by the calibration vehicle with the vehicle-mounted positioning device and the roadside millimeter-wave radar, and performs coordinate transformation on D 2 to obtain the third trajectory data D 3 . 3. Perform calculation and analysis to determine whether the roadside millimeter-wave radar needs to be calibrated. The specific process is shown in Figure 4.
  • one feature point of each point pair From the D 1 data generated by the vehicle positioning device, another feature point D 2 from roadside millimeter-wave radar.
  • the world coordinate system refers to selecting a reference coordinate system in the environment to describe the position of the calibration vehicle, and the coordinate system is called the world coordinate system.
  • the world coordinates are acquired by the on-board positioning device on the calibration vehicle.
  • the vehicle-mounted positioning device can provide the three-dimensional positioning results of the station in the specified coordinate system in real time.
  • the coordinates obtained by the roadside millimeter-wave radar are in the radar coordinate system.
  • the radar calibration parameter matrix in the present invention refers to a matrix that converts the radar coordinate system into the world coordinate system.
  • the radar calibration parameter matrix H is as follows:
  • (x r , y r , z r ) represent the three-dimensional coordinates of a point in D 2
  • (x v , y v , z v ) represent the three-dimensional coordinates of a point in D 1 .
  • the value of the radar calibration parameter matrix H can be obtained by solving the linear equation for the corresponding feature points manually selected in step 1).
  • D 2 in the radar coordinate system can be transformed into D 3 in the world coordinate system.
  • (x r , y r , z r ) represents a point in D 2
  • (x′ r , y′ r , z′ r ) represents the new three-dimensional coordinate data of the roadside millimeter-wave radar after conversion to the world coordinate system, That is, the point in the third trajectory data D3 .
  • the data collection frequency of the on-board positioning device on the calibration vehicle and the roadside millimeter-wave radar is relatively high, the collection of the two is not necessarily the same. Spatially, the time interval between the two sampling points distribution is inconsistent. Therefore, D 1 and D 3 cannot correspond one-to-one in space, and the judgment of the accuracy of the dot trace cannot be completed. To this end, it is necessary to resample the data by means of resampling, and re-correct it into two trajectories with the same number of sampling points and aligned sampling times. Wherein, when the sampling frequency of the vehicle-mounted positioning device is consistent with the sampling frequency of the roadside millimeter-wave radar and the sampling time is the same, no resampling process is required. When the sampling frequency of the vehicle positioning device is inconsistent with the sampling frequency of the roadside millimeter-wave radar or the sampling time point is different, at least one trajectory needs to be resampled.
  • the specific steps are:
  • the coefficients a, b, c, d of the cubic function G x can be solved by bringing in the abscissas of the adjacent four point traces in the trajectory data.
  • the coefficients e, f, g, h of the cubic function G y can be solved by bringing in the ordinates of the three adjacent point traces in the trajectory data.
  • the expression of the cubic function is as follows:
  • the trajectory point is in time with the point D 3 ' match.
  • sim(p,q) represents the similarity between point p and point q
  • It represents the sum of matching similarity of U point pairs in set C 2 , and this index is used to measure the similarity between D 1 ′ and D 3 ′.
  • f S represents the spatial similarity, which measures the size of the time difference between the point pairs in the matching point pair set C 2 .
  • the calculation formula is as follows:
  • f T represents the temporal similarity, which measures the size of the time difference between the point pairs in the matched point pair set C 2 .
  • the calculation formula is as follows:
  • f S represents the spatial similarity, which measures the distance between the abscissa and ordinate between the point pairs in the matching point pair set C 2 .
  • Measuring the similarity of point pairs from the spatial dimension directly reflects the accuracy of the calculated radar calibration parameter matrix; measuring the similarity of point pairs from the time dimension directly reflects the temporal proximity of matching point pairs , which indirectly reflects the accuracy of the calculated radar calibration parameter matrix.
  • weights The proportion of spatial similarity and temporal similarity can be adjusted freely, which can also achieve accurate evaluation when there is a clock synchronization problem.
  • ⁇ represents the weight of the similarity of the non-resampled points
  • (1- ⁇ ) represents the weight of the resampled points.
  • step (3) Calculate the similarity between D 1 ' and D 3 ' through step (3) If the similarity greater than or equal to the spatiotemporal similarity threshold ⁇ , it is considered that the radar calibration parameter matrix H meets the threshold requirements, and no recalibration is required; If it is less than the similarity threshold ⁇ , it is considered that the radar calibration parameter matrix H does not meet the threshold requirements, and the roadside millimeter-wave radar needs to be calibrated according to step (5).
  • step (4) When it is judged in step (4) that the radar calibration parameter matrix H does not meet the threshold requirements, the radar calibration parameter matrix of the roadside millimeter-wave radar needs to be recalculated and calibrated. After calibration, in order to ensure the accuracy of the results, the calibration results should be further verified. The specific process is shown in Figure 10.
  • step 1 Recalibrate.
  • step 3 to perform coordinate transformation, select a new feature matching point pair, recalculate the radar calibration parameter matrix H' for D 1 and D 2 , and update the radar calibration parameter matrix value.
  • the radar calibration parameter matrix H' is as follows:
  • the calibrated vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed complex route or another conventional route, and will bring the
  • the data generated by the calibration vehicle with the on-board positioning device is recorded as fifth trajectory data D 5
  • the data detected by the roadside millimeter-wave radar for target detection is recorded as fourth trajectory data D 4 .
  • the calibration vehicle can select a pre-designed complex route, as shown in Figure 4, the complex route refers to the straight line, curve, circular route and the combination route of the above routes in the center or edge of the road; calibration;
  • the vehicle can also choose another pre-designed conventional route, as shown in FIG. 3 , the conventional route refers to the diagonal straight line, the curve, the circular route and the combination route of the above routes in the road.
  • route selection a complex route with different characteristics from the conventional route or another conventional route can be selected for driving and verification based on the characteristics of the conventional route selected for the first time.
  • route selection it can also be fully based on the difference between the spatiotemporal similarity value calculated by the processing unit and the spatiotemporal similarity threshold value.
  • the targeted selection is more linear than a conventional route for first driving Another conventional route or a complex route that is similar to the result is verified; or when the difference is small, another conventional route or a complex route that is linearly different from the first conventional route is selected. , to verify the results.
  • the fourth trajectory data D 4 in the radar coordinate system can be transformed into the sixth trajectory data D 6 in the world coordinate system by the updated radar calibration parameter matrix H′.
  • the updated radar calibration parameter matrix H′ As follows:
  • (x r , y r , z r ) represents the fourth trajectory data point
  • (x' r , y' r , z' r ) represents the way after the transformation to the world coordinate system according to the solved radar calibration parameter matrix H'
  • the new three-dimensional coordinate data of the side millimeter wave radar is the sixth trajectory data point.
  • step ( 3 ) upload D5 and D6 to the processing unit for data collection and data analysis, that is, perform resampling calculation according to step 2 ) of step ( 3 ) to obtain resampled trajectory data D5 ' and D6 '.
  • the space-time similarity value calculation is performed on the trajectory data D 5 ′ and D 6 ′ obtained after resampling, to obtain the space-time similarity value of D 5 ′ and D 6 ′.
  • the calculated spatiotemporal similarity value is judged according to step (4). If the calculated spatiotemporal similarity value is less than or equal to the spatiotemporal similarity threshold, the calibration parameters of the roadside millimeter-wave radar meet the requirements; if the calculated spatiotemporal similarity value is greater than the spatiotemporal similarity threshold, the following three preparations can be used Options for processing:
  • the data collected by the on-board positioning device on the calibration vehicle is used as the true value data for the roadside millimeter-wave radar calibration, providing an automated and high-precision data source, which effectively improves the calibration accuracy of the roadside millimeter-wave radar.
  • using resampling as a preprocessing method for data matching with different sampling frequencies solves the problem that multi-source trajectory data with different sampling frequencies is difficult to compare.
  • the measurement method of spatiotemporal similarity is used to judge whether the roadside millimeter-wave radar needs to be calibrated. The judgment method is simple and feasible, and no manual calibration is required.
  • the conventional route and complex route driving scheme are used for the calibration vehicle design, and the multi-angle result verification is carried out on the calibration results of the roadside millimeter-wave radar.
  • Fig. 1 is the schematic flow chart of the present invention
  • FIG. 2 is a schematic diagram of the relative position of the roadside millimeter radar layout and the vehicle-mounted positioning device of the present invention
  • FIG. 3 is a schematic diagram of a conventional driving route for data collection
  • Figure 4 is a schematic diagram of a complex driving route for result verification
  • Fig. 5 is the schematic flow chart of processing unit acquisition data and analysis data
  • FIG. 6 is a schematic diagram of trajectory feature point selection
  • Figure 8 is a schematic diagram of the trajectory of the roadside millimeter radar layout and the vehicle positioning device with different sampling frequencies
  • Fig. 9 is a resampling schematic diagram
  • Figure 10 is a schematic flowchart of recalibration and result verification.
  • the invention relates to a roadside millimeter wave radar calibration method based on a vehicle positioning device. As shown in Figure 1, the present invention can be divided into five main steps:
  • the first step is the layout of the roadside millimeter-wave radar and the clock synchronization setting.
  • the on-board positioning device carried by the calibration vehicle can assist automatic driving vehicles or ordinary vehicles to perform high-precision positioning, with centimeter-level positioning accuracy, and can output the driving track points of the calibration vehicle in real time.
  • the invention uses it as a data reference value, so as to calibrate and update the calibration parameters of the roadside millimeter-wave radar.
  • the on-board positioning device carried by the calibration vehicle should be able to output the real-time position data of the calibration vehicle, and the sampling frequency should be set not less than the sampling frequency of the roadside millimeter-wave radar.
  • the roadside millimeter-wave radar is installed on the roadside and has been deployed at different positions on the road.
  • the deployed positions cover the complete range of the road surface.
  • the road site should be as open and open as possible, without high buildings or trees blocking, a single detection range About 300 meters, it can accurately measure the distance and speed of the detection target.
  • the position data of the moving object on the detected road is output according to the set sampling frequency.
  • the processing unit is located in the virtual cloud or in the base station, or is embedded in the roadside millimeter-wave radar or inside the calibration vehicle.
  • the processing unit is connected in real time with the roadside millimeter-wave radar and the calibrated vehicle with the on-board positioning device.
  • the base station connection or network timing is used to achieve clock synchronization.
  • the vehicle is calibrated to follow a pre-designed route and the data is uploaded to the processing unit.
  • the calibrated vehicle with the on-board positioning device drives along the road according to the pre-designed conventional route or complex route.
  • the conventional route is shown in Figure 3
  • the complex route is shown in Figure 4.
  • the calibration vehicle with the on-board positioning device In the process of driving, the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with timestamp in real time, which is recorded as the first trajectory data.
  • the data is located in the world coordinate system, and the first trajectory data is uploaded to the processing unit.
  • the roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the second trajectory data, and the data is located in the radar coordinate system, and the second trajectory data is uploaded to the processing unit.
  • Data collection is achieved by calibrating the driving of the vehicle.
  • the processing unit performs data processing and data analysis.
  • the processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively. As shown in Figure 5.
  • first trajectory data and the second trajectory data are not in the same coordinate system
  • coordinate transformation is performed on the second trajectory data.
  • the trajectory points of the first trajectory data and the second trajectory data as shown in FIG. 6 , more than four sets of feature point pairs are selected to solve the radar calibration parameter matrix of the roadside millimeter-wave radar.
  • the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion.
  • the two types of trajectories are in the same coordinate system.
  • the first track data and the third track data are resampled.
  • Resampling obtains the union of the first trajectory data and the sampling time points in the third trajectory data point set. And use the union of the obtained sampling time points to check the missing time points in the first track number and the third track data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling.
  • the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one. See Figure 9.
  • the spatiotemporal similarity of the first and third trajectory data after coordinate transformation and resampling is calculated. According to the expression of the spatiotemporal similarity, respectively substitute the first trajectory data point and the third trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the first trajectory data and the second trajectory data point.
  • the fourth step is to judge the accuracy of the calibration parameters.
  • the processing unit solves the spatiotemporal similarity value between the first trajectory data and the third trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold, the roadside millimeter-wave radar does not need to be recalibrated, and the process ends; if the solved spatiotemporal similarity value is greater than the preset accuracy threshold, it is considered that the roadside millimeter wave radar The wave radar needs to be recalibrated, the fifth step is performed, and the results are verified.
  • the fifth step is to recalibrate and verify the results.
  • step (4) When it is determined that the roadside millimeter-wave radar needs to be recalibrated in step (4), the current step (5) is performed to perform recalibration and result verification.
  • a schematic diagram of the specific process is shown in Figure 10.
  • the roadside millimeter-wave radar is recalibrated.
  • the calibration parameter matrix For the first trajectory data and the second trajectory data in the initial calibration, recalculate the calibration parameter matrix.
  • more than four sets of feature point pairs different from those in the initial calibration are reselected, and the radar calibration parameter matrix of the roadside millimeter-wave radar is updated.
  • the calibrated vehicle with the on-board positioning device re-runs along the road according to the pre-designed complex route.
  • the complex route is shown in Figure 4.
  • the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with time stamp in real time, which is recorded as the fifth trajectory data.
  • the data is located in the world coordinate system, and the fifth trajectory data is uploaded to the processing unit.
  • the roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the fourth trajectory data, and the data is located in the radar coordinate system, and the fourth trajectory data is uploaded to the processing unit.
  • the processing unit converts the fourth trajectory data point in the radar coordinate system to the world coordinate system according to the updated radar calibration parameter matrix, and obtains the sixth trajectory data to realize coordinate conversion.
  • the two types of trajectories are in the same coordinate system.
  • the processing unit verifies the results of the updated radar calibration parameter matrix.
  • the fifth track data and the sixth track data are resampled.
  • Re-sampling obtains the union of the fifth trajectory data and the sampling time points in the sixth trajectory data point set. And use the union of the obtained sampling time points to check the missing time points in the fifth track number and the sixth track data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling.
  • the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the fifth trajectory data and the sixth trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
  • the spatiotemporal similarity of the fifth and sixth trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the fifth trajectory data point and the sixth trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the fifth trajectory data and the second trajectory data point.
  • the processing unit After the processing unit has solved the spatiotemporal similarity value between the fifth trajectory data and the sixth trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold, the roadside millimeter-wave radar does not need to be recalibrated, and the process ends; if the solved spatiotemporal similarity value is greater than the preset accuracy threshold, it is considered that the roadside millimeter wave radar The wave radar needs to be recalibrated, the fifth step is performed, and the results are verified.
  • the data collected by the on-board positioning device on the calibration vehicle is used as the true value data for the roadside millimeter-wave radar calibration, providing an automated and high-precision data source, which effectively improves the calibration accuracy of the roadside millimeter-wave radar.
  • using resampling as a preprocessing method for data matching with different sampling frequencies solves the problem that multi-source trajectory data with different sampling frequencies is difficult to compare.
  • the measurement method of spatiotemporal similarity is used to judge whether the roadside millimeter-wave radar needs to be calibrated. The judgment method is simple and feasible, and no manual calibration is required.
  • the conventional route and complex route driving scheme are used for the calibration vehicle design, and the multi-angle result verification is carried out on the calibration results of the roadside millimeter-wave radar.
  • Embodiment 1 is as follows:
  • An experimental scene was set up in the SAIC Innovation Port Park, Jiading District, Shanghai.
  • roadside millimeter-wave radars are set at equal intervals of 250 meters on the roadside, with an installation height of 5.0 meters and a depression angle of 10°.
  • a high-precision RTK positioning device is installed on the normal vehicle used for calibration, and the RTK is installed on the top of the calibration vehicle, and the angular relationship with the vehicle is kept stable.
  • the cloud server is connected to the processing unit, and the network is used to uniformly time the roadside millimeter-wave radar, the RTK positioning device for calibrating the vehicle, and the processing unit, so that the three can keep their clocks synchronized.
  • the calibration vehicle drives according to the pre-designed conventional route (curve), and uploads the data to the cloud processing unit.
  • a calibrated vehicle with a high-precision RTK positioning device travels normally along the road of the park and follows the curved route in the pre-designed conventional route.
  • the first trajectory data collected by the RTK high-precision calibration vehicle is uploaded to the cloud processing unit in real time; the second trajectory data collected by the roadside millimeter-wave radar is uploaded to the cloud processing unit in real time. Realize data collection and upload.
  • the cloud processing unit performs data processing and data analysis.
  • the sampling frequency of the collected second trajectory data is 10 Hz; the collection frequency of the collected first trajectory data of the calibration vehicle is 100 Hz.
  • the cloud processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively.
  • the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion.
  • the two types of trajectories are in the same coordinate system.
  • 2Resampling In order to solve the problem that the sampling frequencies of the first trajectory data and the second trajectory data are inconsistent, the first trajectory data and the third trajectory data are resampled. Resampling obtains the union of the sampling time points in the first trajectory data and the third trajectory data point set, and the union is [1516783680.132207,...1516783954.932218]. And use the union of the obtained sampling time points to check the missing time points in the first trajectory data and the third trajectory data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
  • the calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
  • the solution result is compared with the preset threshold ⁇ . It is found that the solution result is less than the preset threshold, indicating that the roadside millimeter-wave radar does not need to be recalibrated.
  • An experimental scene was set up in the SAIC Innovation Port Park, Jiading District, Shanghai.
  • roadside millimeter-wave radars are set at equal intervals of 250 meters on the roadside, with an installation height of 5.0 meters and a depression angle of 10°.
  • a high-precision RTK positioning device is installed on the normal vehicle used for calibration.
  • the RTK is installed on the top of the calibration vehicle, and the angular relationship with the vehicle is kept stable.
  • the cloud server is connected to the processing unit, and the network is used to uniformly time the roadside millimeter-wave radar, the RTK positioning equipment for calibrating the vehicle, and the processing unit, so that the three keep the clock synchronization.
  • the calibration vehicle travels along a pre-designed conventional route (circular), and uploads the data to the roadside processing unit.
  • a calibrated vehicle with a high-precision RTK positioning device travels normally along the road of the park, following a circular route in a pre-designed conventional route.
  • the first trajectory data collected by the RTK high-precision calibration vehicle is uploaded to the cloud processing unit in real time; the second trajectory data collected by the roadside millimeter-wave radar is uploaded to the cloud processing unit in real time. Realize data collection and upload.
  • the roadside processing unit performs data processing and data analysis.
  • the sampling frequency of the collected second trajectory data is 10 Hz; the collection frequency of the collected first trajectory data of the calibration vehicle is 100 Hz.
  • the cloud processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively.
  • the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion.
  • the two types of trajectories are in the same coordinate system.
  • 2Resampling In order to solve the problem that the sampling frequencies of the first trajectory data and the second trajectory data are inconsistent, the first trajectory data and the third trajectory data are resampled. Resampling obtains the union of the sampling time points in the first trajectory data and the third trajectory data point set, and the union is [1516783680.132207,...1516783954.932218]. And use the union of the obtained sampling time points to check the missing time points in the first trajectory data and the third trajectory data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
  • the calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
  • the solution result is compared with the preset threshold ⁇ . It is found that the solution result is greater than the preset threshold, and it is considered that the roadside millimeter-wave radar needs to be recalibrated, and the fifth step is performed, and the result is verified.
  • step (4) When it is determined that the roadside millimeter-wave radar needs to be recalibrated in step (4), the current step (5) is performed to perform recalibration and result verification.
  • a schematic diagram of the specific process is shown in Figure 10.
  • the roadside millimeter-wave radar is recalibrated.
  • the calibration parameter matrix In the trajectory points of the first trajectory data and the second trajectory data, more than four sets of feature point pairs different from those in the initial calibration are reselected, and the radar calibration parameter matrix H' of the roadside millimeter-wave radar is updated.
  • the calibrated vehicle with the on-board positioning device re-runs along the road according to the pre-designed complex route (route combination 1), as shown in Figure 4.
  • the calibration vehicle with the on-board positioning device In the process of driving, the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with time stamp in real time, which is recorded as the fifth trajectory data.
  • the data is located in the world coordinate system, and the fifth trajectory data is uploaded to the processing unit.
  • the roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the fourth trajectory data, and the data is located in the radar coordinate system, and the fourth trajectory data is uploaded to the roadside processing unit.
  • the processing unit converts the fourth trajectory data point in the radar coordinate system to the world coordinate system according to the updated radar calibration parameter matrix, and obtains the sixth trajectory data to realize coordinate conversion.
  • the two types of trajectories are in the same coordinate system.
  • the processing unit verifies the results of the updated radar calibration parameter matrix.
  • the fifth track data and the sixth track data are resampled.
  • Re-sampling obtains the union of the sampling time points in the fifth trajectory data and the sixth trajectory data point set, and the union is [1516783680.132207,...1516784063.852348].
  • the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the fifth trajectory data and the sixth trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
  • the spatiotemporal similarity of the fifth and sixth trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the fifth trajectory data point and the sixth trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the fifth trajectory data and the second trajectory data point.
  • the calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
  • the processing unit After the processing unit has solved the spatiotemporal similarity value between the fifth trajectory data and the sixth trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold ⁇ , the recalibration of the roadside millimeter-wave radar is more accurate this time, and the process ends.

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Abstract

A roadside millimeter-wave radar calibration method based on a vehicle-mounted positioning apparatus, which method belongs to the technical field of moving vehicle detection and sensor detection target calibration. In the method, a calibration vehicle having a vehicle-mounted positioning apparatus travels according to a pre-designed route and collects data, and the collected data is processed and analyzed by means of a processing unit, such that the calibration of a roadside millimeter-wave radar is realized.

Description

一种基于车载定位装置的路侧毫米波雷达校准方法A roadside millimeter-wave radar calibration method based on vehicle positioning device 技术领域technical field
本发明属于移动车辆检测和传感器检测目标标定技术领域，涉及运用车载定位装置对路侧已经安装毫米波雷达进行校准的方法。The invention belongs to the technical field of mobile vehicle detection and sensor detection target calibration, and relates to a method for calibrating a millimeter-wave radar installed on a roadside by using a vehicle-mounted positioning device.
背景技术Background technique
在车路协同背景下，测距传感器如毫米波雷达等能够敏锐感知周围车辆的距离、速度，图像类传感器能够从视觉上补充周围车辆的多维信息，多样传感器的融合能够显著提升对环境的感知能力。在这当中，路侧毫米波雷达优势明显。毫米波雷达显著优势在于，能够不受雨雪雾等天气和光照条件的影响，能全天候工作，实现对目标位置和速度航向的检测，是重要的环境感知手段。路侧毫米波雷达应用在交通领域，可以完成“测速+测距+测角”的功能，实现多目标检测和轨迹跟踪，同时能够实现交通运行异常事件检测、多断面交通流数据统计、交叉口全息状态感知等多样化的功能。与此同时，在车路协同和自动驾驶的场景下，安装在路侧的毫米波雷达能够辅助激光雷达、视频摄像头等完成多传感器的全息感知，实现全息融合的目标级场景语义建立。In the context of vehicle-road collaboration, ranging sensors such as millimeter-wave radar can keenly perceive the distance and speed of surrounding vehicles, image sensors can visually supplement the multi-dimensional information of surrounding vehicles, and the fusion of various sensors can significantly improve the perception of the environment ability. Among them, roadside millimeter-wave radar has obvious advantages. The significant advantage of millimeter-wave radar is that it can not be affected by weather and lighting conditions such as rain, snow, fog, etc., can work around the clock, and realize the detection of target position and speed and heading. It is an important means of environmental perception. The roadside millimeter-wave radar is applied in the field of transportation, which can complete the functions of "speed measurement + distance measurement + angle measurement", realize multi-target detection and trajectory tracking, and at the same time, it can realize abnormal traffic event detection, multi-section traffic flow data statistics, intersections. Diversified functions such as holographic state perception. At the same time, in the scenarios of vehicle-road collaboration and autonomous driving, the millimeter-wave radar installed on the roadside can assist lidar, video cameras, etc. to complete multi-sensor holographic perception, and achieve target-level scene semantic establishment of holographic fusion.
智慧道路对毫米波雷达在数据层面，集中在两个方面的需求，一方面是原始的点云数据，也就是雷达在扫描时，不同空间位置，给雷达返回的信号分析处理的原始结果；另一方面是即时数据，这是雷达在点云数据的基础上进行分析得出的所有交通目标的位置、速度、航向、尺寸及赋予该目标的跟踪编号，这两类原始数据给到边缘计算器后，与其他感知手段实现空间和时间上的融合，实现对交通环境的高精度、多维度的感知，再通过RSU进行感知结果的发布。At the data level, Smart Road focuses on two requirements for millimeter wave radar. On the one hand, the original point cloud data, that is, the original results of signal analysis and processing returned by the radar at different spatial positions when the radar is scanning; the other is the original point cloud data. On the one hand, there is instant data, which is the position, speed, heading, size of all traffic targets and the tracking number assigned to the target obtained by the radar on the basis of point cloud data analysis. These two types of raw data are given to the edge calculator. Afterwards, it realizes spatial and temporal integration with other sensing means to achieve high-precision, multi-dimensional perception of the traffic environment, and then publishes the perception results through RSU.
实现上述融合感知一个重要的前提就是数据标定。传感器的标定是指，通过一定的技术手段或方法，建立设备自身相对坐标系与世界坐标系之间的映射关系。An important prerequisite for realizing the above fusion perception is data calibration. The calibration of the sensor refers to establishing the mapping relationship between the relative coordinate system of the device itself and the world coordinate system through certain technical means or methods.
在车路协同进行信息交互的系统中，车、路两端要有统一的坐标系才能够进行对感知的物体做出精准的定位与融合，因此必须对车端与路侧感知设备进行标定，以确保两端获得的数据有一个统一的参照标准，并能够互相转换。与此同时，路侧或车载的传感器在自然影响(风振、桥振)下，设备的位姿可能会发生非塑性的偏移和改变。此时需要对设备的外部参数进行重新校准。In the system of vehicle-road collaboration for information exchange, the vehicle and road must have a unified coordinate system to be able to accurately locate and fuse the perceived objects. Therefore, the vehicle-side and road-side sensing equipment must be calibrated. To ensure that the data obtained at both ends have a unified reference standard and can be converted to each other. At the same time, under the natural influence of roadside or vehicle-mounted sensors (wind vibration, bridge vibration), the pose of the device may undergo non-plastic shifts and changes. At this point, the external parameters of the device need to be recalibrated.
毫米波雷达传感器在测量时将世界坐标系下的检测目标坐标映射至自身对应的相对坐标系，但由于雷达传感器的安装位置、姿态和角度不同，其坐标映射关系也有所差异。同时，由于自然桥振、风振和外界环境的影响，毫米波雷达的外参会产生改变，需要进行及时的校准，以满足精确定位和多传感器的融合。The millimeter-wave radar sensor maps the coordinates of the detection target in the world coordinate system to its corresponding relative coordinate system during measurement. However, due to the different installation positions, attitudes and angles of the radar sensors, the coordinate mapping relationship is also different. At the same time, due to the influence of natural bridge vibration, wind vibration and the external environment, the external parameters of the millimeter-wave radar will change, and timely calibration is required to meet precise positioning and multi-sensor fusion.
现有方案中对于路侧毫米波雷达的校准研究较少，多数文献描述车载毫米波雷达在出厂前、下线后进行的厂内校准，以及激光雷达、摄像机等其它路侧感知设备的校准技术，较少提出对于路侧毫米波雷达的校准方案。There are few researches on the calibration of roadside millimeter-wave radars in the existing solutions. Most literatures describe the in-factory calibration of vehicle-mounted millimeter-wave radars before leaving the factory and after they are offline, as well as the calibration technology of other roadside sensing devices such as lidar and cameras. , few proposed calibration schemes for roadside millimeter-wave radars.
现有的校准方法根据原理的不同，可以大致分为基于几何的方法、基于运动的方法、基于互信息的方法和基于深度学习的方法。该类方法的本质是，依据已经标定好的某类传感器，从不同的传感器中分别提取特征，根据所提取特征在不同坐标系下的坐标映射关系来进行标定，该类方法是不依赖于标定物、基于特征点(线、面)的快速标定方法，但十分依赖于已经标定好的其他传感器。Existing calibration methods can be roughly divided into geometry-based methods, motion-based methods, mutual information-based methods, and deep learning-based methods according to different principles. The essence of this type of method is to extract features from different sensors according to a certain type of sensor that has been calibrated, and perform calibration according to the coordinate mapping relationship of the extracted features in different coordinate systems. This type of method does not depend on calibration. It is a fast calibration method based on feature points (line, surface), but it is very dependent on other sensors that have been calibrated.
传统的路侧毫米波雷达设备校准方法之一是通过借助人工标定物进行校准。例如为了对路侧毫米波雷达进行标定，首先对安装在同一位置的路侧摄像机进行标定。在同一视野范围内，将标定棋盘图铺设在视频摄像头与毫米波雷达共同的视野范围内的路面上，采用张正友标定法和RTK差分定位技术标定视频摄像头内部参数与外部参数，通过建立视频图像像素坐标与世界经纬度坐标的映射关系，实现对摄像头内外参数的标定，再通过建立视频图像像素坐标系与雷达坐标系之间的关系，完成对毫米波雷达的标定。该方法应用广泛但缺点也十分明显：标定方法过于依赖人工，仅适用于小范围内的标定，难以大规模的批量应用。并且对于在线的自动驾驶系统而言，这将降低自动驾驶系统的自治性；并且该方案需要其他传感器，才能完成对毫米波雷达的间接标定。并且在实际的复杂交通环境中，传感器因强风、桥振等外部因素发生振动偏移，或因人为的旋转产生姿态的变化，导致之前建立的坐标系映射关系失效，此时传感器的参数就需要重新标定。One of the traditional calibration methods of roadside millimeter-wave radar equipment is to calibrate with the help of manual calibration objects. For example, in order to calibrate the roadside millimeter-wave radar, the roadside camera installed in the same position is first calibrated. In the same field of view, lay the calibration chessboard diagram on the road within the common field of view of the video camera and the millimeter-wave radar, and use Zhang Zhengyou's calibration method and RTK differential positioning technology to calibrate the internal and external parameters of the video camera. The mapping relationship between the coordinates and the world latitude and longitude coordinates realizes the calibration of the internal and external parameters of the camera, and then completes the calibration of the millimeter wave radar by establishing the relationship between the video image pixel coordinate system and the radar coordinate system. This method is widely used but has obvious shortcomings: the calibration method relies too much on manual work, and is only suitable for small-scale calibration, which is difficult to apply in large-scale batches. And for the online autonomous driving system, this will reduce the autonomy of the autonomous driving system; and this solution requires other sensors to complete the indirect calibration of the millimeter-wave radar. And in the actual complex traffic environment, the sensor is vibrated and offset due to external factors such as strong wind and bridge vibration, or the attitude changes due to artificial rotation, resulting in the failure of the previously established coordinate system mapping relationship. At this time, the parameters of the sensor need to be Recalibrate.
另一种传统的路侧毫米波雷达标定方法是选择自然环境中的标定物体或特征。通过所选择标定物体或特征的世界坐标系，与毫米波雷达坐标系下的特征点对一一对应，求解参数。该类方法对物体的形状、特征以及位置做出了明确的要求，方可实现准确的标定。该方法可以解决传统标定方法依赖于人工的问题，并且能够实时的解决传感器重新标定的难题，不同方法在不同具体的应用场景下也达到了很高的精度。但该方法由于需要路侧有特征明显的静态特征或标定物，不适宜大范围推广。为此，亟需一种实用性强的方法解决路侧毫米波雷达校准的问题。Another traditional method of roadside millimeter-wave radar calibration is to select calibration objects or features in the natural environment. The parameters are solved through the one-to-one correspondence between the world coordinate system of the selected calibration object or feature and the feature points in the millimeter-wave radar coordinate system. This kind of method makes clear requirements on the shape, characteristics and position of the object to achieve accurate calibration. This method can solve the problem that traditional calibration methods rely on manual labor, and can solve the problem of sensor re-calibration in real time. Different methods also achieve high accuracy in different specific application scenarios. However, this method is not suitable for large-scale promotion due to the need for obvious static features or calibration objects on the roadside. Therefore, there is an urgent need for a practical method to solve the problem of roadside millimeter-wave radar calibration.
随着车路协同技术的不断发展，带有高精度车载定位设备的车辆渗透率不断提高。由于自动驾驶技术的需要，车载定位装置可达到厘米级的定位精度，且能够实时向云端发送自身位置信息。因此，将其作为路侧视频和雷达感知设备的标定数据来源，可以大大降低标定所需人工和定位装置的成本和难度，有效解决多源感知设备检测目标坐标在线标定的难题。With the continuous development of vehicle-road coordination technology, the penetration rate of vehicles with high-precision vehicle positioning equipment continues to increase. Due to the needs of autonomous driving technology, the on-board positioning device can achieve centimeter-level positioning accuracy, and can send its own position information to the cloud in real time. Therefore, using it as the calibration data source of roadside video and radar sensing equipment can greatly reduce the cost and difficulty of labor and positioning devices required for calibration, and effectively solve the problem of online calibration of target coordinates detected by multi-source sensing equipment.
现有专利：Existing patents:
专利CN 111929652 APatent CN 111929652 A
专利CN 110703254 APatent CN 110703254 A
美国专利US 6927725 B2US Patent US 6927725 B2
美国专利US 2015/0070207 A1US Patent US 2015/0070207 A1
美国专利US 2014/0240690 A1US Patent US 2014/0240690 A1
专利CN 111060881 APatent CN 111060881 A
发明内容SUMMARY OF THE INVENTION
为了解决对路侧毫米波雷达进行校准的问题，本发明采用的技术方法是：一种基于车载定位装置的路侧毫米波雷达校准方法。本发明使用带有车载定位装置的校准车辆，按照预先设计的路线行驶并采集数据，通过处理单元对所采集的数据进行处理分析，实现路侧毫米波雷达的校准。In order to solve the problem of calibrating the roadside millimeter wave radar, the technical method adopted in the present invention is: a roadside millimeter wave radar calibration method based on a vehicle positioning device. The invention uses a calibration vehicle with a vehicle-mounted positioning device, travels according to a pre-designed route and collects data, and processes and analyzes the collected data through a processing unit to realize the calibration of the roadside millimeter-wave radar.
【术语解释】【Explanation of terms】
●本发明中所述的带有车载定位装置的校准车辆按照车辆类型不同可划分为两类，一类指内嵌式自动驾驶车辆，内嵌安装有高精度定位装置，自动驾驶车辆能够对车辆位置进行精准定位，输出实时的车辆定位数据；一类指外带定位设备的校准车辆，校准车辆搭载高精度定位装置，能够对车辆位置进行精准定位，并输出实时的车辆定位数据。The calibration vehicle with the vehicle-mounted positioning device described in the present invention can be divided into two categories according to different types of vehicles. Accurate location positioning and output real-time vehicle positioning data; one category refers to calibration vehicles with external positioning equipment. The calibration vehicles are equipped with high-precision positioning devices, which can accurately locate the vehicle position and output real-time vehicle positioning data.
●本发明中所述的路侧毫米波雷达，是指已经安装在道路上的毫米波雷达，所述路侧毫米波雷达的安装位置根据实际场景的不同、安装所处位置不同可分三类，即安装于道 路左侧、安装于道路右侧以及安装于道路的中央，该三类安装方案均为本方案所述的“路侧”范围，并具有识别移动目标车辆的能力，能够输出检测目标的定位数据。The roadside millimeter-wave radar mentioned in the present invention refers to the millimeter-wave radar that has been installed on the road. The installation position of the roadside millimeter-wave radar can be divided into three categories according to different actual scenes and different installation locations. , that is, installed on the left side of the road, installed on the right side of the road, and installed in the center of the road. These three types of installation schemes are the "roadside" range described in this scheme, and have the ability to identify moving target vehicles and output detection. Target's positioning data.
●本发明中所述的标定参数校准，是指对雷达校准参数矩阵的校准，即对毫米波雷达坐标系转换至世界坐标系坐标的转换参数的修正与更新。●The calibration parameter calibration mentioned in the present invention refers to the calibration of the radar calibration parameter matrix, that is, the correction and update of the conversion parameters of the millimeter-wave radar coordinate system to the coordinates of the world coordinate system.
●本发明中所述的预先设计的路线，是指在道路中依据道路线型所设计的行驶路线，分为常规路线与复杂路线：常规路线是用于常规校准的路线，分别为对角线、曲线、环形以及以上路线的组合；复杂路线是用于验证校准结果的路线，分为边缘路线、中央模式，具体路线为对角线、曲线以及环形线路的组合。The pre-designed route described in the present invention refers to the driving route designed according to the road alignment in the road, which is divided into a conventional route and a complex route: a conventional route is a route used for conventional calibration, which are respectively diagonal lines. , curve, loop and the combination of the above routes; complex route is the route used to verify the calibration result, divided into edge route, central mode, and the specific route is the combination of diagonal, curve and circular route.
●本发明所述的处理单元，是具有数据收集、数据处理与数据分析功能的计算机。本发明中，可选用云端处理单元，使之与路侧毫米波雷达、与带有车载定位设备的校准车辆连接；可选用基站处理单元，作为独立基站在室内或路侧安装；可选用内嵌式处理单元，内嵌于路侧毫米波雷达或校准车辆。● The processing unit of the present invention is a computer with functions of data collection, data processing and data analysis. In the present invention, the cloud processing unit can be selected to connect with the roadside millimeter-wave radar and the calibration vehicle with the vehicle-mounted positioning device; the base station processing unit can be selected to be installed indoors or on the roadside as an independent base station; the embedded embedded type processing unit, embedded in the roadside millimeter wave radar or calibration vehicle.
●本发明所述的重采样是指处理单元所进行的数据分析与处理的步骤之一，通过插值或预测等方法使得两条轨迹间的采样点数量相同且采样时间对齐。对两条采样频率一致且采样时间对齐的轨迹而言，无需进行重采样；对两条采样频率不一致或采样时间不相同的轨迹而言，至少一条轨迹需要进行重采样。●Resampling in the present invention refers to one of the steps of data analysis and processing performed by the processing unit. The number of sampling points between the two trajectories is the same and the sampling time is aligned by methods such as interpolation or prediction. For two trajectories with the same sampling frequency and aligned sampling times, no resampling is required; for two trajectories with inconsistent sampling frequencies or different sampling times, at least one trajectory needs to be resampled.
●本发明所述的第一轨迹数据D 1是指带有车载定位装置的校准车辆按照预先设计的常规路线行驶，所输出的原始车辆轨迹数据，该数据的坐标系为世界坐标系。 ●The first trajectory data D 1 in the present invention refers to the original vehicle trajectory data output by a calibration vehicle with a vehicle-mounted positioning device traveling along a pre-designed conventional route, and the coordinate system of the data is the world coordinate system.
●本发明所述的经过重采样的第一轨迹数据D 1′是指对所述第一轨迹数据D 1进行重采样后所得到的轨迹数据，该数据的坐标系为世界坐标系 The resampled first trajectory data D 1 ′ in the present invention refers to trajectory data obtained after resampling the first trajectory data D 1 , and the coordinate system of the data is the world coordinate system
●本发明所述的第二轨迹数据D 2是指路侧毫米波雷达所输出的对目标进行检测得到的轨迹数据，该数据的坐标系为路侧毫米波雷达所处于的雷达坐标系。 ● The second trajectory data D2 in the present invention refers to the trajectory data output by the roadside millimeter-wave radar to detect the target, and the coordinate system of the data is the radar coordinate system where the roadside millimeter-wave radar is located.
●本发明所述的第三轨迹数据D 3是指对第二轨迹数据D 2按照路侧毫米波雷达的校准参数进行转换后所得到的轨迹数据，该数据的坐标系为世界坐标系。 ●The third trajectory data D3 in the present invention refers to the trajectory data obtained by converting the second trajectory data D2 according to the calibration parameters of the roadside millimeter-wave radar, and the coordinate system of the data is the world coordinate system.
●本发明所述的经过重采样的第三轨迹数据D 3′是指对所述第三轨迹数据D 3进行重采样后所得到的轨迹数据，该数据的坐标系为世界坐标系。 ● The resampled third trajectory data D 3 ′ in the present invention refers to trajectory data obtained by resampling the third trajectory data D 3 , and the coordinate system of the data is the world coordinate system.
●本发明所述的第四轨迹数据D 4是指在验证步骤中，路侧毫米波雷达所输出的对目标进行检测得到的轨迹数据，该数据的坐标系为路侧毫米波雷达所处于的雷达坐标系。 The fourth trajectory data D4 in the present invention refers to the trajectory data output by the roadside millimeter-wave radar to detect the target in the verification step, and the coordinate system of the data is the location where the roadside millimeter-wave radar is located. Radar coordinate system.
●本发明所述的第五轨迹数据D 5是指在验证步骤中，带有车载定位装置的校准车辆按照预先设计的复杂路线行驶，所输出的原始车辆轨迹数据，该数据的坐标系为世界坐标系。 The fifth trajectory data D5 described in the present invention refers to the original vehicle trajectory data outputted by the calibration vehicle with the vehicle-mounted positioning device traveling according to the pre-designed complex route in the verification step, and the coordinate system of the data is the world Coordinate System.
●本发明所述的第五轨迹数据D 5′是指是指对所述第五轨迹数据D 5进行重采样后所得到的轨迹数据，该数据的坐标系为世界坐标系。 ●The fifth trajectory data D 5 ′ in the present invention refers to the trajectory data obtained by resampling the fifth trajectory data D 5 , and the coordinate system of the data is the world coordinate system.
●本发明所述的第六轨迹数据D 6是指在验证步骤中，对第四轨迹数据D 4按照路侧毫米波雷达的校准参数进行转换后所得到的轨迹数据，该数据的坐标系为世界坐标系。 The sixth trajectory data D6 in the present invention refers to the trajectory data obtained by converting the fourth trajectory data D4 according to the calibration parameters of the roadside millimeter-wave radar in the verification step, and the coordinate system of the data is world coordinate system.
●本发明所述的第六轨迹数据D 6′是指对所述第六轨迹数据D 6进行重采样后所得到的轨迹数据，该数据的坐标系为世界坐标系。 ● The sixth trajectory data D 6 ′ in the present invention refers to the trajectory data obtained by resampling the sixth trajectory data D 6 , and the coordinate system of the data is the world coordinate system.
●本发明所述的时空相似度数值是指通过本发明所述的时空相似度计算方法，将两条轨迹数据代入该计算方法中所求得的数值。●The spatiotemporal similarity value in the present invention refers to the value obtained by substituting the two trajectory data into the calculating method through the spatiotemporal similarity calculating method in the present invention.
●本发明所述的时空相似度阈值是指，通过人为设定、专家建议或大数据分析等手段，所得出的能够判定雷达的标定参数准确程度的数值。●The spatiotemporal similarity threshold in the present invention refers to a value obtained by means of artificial setting, expert advice or big data analysis, which can determine the accuracy of the calibration parameters of the radar.
●本发明所述的判断是指将本发明所述的时空相似度数值与时空相似度阈值进行大小比较的过程。●The judgment in the present invention refers to the process of comparing the spatiotemporal similarity value of the present invention with the spatiotemporal similarity threshold.
本发明专利解决其技术问题采用以下步骤，其整体流程如图1：The patent of the present invention adopts the following steps to solve its technical problems, and its overall flow is as shown in Figure 1:
1)对校准车辆的车载定位装置设置不小于路侧毫米波雷达的采样频率，对带有车载定位装置的校准车辆、路侧毫米波雷达以及处理单元设置时钟同步；1) Set the sampling frequency of the on-board positioning device of the calibration vehicle not less than that of the roadside millimeter-wave radar, and set the clock synchronization for the calibration vehicle with the on-board positioning device, the roadside millimeter-wave radar and the processing unit;
2)带有车载定位装置的校准车辆按照预先设计的一种常规路线在路侧毫米波雷达的覆盖范围内进行行驶，输出第一轨迹数据与第二轨迹数据；2) The calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed conventional route, and outputs the first trajectory data and the second trajectory data;
3)处理单元收集带有车载定位装置的校准车辆与路侧毫米波雷达所产生的第一轨迹数据、第二轨迹数据，并进行坐标转换后得出第三轨迹数据，对第一轨迹数据与第三轨迹数据进行数据处理与分析，输出第一轨迹数据与第三轨迹数据的时空相似度数值；3) The processing unit collects the first trajectory data and the second trajectory data generated by the calibration vehicle with the on-board positioning device and the roadside millimeter-wave radar, and obtains the third trajectory data after coordinate transformation, and compares the first trajectory data with the second trajectory data. Perform data processing and analysis on the third trajectory data, and output a spatiotemporal similarity value between the first trajectory data and the third trajectory data;
4)处理单元根据时空相似度数值与时空相似度阈值，判断路侧毫米波雷达的参数无需校准，则本次校准结束；处理单元判断路侧毫米波雷达的参数需要校准，则对路侧毫米波雷达的参数进行校准；4) According to the spatiotemporal similarity value and the spatiotemporal similarity threshold, the processing unit judges that the parameters of the roadside millimeter-wave radar do not need to be calibrated, and the calibration ends; the processing unit judges that the parameters of the roadside millimeter-wave radar need to be calibrated, then the The parameters of the wave radar are calibrated;
5)处理单元在对路侧毫米波雷达的参数进行校准后，进行如下验证步骤：5) After calibrating the parameters of the roadside millimeter-wave radar, the processing unit performs the following verification steps:
带有车载定位装置的校准车辆需要按照预先设计的一种复杂路线或另一种常规路线在路侧毫米波雷达的覆盖范围内进行行驶，输出第四轨迹数据与第五轨迹数据；The calibration vehicle with the on-board positioning device needs to drive within the coverage of the roadside millimeter-wave radar according to a pre-designed complex route or another conventional route, and output the fourth trajectory data and the fifth trajectory data;
对所输出的第四轨迹数据与第五轨迹数据，执行步骤3)输出时空相似度数值；对所输出的时空相似度数值，执行步骤4)。For the outputted fourth track data and the fifth track data, step 3) is performed to output the spatiotemporal similarity value; for the outputted spatiotemporal similarity value, step 4) is performed.
若判断路侧毫米波雷达的参数小于等于时空相似度阈值，则本次路侧毫米波雷达的标定参数满足要求。If it is judged that the parameters of the roadside millimeter-wave radar are less than or equal to the space-time similarity threshold, the calibration parameters of the roadside millimeter-wave radar this time meet the requirements.
若所计算获得的时空相似度数值大于时空相似度阈值，则可按照以下三个备选方案进行处理：If the calculated spatiotemporal similarity value is greater than the spatiotemporal similarity threshold, it can be processed according to the following three alternatives:
①不对毫米波雷达的标定参数进行校准，即按照首次获取的雷达标定参数暂时作为雷达的标定参数；① Do not calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the radar calibration parameters obtained for the first time as the calibration parameters of the radar;
②对毫米波雷达的标定参数进行校准，即按照更新后的雷达标定参数暂时作为雷达的标定参数；② Calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the updated radar calibration parameters as the radar calibration parameters;
③重新执行步骤1)至步骤4)，若步骤4)所得的时空相似度数值小于时空相似度阈值，则校准结束；若步骤4)所得的时空相似度数值大于等于时空相似度阈值，则将该雷达标定为故障雷达上报处理单元。3. Re-execute step 1) to step 4), if the time-space similarity value obtained in step 4) is less than the space-time similarity threshold, the calibration ends; if the space-time similarity value obtained in step 4) is greater than or equal to the space-time similarity threshold, then The radar is calibrated as the fault radar reporting processing unit.
本发明专利上述步骤中的具体技术方案如下所述：The specific technical scheme in the above steps of the patent of the present invention is as follows:
(1)所述路侧毫米波雷达的安装角度根据实际场景的不同，在路侧的安装位置如图2所示。所述外带定位设备的校准车辆，其带有的车载定位装置一般为高精度RTK差分定位装置。安装在校准车辆的内部或车顶。(1) The installation angle of the roadside millimeter-wave radar is different according to the actual scene, and the installation position on the roadside is shown in Figure 2. The on-board positioning device provided in the calibration vehicle with the external positioning device is generally a high-precision RTK differential positioning device. Mounted on the interior or roof of a calibration vehicle.
所述带有车载定位装置的校准车辆的采样频率设置，至少不低于路侧毫米波雷达的采样频率。当保持较高采样频率时，校准路侧毫米波雷达的准确率较高。当车载定位装置的采样频率与路侧毫米波雷达的采样频率一致且采样时间相同时，无需进行重采样处理。当车载定位装置的采样频率与路侧毫米波雷达的采样频率不一致或采样时间点不同时，至少有一条轨迹需要进行重采样处理。重采样处理后，两条轨迹可实现采样点数量相同且采样时间对齐。The sampling frequency of the calibration vehicle with the on-board positioning device is set at least not lower than the sampling frequency of the roadside millimeter-wave radar. When the sampling frequency is kept high, the accuracy of calibrating the roadside millimeter-wave radar is higher. When the sampling frequency of the vehicle positioning device is consistent with the sampling frequency of the roadside millimeter-wave radar and the sampling time is the same, no resampling process is required. When the sampling frequency of the vehicle positioning device is inconsistent with the sampling frequency of the roadside millimeter-wave radar or the sampling time point is different, at least one trajectory needs to be resampled. After resampling processing, the two trajectories can achieve the same number of sampling points and aligned sampling times.
处理单元调整带有车载定位装置的校准车辆与路侧毫米波雷达时间时钟，使之与处理单元的时钟时间严格同步，实现时钟同步。The processing unit adjusts the time clock of the calibrated vehicle with the on-board positioning device and the roadside millimeter-wave radar so that it is strictly synchronized with the clock time of the processing unit to achieve clock synchronization.
所述带有车载定位装置的校准车辆所采集的第一轨迹数据D 1的数据点可以使用如下向量表示： The data points of the first trajectory data D 1 collected by the calibration vehicle with the on-board positioning device can be represented by the following vectors:
R v＝[x v,y v,z v] R v =[x v ,y v ,z v ]
其中：in:
x v表示车载定位装置在世界坐标系中的X坐标； x v represents the X coordinate of the vehicle positioning device in the world coordinate system;
y v表示车载定位装置在世界坐标系中的Y坐标； y v represents the Y coordinate of the vehicle positioning device in the world coordinate system;
z v表示车载定位装置在世界坐标系中的Z坐标。 z v represents the Z coordinate of the vehicle positioning device in the world coordinate system.
所述路侧毫米波雷达所采集的第二轨迹数据D 2的数据点可以使用如下向量表示： The data points of the second trajectory data D2 collected by the roadside millimeter-wave radar can be represented by the following vectors:
R r＝[x r,y r,z r] R r =[x r ,y r ,z r ]
其中：in:
x r表示路侧毫米波雷达在雷达坐标系中的X坐标； x r represents the X coordinate of the roadside millimeter-wave radar in the radar coordinate system;
y r表示路侧毫米波雷达在基准坐标系中的Y坐标； y r represents the Y coordinate of the roadside millimeter-wave radar in the reference coordinate system;
z r表示路侧毫米波雷达在基准坐标系中的Z坐标。 z r represents the Z coordinate of the roadside millimeter-wave radar in the reference coordinate system.
(2)在路侧毫米波雷达的检测覆盖范围内，带有车载定位装置的校准车辆按照预先设计的常规路线在路侧毫米波雷达的覆盖范围内进行行驶，产生第一轨迹数据D 1与第二轨迹数据D 2。具体地，带有车载定位装置的校准车辆按照预先设计的一种常规路线行驶，校准车辆的车载定位装置连续采集车辆自身定位数据，得到第一轨迹数据D 1；路侧毫米波雷达连续采集道路中校准车辆的回波数据，得到第二轨迹数据D 2。 (2) Within the detection coverage of the roadside millimeter-wave radar, the calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed conventional route, and generates the first trajectory data D1 and The second trajectory data D 2 . Specifically, the calibration vehicle with the vehicle-mounted positioning device travels according to a pre-designed conventional route, and the vehicle-mounted positioning device of the calibration vehicle continuously collects the vehicle's own positioning data to obtain the first trajectory data D 1 ; the roadside millimeter-wave radar continuously collects the road The echo data of the vehicle is calibrated in the middle to obtain the second trajectory data D 2 .
如图3所示，常规路线是指在道路中的对角直线、曲线、环形路线以及以上路线的组合线路，用于本发明中对路侧毫米波雷达进行常规校准。As shown in FIG. 3 , a conventional route refers to a diagonal straight line, a curved line, a circular route and a combination route of the above routes in the road, which are used for the conventional calibration of the roadside millimeter-wave radar in the present invention.
1)第一轨迹数据D 1，是所述带有车载定位装置的校准车辆，按照预先设计的常规路线(或复杂路线)在路侧毫米波雷达的覆盖范围内进行行驶，车载定位装置在不同时刻实时不间断采集校准车辆自身定位数据。数据可用如下集合表示： 1) The first trajectory data D 1 is the calibration vehicle with the on-board positioning device, which travels within the coverage area of the roadside millimeter-wave radar according to a pre-designed conventional route (or complex route), and the on-board positioning device operates in different Real-time and uninterrupted collection and calibration of the vehicle's own positioning data. Data can be represented by the following sets:
S v＝{tr 1,tr 2,…tr N} S v ={tr 1 ,tr 2 ,...tr N }
其中，tr n表示该车载定位装置在不同时刻采集到第n条轨迹集合。 Among them, tr n indicates that the vehicle positioning device collects the nth track set at different times.
Figure PCTCN2022084929-appb-000001
Figure PCTCN2022084929-appb-000001
其中：in:
Figure PCTCN2022084929-appb-000002
表示在车载定位装置采集到的第n个目标轨迹中，第j个轨迹点的采集时间；
Figure PCTCN2022084929-appb-000002
Indicates the acquisition time of the jth trajectory point in the nth target trajectory collected by the vehicle positioning device;
Figure PCTCN2022084929-appb-000003
表示在车载定位装置采集到的第n个目标轨迹中，第j个轨迹点的横坐标；
Figure PCTCN2022084929-appb-000003
Indicates the abscissa of the jth track point in the nth target trajectory collected by the vehicle positioning device;
Figure PCTCN2022084929-appb-000004
表示在车载定位装置采集到的第n个目标轨迹中，第j个轨迹点的纵坐标。
Figure PCTCN2022084929-appb-000004
Indicates the ordinate of the jth trajectory point in the nth target trajectory collected by the vehicle-mounted positioning device.
2)第二轨迹数据D 2，是所述路侧毫米波雷达不间断产生的校准车辆的轨迹回波数据。数据可用如下集合表示： 2) The second trajectory data D 2 is the trajectory echo data of the calibration vehicle continuously generated by the roadside millimeter-wave radar. Data can be represented by the following sets:
S r＝{tr 1,tr 2,…tr M} S r ={tr 1 ,tr 2 ,...tr M }
其中，tr m表示路侧毫米波雷达采集到第m个目标轨迹集合。其表达式为： Among them, tr m represents the m-th target trajectory set collected by the roadside millimeter-wave radar. Its expression is:
Figure PCTCN2022084929-appb-000005
Figure PCTCN2022084929-appb-000005
其中：in:
Figure PCTCN2022084929-appb-000006
表示路侧毫米波雷达采集到的第m个目标轨迹中，第i个轨迹点的采集时间；
Figure PCTCN2022084929-appb-000006
Indicates the acquisition time of the i-th trajectory point in the m-th target trajectory collected by the roadside millimeter-wave radar;
Figure PCTCN2022084929-appb-000007
表示路侧毫米波雷达采集到的第m个目标轨迹中，第i个轨迹点的横坐标；
Figure PCTCN2022084929-appb-000007
Indicates the abscissa of the i-th trajectory point in the m-th target trajectory collected by the roadside millimeter-wave radar;
Figure PCTCN2022084929-appb-000008
表示路侧毫米波雷达采集到的第m个目标轨迹中，第i个轨迹点的纵坐标。
Figure PCTCN2022084929-appb-000008
Indicates the ordinate of the i-th trajectory point in the m-th target trajectory collected by the roadside millimeter-wave radar.
(3)处理单元获取带有车载定位装置的校准车辆与路侧毫米波雷达所产生的D 1、D 2，并对D 2进行坐标转换得出第三轨迹数据D 3，对D 1与D 3进行计算分析，判断路侧毫米波雷达是否需要校准。具体流程如图4所示。 (3) The processing unit obtains D 1 and D 2 generated by the calibration vehicle with the vehicle-mounted positioning device and the roadside millimeter-wave radar, and performs coordinate transformation on D 2 to obtain the third trajectory data D 3 . 3. Perform calculation and analysis to determine whether the roadside millimeter-wave radar needs to be calibrated. The specific process is shown in Figure 4.
1)坐标转换。由于D 1处于世界坐标系，而D 2处于雷达坐标系，两者之间存在角度和位置的差异，无法对两个坐标系下不同尺度的数据进行直接处理，因此需要进行坐标的转换。坐标转换是通过选取两类轨迹点中对应特征点，形成4个及以上的点对集合，并根据坐标转换公式，计算雷达坐标系到世界坐标系的校准参数矩阵。最后通过校准参数矩阵，将D 2转换为D 3。具体的过程如下： 1) Coordinate transformation. Since D 1 is in the world coordinate system and D 2 is in the radar coordinate system, there is a difference in angle and position between the two, so it is impossible to directly process the data of different scales in the two coordinate systems, so it is necessary to convert the coordinates. The coordinate transformation is to form a set of 4 or more point pairs by selecting the corresponding feature points in the two types of trajectory points, and calculate the calibration parameter matrix from the radar coordinate system to the world coordinate system according to the coordinate transformation formula. Finally, D2 is converted to D3 by calibrating the parameter matrix. The specific process is as follows:
①选取D 1集合与D 2集合中对应的特征点，至少选取4对及以上的特征点对。特征选取的原则是，在两个轨迹数据中选择具有时间、空间对应关系的点。选取的特征点对如下所示： ①Select the corresponding feature points in the D 1 set and the D 2 set, and select at least four or more feature point pairs. The principle of feature selection is to select points with temporal and spatial correspondence in the two trajectory data. The selected feature point pairs are as follows:
Figure PCTCN2022084929-appb-000009
Figure PCTCN2022084929-appb-000009
该特征点对中，每一个点对的一个特征点
Figure PCTCN2022084929-appb-000010
来自于车载定位装置所产生的D 1数据，另一个特征点
Figure PCTCN2022084929-appb-000011
来自于路侧毫米波雷达所产生的D 2。 In the feature point pair, one feature point of each point pair
Figure PCTCN2022084929-appb-000010
From the D 1 data generated by the vehicle positioning device, another feature point
Figure PCTCN2022084929-appb-000011
D 2 from roadside millimeter-wave radar.
②根据所获取的特征点对，解算将D 1坐标转换至D 3所对应的校准参数矩阵。 ②According to the acquired feature point pairs, solve the calibration parameter matrix corresponding to the transformation of D1 coordinates to D3 .
本发明中，世界坐标系指在环境中选择一个参考坐标系来描述校准车辆的位置，该坐标系称为世界坐标系。本发明中，世界坐标由校准车辆上带有的车载定位装置获取。车载定位装置能够实时地提供测站点在指定坐标系中的三维定位结果。In the present invention, the world coordinate system refers to selecting a reference coordinate system in the environment to describe the position of the calibration vehicle, and the coordinate system is called the world coordinate system. In the present invention, the world coordinates are acquired by the on-board positioning device on the calibration vehicle. The vehicle-mounted positioning device can provide the three-dimensional positioning results of the station in the specified coordinate system in real time.
本发明中，路侧毫米波雷达获取的坐标处于雷达坐标系。本发明中的雷达校准参数矩阵是指将雷达坐标系转换为世界坐标系的矩阵。雷达校准参数矩阵H如下式所示：In the present invention, the coordinates obtained by the roadside millimeter-wave radar are in the radar coordinate system. The radar calibration parameter matrix in the present invention refers to a matrix that converts the radar coordinate system into the world coordinate system. The radar calibration parameter matrix H is as follows:
Figure PCTCN2022084929-appb-000012
Figure PCTCN2022084929-appb-000012
如下式所示，(x r,y r,z r)表示D 2中某点的三维坐标，(x v,y v,z v)表示D 1中某点的三维坐标。 As shown in the following formula, (x r , y r , z r ) represent the three-dimensional coordinates of a point in D 2 , and (x v , y v , z v ) represent the three-dimensional coordinates of a point in D 1 .
Figure PCTCN2022084929-appb-000013
Figure PCTCN2022084929-appb-000013
通过步骤1)中手动选取的对应特征点对进行线性方程求解，可以求解出雷达校准参数矩阵H的值。The value of the radar calibration parameter matrix H can be obtained by solving the linear equation for the corresponding feature points manually selected in step 1).
③根据所求解的雷达校准参数矩阵H，可以将雷达坐标系下的D 2变换到世界坐标系下的D 3。如下所示： ③ According to the solved radar calibration parameter matrix H, D 2 in the radar coordinate system can be transformed into D 3 in the world coordinate system. As follows:
Figure PCTCN2022084929-appb-000014
Figure PCTCN2022084929-appb-000014
Figure PCTCN2022084929-appb-000015
Figure PCTCN2022084929-appb-000015
Figure PCTCN2022084929-appb-000016
Figure PCTCN2022084929-appb-000016
其中，(x r,y r,z r)表示D 2中某点，(x′ r,y′ r,z′ r)表示转换到世界坐标系后路侧毫米波雷达新的三维坐标数据，即为第三轨迹数据D 3中的点。 Among them, (x r , y r , z r ) represents a point in D 2 , (x′ r , y′ r , z′ r ) represents the new three-dimensional coordinate data of the roadside millimeter-wave radar after conversion to the world coordinate system, That is, the point in the third trajectory data D3 .
2)对D 1与D 3进行计算分析。 2) Calculate and analyze D 1 and D 3 .
①重采样：在1)的基础上，将D 1与D 3进行重采样，使得D 1与D 3在空间上保持一致，两类数据的点迹一一对应。 ①Resampling: On the basis of 1), resample D 1 and D 3 , so that D 1 and D 3 are consistent in space, and the traces of the two types of data correspond one-to-one.
虽然校准车辆上的车载定位装置与路侧毫米波雷达的数据采集频率都较高，但是两者的采集不一定相同。在空间上，表现为两者的采样点分布时间间隔不一致。因此D 1与D 3在空间上不能一一对应，无法完成对点迹准确程度的判断。为此，需要通过重采样的方法，对数据进行重新采样，重新修正为采样点数量相同且采样时间对齐的两条轨迹。其中，当车载定位装置的采样频率与路侧毫米波雷达的采样频率一致且采样时间相同时，无需进行重采样处理。当车载定位装置的采样频率与路侧毫米波雷达的采样频率不一致或采样时间点不同时，至少有一条轨 迹需要进行重采样处理。具体的步骤为： Although the data collection frequency of the on-board positioning device on the calibration vehicle and the roadside millimeter-wave radar is relatively high, the collection of the two is not necessarily the same. Spatially, the time interval between the two sampling points distribution is inconsistent. Therefore, D 1 and D 3 cannot correspond one-to-one in space, and the judgment of the accuracy of the dot trace cannot be completed. To this end, it is necessary to resample the data by means of resampling, and re-correct it into two trajectories with the same number of sampling points and aligned sampling times. Wherein, when the sampling frequency of the vehicle-mounted positioning device is consistent with the sampling frequency of the roadside millimeter-wave radar and the sampling time is the same, no resampling process is required. When the sampling frequency of the vehicle positioning device is inconsistent with the sampling frequency of the roadside millimeter-wave radar or the sampling time point is different, at least one trajectory needs to be resampled. The specific steps are:
首先求解D 1中时间点集合T 1与D 3中时间点集合T 3的并集T all。具体关系式如下所示： First, solve the union T all of the time point set T 1 in D 1 and the time point set T 3 in D 3 . The specific relationship is as follows:
T 1∪T 3＝T all T 1 ∪T 3 =T all
接着，依次遍历D 1与D 3的数据点，每隔四个点作为一组，拟合一个三次函数。带入轨迹数据中相邻四个点迹的横坐标即可求解该三次函数G x的系数a,b,c,d。带入轨迹数据中相邻三个点迹的纵坐标即可求解该三次函数函数G y的系数e,f,g,h。三次函数的表达式如下式所示： Next, traverse the data points of D 1 and D 3 in turn, and fit every four points as a group to fit a cubic function. The coefficients a, b, c, d of the cubic function G x can be solved by bringing in the abscissas of the adjacent four point traces in the trajectory data. The coefficients e, f, g, h of the cubic function G y can be solved by bringing in the ordinates of the three adjacent point traces in the trajectory data. The expression of the cubic function is as follows:
G x＝ax 3+bx 2+cx+d G x =ax 3 +bx 2 +cx+d
G y＝ex 3+fx 2+gx+h G y =ex 3 +fx 2 +gx+h
最后，以区间内的T all时刻点作为数据重采样的横坐标，求取坐标点的三次函数y g中，最终获取以T all为时刻点的、重采样后的路侧毫米波雷达新数据D 1′与重采样后的车载定位装置轨迹数据D 3′，两者在时间维度上保持一致的采样频率，如图7所示。 Finally, take the time point of T all in the interval as the abscissa of data resampling, and obtain the cubic function y g of the coordinate point, and finally obtain the new data of roadside millimeter-wave radar after re-sampling with T all as the time point D 1 ′ and the resampled vehicle-mounted positioning device trajectory data D 3 ′ maintain the same sampling frequency in the time dimension, as shown in FIG. 7 .
②时空相似度计算：重采样后的D 1′与D 3′在各个时刻点一一对应。为了对雷达校准参数矩阵的准确性进行判断，需要将D 1′与D 3′进行时空相似度计算，在时间空间双重维度上进行准确性评价。评价的流程如下所示： ②Calculation of spatio-temporal similarity: The resampling D 1 ′ and D 3 ′ correspond one-to-one at each time point. In order to judge the accuracy of the radar calibration parameter matrix, it is necessary to calculate the space-time similarity between D 1 ′ and D 3 ′, and to evaluate the accuracy in the dual dimensions of time and space. The evaluation process is as follows:
首先，为D 1′与D 3′建立匹配点对的集合C 2。表达式如下所示： First, a set C2 of matching point pairs is established for D1 ' and D3 '. The expression looks like this:
Figure PCTCN2022084929-appb-000017
Figure PCTCN2022084929-appb-000017
其中，
Figure PCTCN2022084929-appb-000018
表示D 3′数据集合中第u个路侧毫米波雷达轨迹数据点，数据点包含横坐标
Figure PCTCN2022084929-appb-000019
纵坐标
Figure PCTCN2022084929-appb-000020
以及时间戳
Figure PCTCN2022084929-appb-000021
表示D 1′数据集合中第u个带有车载定位装置的校准车辆所行驶的轨迹数据，数据点包含横坐标
Figure PCTCN2022084929-appb-000022
纵坐标
Figure PCTCN2022084929-appb-000023
以及时间戳
Figure PCTCN2022084929-appb-000024
且该轨迹点在时间上与D 3′点
Figure PCTCN2022084929-appb-000025
相匹配。 in,
Figure PCTCN2022084929-appb-000018
Represents the u-th roadside millimeter-wave radar trajectory data point in the D 3 ′ data set, and the data point contains the abscissa
Figure PCTCN2022084929-appb-000019
Y-axis
Figure PCTCN2022084929-appb-000020
and timestamp
Figure PCTCN2022084929-appb-000021
Represents the trajectory data of the u-th calibration vehicle with the on-board positioning device in the D 1 ' data set, and the data point contains the abscissa
Figure PCTCN2022084929-appb-000022
Y-axis
Figure PCTCN2022084929-appb-000023
and timestamp
Figure PCTCN2022084929-appb-000024
And the trajectory point is in time with the point D 3 '
Figure PCTCN2022084929-appb-000025
match.
接着，基于所述匹配点对集合C 2，对点对的相似度从空间、时间双重维度进行评价。评价相似度的表达式如下所示： Next, based on the matching point pair set C 2 , the similarity of the point pairs is evaluated from the dual dimensions of space and time. The expression for evaluating similarity is as follows:
Figure PCTCN2022084929-appb-000026
Figure PCTCN2022084929-appb-000026
其中，式中sim(p,q)表示点p与点q的相似度，
Figure PCTCN2022084929-appb-000027
表示集合C 2中U个点对的匹配相似度的和，用该指标衡量D 1′与D 3′的相似程度。相似度的值越大，表示雷达校准参数矩阵越准确。 where sim(p,q) represents the similarity between point p and point q,
Figure PCTCN2022084929-appb-000027
It represents the sum of matching similarity of U point pairs in set C 2 , and this index is used to measure the similarity between D 1 ′ and D 3 ′. The larger the similarity value, the more accurate the radar calibration parameter matrix.
f S表示空间相似度，衡量所述匹配点对集合C 2中点对之间时间差值的大小。计算公式如下所示： f S represents the spatial similarity, which measures the size of the time difference between the point pairs in the matching point pair set C 2 . The calculation formula is as follows:
Figure PCTCN2022084929-appb-000028
Figure PCTCN2022084929-appb-000028
f T表示时间相似度，衡量所述匹配点对集合C 2中点对之间时间差值的大小。计算公式如下所示： f T represents the temporal similarity, which measures the size of the time difference between the point pairs in the matched point pair set C 2 . The calculation formula is as follows:
Figure PCTCN2022084929-appb-000029
Figure PCTCN2022084929-appb-000029
此外，式中
Figure PCTCN2022084929-appb-000030
表示空间相似度的权重，
Figure PCTCN2022084929-appb-000031
表示时间相似度的权重。f S表示空间相似度，衡量所述匹配点对集合C 2中点对之间横纵坐标的距离大小。从空间维度对点对的相似度进行度量，直接反映出所计算出的雷达校准参数矩阵的准确程度；从时间维度对点对的相似度进行度量，直接反映出匹配点对在时间上的接近程度，间接反映出所计算出的雷达校准参数矩阵的准确程度。采用权重
Figure PCTCN2022084929-appb-000032
自由调节空间相似度与时间相似度所占比重，能够实现在存在时钟同步问题时，同样实现准确的评价。 In addition, in the formula
Figure PCTCN2022084929-appb-000030
is the weight representing the spatial similarity,
Figure PCTCN2022084929-appb-000031
A weight representing the temporal similarity. f S represents the spatial similarity, which measures the distance between the abscissa and ordinate between the point pairs in the matching point pair set C 2 . Measuring the similarity of point pairs from the spatial dimension directly reflects the accuracy of the calculated radar calibration parameter matrix; measuring the similarity of point pairs from the time dimension directly reflects the temporal proximity of matching point pairs , which indirectly reflects the accuracy of the calculated radar calibration parameter matrix. weights
Figure PCTCN2022084929-appb-000032
The proportion of spatial similarity and temporal similarity can be adjusted freely, which can also achieve accurate evaluation when there is a clock synchronization problem.
此外，式中β表示非重采样点相似度的权重，(1-β)表示重采样点所占权重。
Figure PCTCN2022084929-appb-000033
表示非重采样点之间的相似度，
Figure PCTCN2022084929-appb-000034
表示重采样点之间的相似度。对非重采样点对的相似度进行度量，可信度高，直接反映出D 1′与D 3′之间的相似程度；对重采样点对的相似度进行度量，可信度低，间接反映出D 1′与D 3′之间的相似程度；采用权重β自由调节非重采样点对与重采样点对所占比重，能够在进行重采样后可信度降低的情况下，实现较为准确的评价。 In addition, in the formula, β represents the weight of the similarity of the non-resampled points, and (1-β) represents the weight of the resampled points.
Figure PCTCN2022084929-appb-000033
represents the similarity between non-resampled points,
Figure PCTCN2022084929-appb-000034
Represents the similarity between resampling points. Measure the similarity of non-resampling point pairs, the reliability is high, which directly reflects the degree of similarity between D 1 ' and D 3 '; the similarity of resampling point pairs is measured, the reliability is low, indirect It reflects the degree of similarity between D 1 ′ and D 3 ′; the weight β is used to freely adjust the proportion of non-resampled point pairs and resampled point pairs, which can achieve relatively low reliability after resampling. accurate evaluation.
(4)判断参数是否满足时空相似度阈值：在经处理单元采集数据与分析数据基础上，判断所求解的D 1′与D 3′的时空相似度是否小于等于时空相似度阈值。若满足阈值要求，则路侧毫米波雷达无需进行重新校准；若不满足阈值要求，则认为路侧毫米波雷达需要进行重新校准。具体步骤为： (4) Judging whether the parameters meet the spatiotemporal similarity threshold: On the basis of the data collected and analyzed by the processing unit, determine whether the solved spatiotemporal similarity between D 1 ′ and D 3 ′ is less than or equal to the spatiotemporal similarity threshold. If the threshold requirements are met, the roadside millimeter-wave radar does not need to be recalibrated; if the threshold requirements are not met, it is considered that the roadside millimeter-wave radar needs to be recalibrated. The specific steps are:
通过步骤(3)计算出D 1′与D 3′的相似度
Figure PCTCN2022084929-appb-000035
若相似度
Figure PCTCN2022084929-appb-000036
大于等于时空相似度阈值δ，则认为雷达校准参数矩阵H满足阈值要求，无需进行重新校准；若相似
Figure PCTCN2022084929-appb-000037
小于相似度阈值δ，则认为雷达校准参数矩阵H不满足阈值要求，路侧毫米波雷达需要按照步骤(5)进行校准。 Calculate the similarity between D 1 ' and D 3 ' through step (3)
Figure PCTCN2022084929-appb-000035
If the similarity
Figure PCTCN2022084929-appb-000036
greater than or equal to the spatiotemporal similarity threshold δ, it is considered that the radar calibration parameter matrix H meets the threshold requirements, and no recalibration is required;
Figure PCTCN2022084929-appb-000037
If it is less than the similarity threshold δ, it is considered that the radar calibration parameter matrix H does not meet the threshold requirements, and the roadside millimeter-wave radar needs to be calibrated according to step (5).
(5)校准与验证：当经步骤(4)判断雷达校准参数矩阵H不满足阈值要求，则需对路侧毫米波雷达的雷达校准参数矩阵重新计算，进行校准。校准完毕后，为了确保结果的准确性，应当对校准结果进行进一步验证。具体流程如图10所示。(5) Calibration and verification: When it is judged in step (4) that the radar calibration parameter matrix H does not meet the threshold requirements, the radar calibration parameter matrix of the roadside millimeter-wave radar needs to be recalculated and calibrated. After calibration, in order to ensure the accuracy of the results, the calibration results should be further verified. The specific process is shown in Figure 10.
①重新校准。重复步骤(3)中的第①步进行坐标转换，选取新的特征匹配点对，对D 1与D 2重新计算雷达校准参数矩阵H′，更新雷达校准参数矩阵值。雷达校准参数矩阵H′如下式所示： ①Recalibrate. Repeat step ① in step (3) to perform coordinate transformation, select a new feature matching point pair, recalculate the radar calibration parameter matrix H' for D 1 and D 2 , and update the radar calibration parameter matrix value. The radar calibration parameter matrix H' is as follows:
Figure PCTCN2022084929-appb-000038
Figure PCTCN2022084929-appb-000038
②结果验证。在路侧毫米波雷达的检测覆盖范围内，带有车载定位装置的校准车辆按照预先设计的一种复杂路线或另一种常规路线，在路侧毫米波雷达的覆盖范围内进行行驶，将带有车载定位装置的校准车辆产生的数据记为第五轨迹数据D 5，由路侧毫米波雷达进行目标检测到的数据记为第四轨迹数据D 4。 ②Result verification. Within the detection coverage of the roadside millimeter-wave radar, the calibrated vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed complex route or another conventional route, and will bring the The data generated by the calibration vehicle with the on-board positioning device is recorded as fifth trajectory data D 5 , and the data detected by the roadside millimeter-wave radar for target detection is recorded as fourth trajectory data D 4 .
所述结果验证的步骤中，校准车辆可选择预先设计的一种复杂路线，如图4所示，复杂路线是指在道路中央或边缘的直线、曲线、环形路线以及以上路线的组合线路；校准车辆也可选择预先设计的另一种常规路线，如图3所示，常规路线是指在道路中的对角直线、曲线、环形路线以及以上路线的组合线路。在路线的选择方面，可充分依据首次选择出常规路线的特点，针对性的选择与所述常规路线特点有所差异的复杂路线、或另一种常规路线进行行驶与验证。在路线的选择方面，还可充分依据处理单元所计算得出的时空相似度数值与时空相似度阈值的差异大小，当差异较大时，针对性的选择与首次行驶的一种常规路线线性较为相似的另一种常规路线或一种复杂路线，对结果进行验证；或当差异较小时，针对性的选择与首次行驶的一种常规路线线性较为不同的另一种常规路线或一种复杂路线，对结果进行验证。In the step of result verification, the calibration vehicle can select a pre-designed complex route, as shown in Figure 4, the complex route refers to the straight line, curve, circular route and the combination route of the above routes in the center or edge of the road; calibration; The vehicle can also choose another pre-designed conventional route, as shown in FIG. 3 , the conventional route refers to the diagonal straight line, the curve, the circular route and the combination route of the above routes in the road. In terms of route selection, a complex route with different characteristics from the conventional route or another conventional route can be selected for driving and verification based on the characteristics of the conventional route selected for the first time. In terms of route selection, it can also be fully based on the difference between the spatiotemporal similarity value calculated by the processing unit and the spatiotemporal similarity threshold value. When the difference is large, the targeted selection is more linear than a conventional route for first driving Another conventional route or a complex route that is similar to the result is verified; or when the difference is small, another conventional route or a complex route that is linearly different from the first conventional route is selected. , to verify the results.
首先，由所述更新后的雷达校准参数矩阵H′可以将雷达坐标系下的第四轨迹数据D 4变换到世界坐标系下的第六轨迹数据D 6。如下所示： First, the fourth trajectory data D 4 in the radar coordinate system can be transformed into the sixth trajectory data D 6 in the world coordinate system by the updated radar calibration parameter matrix H′. As follows:
Figure PCTCN2022084929-appb-000039
Figure PCTCN2022084929-appb-000039
Figure PCTCN2022084929-appb-000040
Figure PCTCN2022084929-appb-000040
Figure PCTCN2022084929-appb-000041
Figure PCTCN2022084929-appb-000041
其中，(x r,y r,z r)表示第四轨迹数据点，(x′ r,y′ r,z′ r)表示按照所求解的雷达校准参数矩阵H′ 转换到世界坐标系后路侧毫米波雷达新的三维坐标数据，即为第六轨迹数据点。 Among them, (x r , y r , z r ) represents the fourth trajectory data point, (x' r , y' r , z' r ) represents the way after the transformation to the world coordinate system according to the solved radar calibration parameter matrix H' The new three-dimensional coordinate data of the side millimeter wave radar is the sixth trajectory data point.
接着，将D 5与D 6上传至处理单元进行数据收集与数据分析，即按照步骤(3)的第2)步进行重采样计算，得到重采样后的轨迹数据D 5′与D 6′。 Next, upload D5 and D6 to the processing unit for data collection and data analysis, that is, perform resampling calculation according to step 2 ) of step ( 3 ) to obtain resampled trajectory data D5 ' and D6 '.
接着，对重采样后获得的轨迹数据D 5′与D 6′进行时空相似度数值计算，获得D 5′与D 6′的时空相似度数值。将所计算获得的时空相似度数值按照步骤(4)进行判断。若所计算获得的时空相似度数值小于等于时空相似度阈值，则路侧毫米波雷达的标定参数满足要求；若所计算获得的时空相似度数值大于时空相似度阈值，则可按照以下三个备选方案进行处理： Next, the space-time similarity value calculation is performed on the trajectory data D 5 ′ and D 6 ′ obtained after resampling, to obtain the space-time similarity value of D 5 ′ and D 6 ′. The calculated spatiotemporal similarity value is judged according to step (4). If the calculated spatiotemporal similarity value is less than or equal to the spatiotemporal similarity threshold, the calibration parameters of the roadside millimeter-wave radar meet the requirements; if the calculated spatiotemporal similarity value is greater than the spatiotemporal similarity threshold, the following three preparations can be used Options for processing:
1)不对毫米波雷达的标定参数进行校准，即按照首次获取的雷达标定参数H暂时作为雷达的标定参数；1) Do not calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the radar calibration parameter H obtained for the first time as the calibration parameter of the radar;
2)对毫米波雷达的标定参数进行校准，即按照更新后的雷达标定参数H′暂时作为雷达的标定参数；2) Calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the updated radar calibration parameter H' as the calibration parameter of the radar;
3)重新执行步骤(1)至步骤(4)，若步骤(4)所得的时空相似度数值小于时空相似度阈值，则校准结束；若步骤(4)所得的时空相似度数值大于等于时空相似度阈值，则将该雷达标定为故障雷达上报处理单元。3) Re-execute steps (1) to (4), if the spatiotemporal similarity value obtained in step (4) is less than the spatiotemporal similarity threshold, the calibration is over; if the spatiotemporal similarity value obtained in step (4) is greater than or equal to the spatiotemporal similarity If the degree threshold is exceeded, the radar is calibrated as the fault radar reporting processing unit.
本发明具有技术关键点和优势包括：The present invention has technical key points and advantages including:
使用校准车辆上带有的车载定位装置所采集的数据作为路侧毫米波雷达校准的真值数据，提供一种自动化、高精度的数据源，有效提升对路侧毫米波雷达的校准精度。同时，使用重采样作为不同采样频率数据匹配的预处理方法，解决了不同采样频率的多源轨迹数据难以进行比较的问题。此外，使用时空相似度的度量方法判断路侧毫米波雷达是否需要进行校准，判断方法简便可行，无需采用人工进行校准。最后，为校准车辆设计采用常规路线与复杂路线行驶方案，对路侧毫米波雷达的校准结果进行多角度的结果验证。The data collected by the on-board positioning device on the calibration vehicle is used as the true value data for the roadside millimeter-wave radar calibration, providing an automated and high-precision data source, which effectively improves the calibration accuracy of the roadside millimeter-wave radar. At the same time, using resampling as a preprocessing method for data matching with different sampling frequencies solves the problem that multi-source trajectory data with different sampling frequencies is difficult to compare. In addition, the measurement method of spatiotemporal similarity is used to judge whether the roadside millimeter-wave radar needs to be calibrated. The judgment method is simple and feasible, and no manual calibration is required. Finally, the conventional route and complex route driving scheme are used for the calibration vehicle design, and the multi-angle result verification is carried out on the calibration results of the roadside millimeter-wave radar.
以上符号及其所表示含义归纳如下表：The above symbols and their meanings are summarized in the following table:
Figure PCTCN2022084929-appb-000042
Figure PCTCN2022084929-appb-000042
Figure PCTCN2022084929-appb-000043
Figure PCTCN2022084929-appb-000043
附图说明Description of drawings
图1为本发明的流程示意图；Fig. 1 is the schematic flow chart of the present invention;
图2为本发明的路侧毫米雷达布设与车载定位装置相对位置的示意图；2 is a schematic diagram of the relative position of the roadside millimeter radar layout and the vehicle-mounted positioning device of the present invention;
图3为数据采集的常规行驶路线示意图；3 is a schematic diagram of a conventional driving route for data collection;
图4为结果验证的复杂行驶路线示意图；Figure 4 is a schematic diagram of a complex driving route for result verification;
图5为处理单元采集数据与分析数据的流程示意图；Fig. 5 is the schematic flow chart of processing unit acquisition data and analysis data;
图6为轨迹特征点选取示意图；6 is a schematic diagram of trajectory feature point selection;
图7为轨迹空间匹配示意图；7 is a schematic diagram of trajectory space matching;
图8为路侧毫米雷达布设与车载定位装置不同采样频率的轨迹示意图；Figure 8 is a schematic diagram of the trajectory of the roadside millimeter radar layout and the vehicle positioning device with different sampling frequencies;
图9为重采样示意图；Fig. 9 is a resampling schematic diagram;
图10为重新校准与结果验证的流程示意图。Figure 10 is a schematic flowchart of recalibration and result verification.
【具体实施方式】【Detailed ways】
下面结合附图和具体实施方式对本发明作详细说明。The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
本发明涉及一种基于车载定位装置的路侧毫米波雷达校准方法。如图1所示，本发明可以分为五个主要步骤：The invention relates to a roadside millimeter wave radar calibration method based on a vehicle positioning device. As shown in Figure 1, the present invention can be divided into five main steps:
第一步，路侧毫米波雷达的布设以及时钟同步设置。The first step is the layout of the roadside millimeter-wave radar and the clock synchronization setting.
校准车辆带有的车载定位装置能够辅助自动驾驶车辆或普通车辆进行高精度定位，具有厘米级的定位精度，能够实时输出校准车辆的行驶轨迹点。本发明将其作为数据参照值，从而对路侧毫米波雷达进行标定参数的校准与更新。校准车辆带有的车载定位装置应当能够实现对校准车辆实时的位置数据进行输出，设置不小于路侧毫米波雷达采样频率的采样频率。The on-board positioning device carried by the calibration vehicle can assist automatic driving vehicles or ordinary vehicles to perform high-precision positioning, with centimeter-level positioning accuracy, and can output the driving track points of the calibration vehicle in real time. The invention uses it as a data reference value, so as to calibrate and update the calibration parameters of the roadside millimeter-wave radar. The on-board positioning device carried by the calibration vehicle should be able to output the real-time position data of the calibration vehicle, and the sampling frequency should be set not less than the sampling frequency of the roadside millimeter-wave radar.
路侧毫米波雷达安装于路侧，已布设于道路的不同位置，所布设位置覆盖道路路面完整范围，如图2所示，道路场地应当尽可能空旷开阔，无高楼或树木遮挡，单个检测范围300米左右，能够实现对检测目标的距离、速度的准确测量。按照设定的采样频率输出检测道路上移动目标的位置数据。The roadside millimeter-wave radar is installed on the roadside and has been deployed at different positions on the road. The deployed positions cover the complete range of the road surface. As shown in Figure 2, the road site should be as open and open as possible, without high buildings or trees blocking, a single detection range About 300 meters, it can accurately measure the distance and speed of the detection target. The position data of the moving object on the detected road is output according to the set sampling frequency.
处理单元位于虚拟云端或位于基站，或采用内嵌方式安装于路侧毫米波雷达或校准车辆内部。处理单元与路侧毫米波雷达与带有车载定位装置的校准车辆实时连接。对路侧毫米波雷达、处理单元与校准车辆上的车载定位装置，利用基站连接或网络授时实现时钟同步。The processing unit is located in the virtual cloud or in the base station, or is embedded in the roadside millimeter-wave radar or inside the calibration vehicle. The processing unit is connected in real time with the roadside millimeter-wave radar and the calibrated vehicle with the on-board positioning device. For the roadside millimeter-wave radar, processing unit and on-board positioning device on the calibration vehicle, the base station connection or network timing is used to achieve clock synchronization.
第二步，校准车辆按照预先设计的路线行驶，并将数据上传至处理单元。In the second step, the vehicle is calibrated to follow a pre-designed route and the data is uploaded to the processing unit.
首先，在路侧毫米波雷达实际监测的道路环境中，带有车载定位装置的校准车辆沿道路按照预先设计的常规路线或复杂路线行驶，常规路线见图3，复杂路线见图4。First, in the road environment actually monitored by the roadside millimeter-wave radar, the calibrated vehicle with the on-board positioning device drives along the road according to the pre-designed conventional route or complex route. The conventional route is shown in Figure 3, and the complex route is shown in Figure 4.
在行驶的过程中，带有车载定位装置的校准车辆实时产生带有时间戳的目标车辆轨迹数据，记为第一轨迹数据，数据位于世界坐标系下，并将第一轨迹数据上传至处理单元；路侧毫米波雷达同步获取带有时间戳的目标车辆轨迹检测数据，记为第二轨迹数据，数据位于雷达坐标系下，并将第二轨迹数据上传至处理单元。通过校准车辆的行驶实现数据采集。In the process of driving, the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with timestamp in real time, which is recorded as the first trajectory data. The data is located in the world coordinate system, and the first trajectory data is uploaded to the processing unit. ; The roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the second trajectory data, and the data is located in the radar coordinate system, and the second trajectory data is uploaded to the processing unit. Data collection is achieved by calibrating the driving of the vehicle.
第三步，处理单元进行数据处理与数据分析。In the third step, the processing unit performs data processing and data analysis.
处理单元接受到所上传的第一轨迹数据与第二轨迹数据，并对数据进行处理与分析。分别为坐标转换、重采样与时空相似度计算。如图5所示。The processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively. As shown in Figure 5.
首先，由于第一轨迹数据与第二轨迹数据未处于同一坐标系，对第二轨迹数据进行坐标转换。在第一轨迹数据与第二轨迹数据的轨迹点中，如图6所示，选取四组以上的特征点对，解算路侧毫米波雷达的雷达校准参数矩阵。按照所解算的雷达校准参数矩阵，将雷达坐标系下的第二轨迹数据点转换至世界坐标系，得到第三轨迹数据，实现坐标转换，此时两类轨迹处于同一坐标系。First, since the first trajectory data and the second trajectory data are not in the same coordinate system, coordinate transformation is performed on the second trajectory data. Among the trajectory points of the first trajectory data and the second trajectory data, as shown in FIG. 6 , more than four sets of feature point pairs are selected to solve the radar calibration parameter matrix of the roadside millimeter-wave radar. According to the calculated radar calibration parameter matrix, the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion. At this time, the two types of trajectories are in the same coordinate system.
接着，为了解决第一轨迹数据与第二轨迹数据采样频率不一致的问题，如图8所示，对第一轨迹数据与第三轨迹数据进行重采样。重采样求取第一轨迹数据与第三轨迹数据点集中采样时间点的并集。并利用所求取的采样时间点的并集，检查第一轨迹数和第三轨迹数据中分别缺失的时间点，对所检查出的缺失时间点基于三次插值算法补缺横纵坐标，实现重采样，此时两 类轨迹处于同一坐标系且采样时间点一致，第一轨迹数据与路侧毫米波雷达所对应的第三轨迹数据点实现一对一匹配。见图9。Next, in order to solve the problem that the sampling frequencies of the first track data and the second track data are inconsistent, as shown in FIG. 8 , the first track data and the third track data are resampled. Resampling obtains the union of the first trajectory data and the sampling time points in the third trajectory data point set. And use the union of the obtained sampling time points to check the missing time points in the first track number and the third track data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one. See Figure 9.
最后，为了定量判断路侧毫米波雷达的雷达校准参数是否准确，对经过坐标转换与重采样后的第一、第三轨迹数据进行时空相似度计算。依据时空相似度的表达式，分别代入相同采样时间点下的第一轨迹数据点与第三轨迹数据点对，计算出第一轨迹数据与第二轨迹数据点的时空相似度值。Finally, in order to quantitatively judge whether the radar calibration parameters of the roadside millimeter-wave radar are accurate, the spatiotemporal similarity of the first and third trajectory data after coordinate transformation and resampling is calculated. According to the expression of the spatiotemporal similarity, respectively substitute the first trajectory data point and the third trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the first trajectory data and the second trajectory data point.
第四步，判断校准参数的准确度。The fourth step is to judge the accuracy of the calibration parameters.
处理单元求解完第一轨迹数据与第三轨迹数据的时空相似度值后，将求解结果与预设的精度阈值进行比较。若所求解的时空相似度值小于等于预设的精度阈值，则路侧毫米波雷达无需进行重新校准，流程结束；若所求解的时空相似度值大于预设的精度阈值，则认为路侧毫米波雷达需要进行重新校准，执行第五步，并进行结果验证。After the processing unit solves the spatiotemporal similarity value between the first trajectory data and the third trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold, the roadside millimeter-wave radar does not need to be recalibrated, and the process ends; if the solved spatiotemporal similarity value is greater than the preset accuracy threshold, it is considered that the roadside millimeter wave radar The wave radar needs to be recalibrated, the fifth step is performed, and the results are verified.
第五步，重新校准与结果验证。The fifth step is to recalibrate and verify the results.
当确定路侧毫米波雷达在步骤(4)中确定需要进行重新校准后，执行当前步骤(5)进行重新校准与结果验证。具体流程示意如图10所示。When it is determined that the roadside millimeter-wave radar needs to be recalibrated in step (4), the current step (5) is performed to perform recalibration and result verification. A schematic diagram of the specific process is shown in Figure 10.
首先路侧毫米波雷达进行重新校准。对于初次校准中的第一轨迹数据与第二轨迹数据，重新计算达校准参数矩阵。在第一轨迹数据与第二轨迹数据的轨迹点中，如图6所示，重新选取不同于初次校准中的四组以上的特征点对，更新路侧毫米波雷达的雷达校准参数矩阵。First, the roadside millimeter-wave radar is recalibrated. For the first trajectory data and the second trajectory data in the initial calibration, recalculate the calibration parameter matrix. Among the trajectory points of the first trajectory data and the second trajectory data, as shown in Figure 6, more than four sets of feature point pairs different from those in the initial calibration are reselected, and the radar calibration parameter matrix of the roadside millimeter-wave radar is updated.
接着进行校准车辆的校准实验。在路侧毫米波雷达实际监测的道路环境中，带有车载定位装置的校准车辆重新沿道路按照预先设计的复杂路线行驶，复杂路线见图4。在行驶的过程中，带有车载定位装置的校准车辆实时产生带有时间戳的目标车辆轨迹数据，记为第五轨迹数据，数据位于世界坐标系下，并将第五轨迹数据上传至处理单元；路侧毫米波雷达同步获取带有时间戳的目标车辆轨迹检测数据，记为第四轨迹数据，数据位于雷达坐标系下，并将第四轨迹数据上传至处理单元。处理单元按照所更新的雷达校准参数矩阵，将雷达坐标系下的第四轨迹数据点转换至世界坐标系，得到第六轨迹数据，实现坐标转换，此时两类轨迹处于同一坐标系。Next, a calibration experiment for calibrating the vehicle is performed. In the road environment actually monitored by the roadside millimeter-wave radar, the calibrated vehicle with the on-board positioning device re-runs along the road according to the pre-designed complex route. The complex route is shown in Figure 4. In the process of driving, the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with time stamp in real time, which is recorded as the fifth trajectory data. The data is located in the world coordinate system, and the fifth trajectory data is uploaded to the processing unit. ; The roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the fourth trajectory data, and the data is located in the radar coordinate system, and the fourth trajectory data is uploaded to the processing unit. The processing unit converts the fourth trajectory data point in the radar coordinate system to the world coordinate system according to the updated radar calibration parameter matrix, and obtains the sixth trajectory data to realize coordinate conversion. At this time, the two types of trajectories are in the same coordinate system.
最后，处理单元对更新后的雷达校准参数矩阵进行结果验证。按照以下三步依次进行：Finally, the processing unit verifies the results of the updated radar calibration parameter matrix. Follow these three steps in order:
①为了解决第五轨迹数据与第六轨迹数据采样频率不一致的问题，对第五轨迹数据与第六轨迹数据进行重采样。重采样求取第五轨迹数据与第六轨迹数据点集中采样时间点的并集。并利用所求取的采样时间点的并集，检查第五轨迹数和第六轨迹数据中分别缺失的时间点，对所检查出的缺失时间点基于三次插值算法补缺横纵坐标，实现重采样，此时两类轨迹处于同一坐标系且采样时间点一致，第五轨迹数据与路侧毫米波雷达所对应的第六轨迹数据点实现一对一匹配。① In order to solve the problem that the sampling frequency of the fifth track data is inconsistent with that of the sixth track data, the fifth track data and the sixth track data are resampled. Re-sampling obtains the union of the fifth trajectory data and the sampling time points in the sixth trajectory data point set. And use the union of the obtained sampling time points to check the missing time points in the fifth track number and the sixth track data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the fifth trajectory data and the sixth trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
②为了定量判断路侧毫米波雷达的雷达校准参数是否准确，对经过坐标转换与重采样后的第五、第六轨迹数据进行时空相似度计算。依据时空相似度的表达式，分别代入相同采样时间点下的第五轨迹数据点与第六轨迹数据点对，计算出第五轨迹数据与第二轨迹数据点的时空相似度值。② In order to quantitatively judge whether the radar calibration parameters of the roadside millimeter-wave radar are accurate, the spatiotemporal similarity of the fifth and sixth trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the fifth trajectory data point and the sixth trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the fifth trajectory data and the second trajectory data point.
③处理单元求解完第五轨迹数据与第六轨迹数据的时空相似度值后，将求解结果与预设的精度阈值进行比较。若所求解的时空相似度值小于等于预设的精度阈值，则路侧毫米波雷达无需进行重新校准，流程结束；若所求解的时空相似度值大于预设的精度阈值，则认为路侧毫米波雷达需要进行重新校准，执行第五步，并进行结果验证。3. After the processing unit has solved the spatiotemporal similarity value between the fifth trajectory data and the sixth trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold, the roadside millimeter-wave radar does not need to be recalibrated, and the process ends; if the solved spatiotemporal similarity value is greater than the preset accuracy threshold, it is considered that the roadside millimeter wave radar The wave radar needs to be recalibrated, the fifth step is performed, and the results are verified.
本发明具有技术关键点和优势包括：The present invention has technical key points and advantages including:
使用校准车辆上带有的车载定位装置所采集的数据作为路侧毫米波雷达校准的真值数据， 提供一种自动化、高精度的数据源，有效提升对路侧毫米波雷达的校准精度。同时，使用重采样作为不同采样频率数据匹配的预处理方法，解决了不同采样频率的多源轨迹数据难以进行比较的问题。此外，使用时空相似度的度量方法判断路侧毫米波雷达是否需要进行校准，判断方法简便可行，无需采用人工进行校准。最后，为校准车辆设计采用常规路线与复杂路线行驶方案，对路侧毫米波雷达的校准结果进行多角度的结果验证。The data collected by the on-board positioning device on the calibration vehicle is used as the true value data for the roadside millimeter-wave radar calibration, providing an automated and high-precision data source, which effectively improves the calibration accuracy of the roadside millimeter-wave radar. At the same time, using resampling as a preprocessing method for data matching with different sampling frequencies solves the problem that multi-source trajectory data with different sampling frequencies is difficult to compare. In addition, the measurement method of spatiotemporal similarity is used to judge whether the roadside millimeter-wave radar needs to be calibrated. The judgment method is simple and feasible, and no manual calibration is required. Finally, the conventional route and complex route driving scheme are used for the calibration vehicle design, and the multi-angle result verification is carried out on the calibration results of the roadside millimeter-wave radar.
实施例一如下：Embodiment 1 is as follows:
(1)路侧毫米波雷达的布设以及时钟同步设置，云端服务器作为处理单元(1) The layout of the roadside millimeter-wave radar and the clock synchronization setting, the cloud server is used as the processing unit
在上海市嘉定区上汽创新港园区设置实验场景。在实验场景中，在路侧250米等间距设置路侧毫米波雷达，安装高度5.0米，俯角10°，路侧毫米波雷达覆盖道路全部区域，场地空旷且没有到达建筑物遮挡。在用于校准的普通车辆上安装高精度RTK定位设备，RTK安装在校准车辆的顶端，与车辆之间的角度关系保持稳定。接入云端服务器用作处理单元，同时采用网络对路侧毫米波雷达、校准车辆的RTK定位设备、处理单元进行统一授时，使得三者保持时钟同步。An experimental scene was set up in the SAIC Innovation Port Park, Jiading District, Shanghai. In the experimental scene, roadside millimeter-wave radars are set at equal intervals of 250 meters on the roadside, with an installation height of 5.0 meters and a depression angle of 10°. A high-precision RTK positioning device is installed on the normal vehicle used for calibration, and the RTK is installed on the top of the calibration vehicle, and the angular relationship with the vehicle is kept stable. The cloud server is connected to the processing unit, and the network is used to uniformly time the roadside millimeter-wave radar, the RTK positioning device for calibrating the vehicle, and the processing unit, so that the three can keep their clocks synchronized.
(2)校准车辆按照预先设计的常规路线(曲线)行驶，并将数据上传至云端处理单元。(2) The calibration vehicle drives according to the pre-designed conventional route (curve), and uploads the data to the cloud processing unit.
在园区的实验道路环境中，带有高精度RTK定位设备的校准车辆沿园区的道路，按照预先设计的常规路线中的曲线路线正常行驶。与此同时，校准车辆上的RTK高精度所采集的第一轨迹数据实时上传至云端处理单元；路侧毫米波雷达所采集的第二轨迹数据实时上传至云端处理单元。实现数据的采集与上传。In the experimental road environment of the park, a calibrated vehicle with a high-precision RTK positioning device travels normally along the road of the park and follows the curved route in the pre-designed conventional route. At the same time, the first trajectory data collected by the RTK high-precision calibration vehicle is uploaded to the cloud processing unit in real time; the second trajectory data collected by the roadside millimeter-wave radar is uploaded to the cloud processing unit in real time. Realize data collection and upload.
(3)云端处理单元进行数据处理与数据分析。(3) The cloud processing unit performs data processing and data analysis.
经过步骤(2)，采集到的第二轨迹数据的采样频率为10Hz；采集到的校准车辆的第一轨迹数据的采集频率为100Hz。云端处理单元接受到所上传的第一轨迹数据与第二轨迹数据，并对数据进行处理与分析。分别为坐标转换、重采样与时空相似度计算。After step (2), the sampling frequency of the collected second trajectory data is 10 Hz; the collection frequency of the collected first trajectory data of the calibration vehicle is 100 Hz. The cloud processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively.
①坐标转换：在云端处理单元实现数据采集后，由于第一轨迹数据与第二轨迹数据未处于同一坐标系，因此对第二轨迹数据进行坐标转换。在第一轨迹数据与第二轨迹数据的轨迹点中，选取四组的特征点对，解算路侧毫米波雷达的雷达校准参数矩阵H：①Coordinate transformation: After the cloud processing unit realizes data collection, since the first trajectory data and the second trajectory data are not in the same coordinate system, coordinate transformation is performed on the second trajectory data. In the trajectory points of the first trajectory data and the second trajectory data, four sets of feature point pairs are selected to solve the radar calibration parameter matrix H of the roadside millimeter-wave radar:
Figure PCTCN2022084929-appb-000044
Figure PCTCN2022084929-appb-000044
按照所解算的雷达校准参数矩阵，将雷达坐标系下的第二轨迹数据点转换至世界坐标系，得到第三轨迹数据，实现坐标转换，此时两类轨迹处于同一坐标系。According to the calculated radar calibration parameter matrix, the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion. At this time, the two types of trajectories are in the same coordinate system.
②重采样：为了解决第一轨迹数据与第二轨迹数据采样频率不一致的问题，对第一轨迹数据与第三轨迹数据进行重采样。重采样求取第一轨迹数据与第三轨迹数据点集中采样时间点的并集，并集为[1516783680.132207,…1516783954.932218]。并利用所求取的采样时间点的并集，检查第一轨迹数据和第三轨迹数据中分别缺失的时间点，对所检查出的缺失时间点基于三次插值算法补缺横纵坐标，实现重采样，此时两类轨迹处于同一坐标系且采样时间点一致，第一轨迹数据与路侧毫米波雷达所对应的第三轨迹数据点实现一对一匹配。②Resampling: In order to solve the problem that the sampling frequencies of the first trajectory data and the second trajectory data are inconsistent, the first trajectory data and the third trajectory data are resampled. Resampling obtains the union of the sampling time points in the first trajectory data and the third trajectory data point set, and the union is [1516783680.132207,...1516783954.932218]. And use the union of the obtained sampling time points to check the missing time points in the first trajectory data and the third trajectory data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
③时空相似度计算：为了定量判断路侧毫米波雷达的雷达校准参数是否准确，对经过坐标转换与重采样后的第一、第三轨迹数据进行时空相似度计算。依据时空相似度的表达式，分别代入相同采样时间点下的第一轨迹数据点与第三轨迹数据点对，计算出第一轨迹数据与第二轨迹数据点的时空相似度值③ Calculation of spatiotemporal similarity: In order to quantitatively judge whether the radar calibration parameters of the roadside millimeter-wave radar are accurate, the spatiotemporal similarity of the first and third trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the first trajectory data point and the third trajectory data point pair under the same sampling time point, and calculate the spatiotemporal similarity value of the first trajectory data and the second trajectory data point
具体地，取空间相似度的权重
Figure PCTCN2022084929-appb-000045
则时间的相似度的权重
Figure PCTCN2022084929-appb-000046
以不同权重计算所匹配点迹中经过重采样的点与未经过重采样的点迹。取非重采样点对的权重β＝0.6,则重采样点对的权重(1-β)＝0.4。计算sim(p,q)： Specifically, take the weight of the spatial similarity
Figure PCTCN2022084929-appb-000045
Then the weight of the similarity in time
Figure PCTCN2022084929-appb-000046
Calculate the resampled and non-resampled traces in the matched traces with different weights. Taking the weight of the non-resampling point pair β=0.6, the weight of the resampling point pair (1-β)=0.4. Calculate sim(p,q):
Figure PCTCN2022084929-appb-000047
Figure PCTCN2022084929-appb-000047
相似度函数f的计算方法均按照欧式距离的平均值计算。The calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
(4)判断校准参数的准确度。(4) Judge the accuracy of the calibration parameters.
在实现时空相似度指标的求解后，将求解结果与预设的阈值δ进行比较。发现求解结果小于预设的阈值，表明路侧毫米波雷达无需重新校准。After the solution of the spatiotemporal similarity index is realized, the solution result is compared with the preset threshold δ. It is found that the solution result is less than the preset threshold, indicating that the roadside millimeter-wave radar does not need to be recalibrated.
实施例二如下：The second embodiment is as follows:
(1)路侧毫米波雷达的布设以及时钟同步设置，路侧计算机作为处理单元(1) The layout of the roadside millimeter-wave radar and the clock synchronization setting, and the roadside computer is used as the processing unit
在上海市嘉定区上汽创新港园区设置实验场景。在实验场景中，在路侧250米等间距设置路侧毫米波雷达，安装高度5.0米，俯角10°，路侧毫米波雷达覆盖道路全部区域，场地空旷且没有到达建筑物遮挡。在用于校准的普通车辆上安装高精度RTK定位设备，RTK安装在校准车辆的顶端，与车辆之间的角度关系保持稳定。接入云端服务器用作处理单元，同时采用网络对路侧毫米波雷达、校准车辆的RTK定位设备、处理单元进行统一授时，使得三者保持时钟同步。An experimental scene was set up in the SAIC Innovation Port Park, Jiading District, Shanghai. In the experimental scene, roadside millimeter-wave radars are set at equal intervals of 250 meters on the roadside, with an installation height of 5.0 meters and a depression angle of 10°. A high-precision RTK positioning device is installed on the normal vehicle used for calibration. The RTK is installed on the top of the calibration vehicle, and the angular relationship with the vehicle is kept stable. The cloud server is connected to the processing unit, and the network is used to uniformly time the roadside millimeter-wave radar, the RTK positioning equipment for calibrating the vehicle, and the processing unit, so that the three keep the clock synchronization.
(2)校准车辆按照预先设计的常规路线(环形)行驶，并将数据上传至路侧处理单元。(2) The calibration vehicle travels along a pre-designed conventional route (circular), and uploads the data to the roadside processing unit.
在园区的实验道路环境中，带有高精度RTK定位设备的校准车辆沿园区的道路，按照预先设计的常规路线中的环形路线正常行驶。与此同时，校准车辆上的RTK高精度所采集的第一轨迹数据实时上传至云端处理单元；路侧毫米波雷达所采集的第二轨迹数据实时上传至云端处理单元。实现数据的采集与上传。In the experimental road environment of the park, a calibrated vehicle with a high-precision RTK positioning device travels normally along the road of the park, following a circular route in a pre-designed conventional route. At the same time, the first trajectory data collected by the RTK high-precision calibration vehicle is uploaded to the cloud processing unit in real time; the second trajectory data collected by the roadside millimeter-wave radar is uploaded to the cloud processing unit in real time. Realize data collection and upload.
(3)路侧处理单元进行数据处理与数据分析。(3) The roadside processing unit performs data processing and data analysis.
经过步骤(2)，采集到的第二轨迹数据的采样频率为10Hz；采集到的校准车辆的第一轨迹数据的采集频率为100Hz。云端处理单元接受到所上传的第一轨迹数据与第二轨迹数据，并对数据进行处理与分析。分别为坐标转换、重采样与时空相似度计算。After step (2), the sampling frequency of the collected second trajectory data is 10 Hz; the collection frequency of the collected first trajectory data of the calibration vehicle is 100 Hz. The cloud processing unit receives the uploaded first trajectory data and the second trajectory data, and processes and analyzes the data. They are coordinate transformation, resampling and spatiotemporal similarity calculation respectively.
①坐标转换：在云端处理单元实现数据采集后，由于第一轨迹数据与第二轨迹数据未处于同一坐标系，因此对第二轨迹数据进行坐标转换。在第一轨迹数据与第二轨迹数据的轨迹点中，选取四组的特征点对，解算路侧毫米波雷达的雷达校准参数矩阵H：①Coordinate transformation: After the cloud processing unit realizes data collection, since the first trajectory data and the second trajectory data are not in the same coordinate system, coordinate transformation is performed on the second trajectory data. In the trajectory points of the first trajectory data and the second trajectory data, four sets of feature point pairs are selected to solve the radar calibration parameter matrix H of the roadside millimeter-wave radar:
Figure PCTCN2022084929-appb-000048
Figure PCTCN2022084929-appb-000048
按照所解算的雷达校准参数矩阵，将雷达坐标系下的第二轨迹数据点转换至世界坐标系，得到第三轨迹数据，实现坐标转换，此时两类轨迹处于同一坐标系。According to the calculated radar calibration parameter matrix, the second trajectory data point in the radar coordinate system is converted to the world coordinate system, and the third trajectory data is obtained to realize the coordinate conversion. At this time, the two types of trajectories are in the same coordinate system.
②重采样：为了解决第一轨迹数据与第二轨迹数据采样频率不一致的问题，对第一轨迹数据与第三轨迹数据进行重采样。重采样求取第一轨迹数据与第三轨迹数据点集中采样时间点的并集，并集为[1516783680.132207,…1516783954.932218]。并利用所求取的采样时间点的并 集，检查第一轨迹数据和第三轨迹数据中分别缺失的时间点，对所检查出的缺失时间点基于三次插值算法补缺横纵坐标，实现重采样，此时两类轨迹处于同一坐标系且采样时间点一致，第一轨迹数据与路侧毫米波雷达所对应的第三轨迹数据点实现一对一匹配。②Resampling: In order to solve the problem that the sampling frequencies of the first trajectory data and the second trajectory data are inconsistent, the first trajectory data and the third trajectory data are resampled. Resampling obtains the union of the sampling time points in the first trajectory data and the third trajectory data point set, and the union is [1516783680.132207,...1516783954.932218]. And use the union of the obtained sampling time points to check the missing time points in the first trajectory data and the third trajectory data respectively, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data and the third trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
③时空相似度计算：为了定量判断路侧毫米波雷达的雷达校准参数是否准确，对经过坐标转换与重采样后的第一、第三轨迹数据进行时空相似度计算。依据时空相似度的表达式，分别代入相同采样时间点下的第一轨迹数据点与第三轨迹数据点对，计算出第一轨迹数据与第二轨迹数据点的时空相似度值③ Calculation of spatiotemporal similarity: In order to quantitatively judge whether the radar calibration parameters of the roadside millimeter-wave radar are accurate, the spatiotemporal similarity of the first and third trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the first trajectory data point and the third trajectory data point pair under the same sampling time point, and calculate the spatiotemporal similarity value of the first trajectory data and the second trajectory data point
具体地，取空间相似度的权重
Figure PCTCN2022084929-appb-000049
则时间的相似度的权重
Figure PCTCN2022084929-appb-000050
以不同权重计算所匹配点迹中经过重采样的点与未经过重采样的点迹。取非重采样点对的权重β＝0.5,则重采样点对的权重(1-β)＝0.5。计算sim(p,q)： Specifically, take the weight of the spatial similarity
Figure PCTCN2022084929-appb-000049
Then the weight of the similarity in time
Figure PCTCN2022084929-appb-000050
Calculate the resampled and non-resampled traces in the matched traces with different weights. Take the weight of the non-resampling point pair β=0.5, then the weight of the resampling point pair (1-β)=0.5. Calculate sim(p,q):
Figure PCTCN2022084929-appb-000051
Figure PCTCN2022084929-appb-000051
相似度函数f的计算方法均按照欧式距离的平均值计算。The calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
(4)判断校准参数的准确度。(4) Judge the accuracy of the calibration parameters.
在实现时空相似度指标的求解后，将求解结果与预设的阈值δ进行比较。发现求解结果大于预设的阈值，认为路侧毫米波雷达需要进行重新校准，执行第五步，并进行结果验证。After the solution of the spatiotemporal similarity index is realized, the solution result is compared with the preset threshold δ. It is found that the solution result is greater than the preset threshold, and it is considered that the roadside millimeter-wave radar needs to be recalibrated, and the fifth step is performed, and the result is verified.
(5)重新校准与结果验证。(5) Recalibration and result verification.
当确定路侧毫米波雷达在步骤(4)中确定需要进行重新校准后，执行当前步骤(5)进行重新校准与结果验证。具体流程示意如图10所示。When it is determined that the roadside millimeter-wave radar needs to be recalibrated in step (4), the current step (5) is performed to perform recalibration and result verification. A schematic diagram of the specific process is shown in Figure 10.
首先对路侧毫米波雷达进行重新校准。对于初次校准中的第一轨迹数据与第二轨迹数据，重新计算达校准参数矩阵。在第一轨迹数据与第二轨迹数据的轨迹点中，重新选取不同于初次校准中的四组以上的特征点对，更新路侧毫米波雷达的雷达校准参数矩阵H′。First, the roadside millimeter-wave radar is recalibrated. For the first trajectory data and the second trajectory data in the initial calibration, recalculate the calibration parameter matrix. In the trajectory points of the first trajectory data and the second trajectory data, more than four sets of feature point pairs different from those in the initial calibration are reselected, and the radar calibration parameter matrix H' of the roadside millimeter-wave radar is updated.
Figure PCTCN2022084929-appb-000052
Figure PCTCN2022084929-appb-000052
接着进行校准车辆的校准实验。在路侧毫米波雷达实际监测的道路环境中，带有车载定位装置的校准车辆重新沿道路按照预先设计的复杂路线(路线组合1)行驶，见图4。在行驶的过程中，带有车载定位装置的校准车辆实时产生带有时间戳的目标车辆轨迹数据，记为第五轨迹数据，数据位于世界坐标系下，并将第五轨迹数据上传至处理单元；路侧毫米波雷达同步获取带有时间戳的目标车辆轨迹检测数据，记为第四轨迹数据，数据位于雷达坐标系下，并将第四轨迹数据上传至路侧处理单元。处理单元按照所更新的雷达校准参数矩阵，将雷达坐标系下的第四轨迹数据点转换至世界坐标系，得到第六轨迹数据，实现坐标转换，此时两类轨迹处于同一坐标系。Next, a calibration experiment to calibrate the vehicle is performed. In the road environment actually monitored by the roadside millimeter-wave radar, the calibrated vehicle with the on-board positioning device re-runs along the road according to the pre-designed complex route (route combination 1), as shown in Figure 4. In the process of driving, the calibration vehicle with the on-board positioning device generates the target vehicle trajectory data with time stamp in real time, which is recorded as the fifth trajectory data. The data is located in the world coordinate system, and the fifth trajectory data is uploaded to the processing unit. ; The roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with time stamp, which is recorded as the fourth trajectory data, and the data is located in the radar coordinate system, and the fourth trajectory data is uploaded to the roadside processing unit. The processing unit converts the fourth trajectory data point in the radar coordinate system to the world coordinate system according to the updated radar calibration parameter matrix, and obtains the sixth trajectory data to realize coordinate conversion. At this time, the two types of trajectories are in the same coordinate system.
最后，处理单元对更新后的雷达校准参数矩阵进行结果验证。按照以下三步依次进行：Finally, the processing unit verifies the results of the updated radar calibration parameter matrix. Follow these three steps in order:
①为了解决第五轨迹数据与第六轨迹数据采样频率不一致的问题，对第五轨迹数据与第六轨迹数据进行重采样。重采样求取第五轨迹数据与第六轨迹数据点集中采样时间点的并集，，并集为[1516783680.132207,…1516784063.852348]。并利用所求取的采样时间点的并集，检查第五轨迹数和第六轨迹数据中分别缺失的时间点，对所检查出的缺失时间点基于三次插值算法 补缺横纵坐标，实现重采样，此时两类轨迹处于同一坐标系且采样时间点一致，第五轨迹数据与路侧毫米波雷达所对应的第六轨迹数据点实现一对一匹配。① In order to solve the problem that the sampling frequency of the fifth track data is inconsistent with that of the sixth track data, the fifth track data and the sixth track data are resampled. Re-sampling obtains the union of the sampling time points in the fifth trajectory data and the sixth trajectory data point set, and the union is [1516783680.132207,...1516784063.852348]. And use the union of the obtained sampling time points to check the missing time points in the fifth track number and the sixth track data, and fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm for the detected missing time points to realize resampling. , at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the fifth trajectory data and the sixth trajectory data point corresponding to the roadside millimeter-wave radar are matched one-to-one.
②为了定量判断路侧毫米波雷达的雷达校准参数是否准确，对经过坐标转换与重采样后的第五、第六轨迹数据进行时空相似度计算。依据时空相似度的表达式，分别代入相同采样时间点下的第五轨迹数据点与第六轨迹数据点对，计算出第五轨迹数据与第二轨迹数据点的时空相似度值。② In order to quantitatively judge whether the radar calibration parameters of the roadside millimeter-wave radar are accurate, the spatiotemporal similarity of the fifth and sixth trajectory data after coordinate transformation and resampling is calculated. According to the expression of spatiotemporal similarity, respectively substitute the fifth trajectory data point and the sixth trajectory data point pair at the same sampling time point, and calculate the spatiotemporal similarity value of the fifth trajectory data and the second trajectory data point.
具体地，取空间相似度的权重
Figure PCTCN2022084929-appb-000053
则时间的相似度的权重
Figure PCTCN2022084929-appb-000054
以不同权重计算所匹配点迹中经过重采样的点与未经过重采样的点迹。取非重采样点对的权重β＝0.5,则重采样点对的权重(1-β)＝0.5。计算sim(p,q)： Specifically, take the weight of the spatial similarity
Figure PCTCN2022084929-appb-000053
Then the weight of the similarity in time
Figure PCTCN2022084929-appb-000054
Calculate the resampled and non-resampled traces in the matched traces with different weights. Take the weight of the non-resampling point pair β=0.5, then the weight of the resampling point pair (1-β)=0.5. Calculate sim(p,q):
Figure PCTCN2022084929-appb-000055
Figure PCTCN2022084929-appb-000055
相似度函数f的计算方法均按照欧式距离的平均值计算。The calculation method of the similarity function f is calculated according to the average value of the Euclidean distance.
③处理单元求解完第五轨迹数据与第六轨迹数据的时空相似度值后，将求解结果与预设的精度阈值进行比较。所求解的时空相似度值小于等于预设的精度阈值δ，则路侧毫米波雷达此次重新校准较为准确，流程结束。3. After the processing unit has solved the spatiotemporal similarity value between the fifth trajectory data and the sixth trajectory data, it compares the solving result with a preset precision threshold. If the solved spatiotemporal similarity value is less than or equal to the preset accuracy threshold δ, the recalibration of the roadside millimeter-wave radar is more accurate this time, and the process ends.
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应该以权力要求的保护范围为准。The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (12)

  1. 一种基于车载定位装置的路侧毫米波雷达校准方法，所述方法涉及校准车辆、路侧毫米波雷达以及处理单元，包括以下步骤：A roadside millimeter-wave radar calibration method based on a vehicle positioning device, the method involves calibrating a vehicle, a roadside millimeter-wave radar and a processing unit, including the following steps:
    1)对校准车辆的车载定位装置设置不小于路侧毫米波雷达的采样频率，对带有车载定位装置的校准车辆、路侧毫米波雷达以及处理单元设置时钟同步；1) Set the sampling frequency of the on-board positioning device of the calibration vehicle not less than that of the roadside millimeter-wave radar, and set the clock synchronization for the calibration vehicle with the on-board positioning device, the roadside millimeter-wave radar and the processing unit;
    2)所述带有车载定位装置的校准车辆按照预先设计的一种常规路线在所述路侧毫米波雷达的覆盖范围内进行行驶，输出第一轨迹数据与第二轨迹数据；2) The calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed conventional route, and outputs the first trajectory data and the second trajectory data;
    3)所述处理单元收集带有所述车载定位装置的校准车辆与所述路侧毫米波雷达所产生的第一轨迹数据、第二轨迹数据，进行坐标转换后得出第三轨迹数据，对第一轨迹数据与第三轨迹数据进行数据处理与分析，输出第一轨迹数据与第三轨迹数据的时空相似度数值；3) The processing unit collects the first trajectory data and the second trajectory data generated by the calibration vehicle with the on-board positioning device and the roadside millimeter-wave radar, and obtains the third trajectory data after coordinate transformation, Perform data processing and analysis on the first trajectory data and the third trajectory data, and output a spatiotemporal similarity value between the first trajectory data and the third trajectory data;
    4)所述处理单元根据时空相似度数值与时空相似度阈值，若判断路侧毫米波雷达的参数无需校准，则本次校准结束；若处理单元判断路侧毫米波雷达的参数需要校准，则对路侧毫米波雷达的参数进行校准；4) According to the spatiotemporal similarity value and the spatiotemporal similarity threshold, the processing unit judges that the parameters of the roadside millimeter-wave radar do not need to be calibrated, then this calibration ends; if the processing unit judges that the parameters of the roadside millimeter-wave radar need to be calibrated, then Calibrate the parameters of the roadside millimeter-wave radar;
    5)处理单元在对路侧毫米波雷达的参数进行校准后，进行如下验证步骤：5) After calibrating the parameters of the roadside millimeter-wave radar, the processing unit performs the following verification steps:
    5.1)带有车载定位装置的校准车辆按照预先设计的一种复杂路线或另一种常规路线在路侧毫米波雷达的覆盖范围内进行行驶，输出第四轨迹数据与第五轨迹数据；5.1) The calibration vehicle with the on-board positioning device travels within the coverage of the roadside millimeter-wave radar according to a pre-designed complex route or another conventional route, and outputs the fourth trajectory data and the fifth trajectory data;
    5.2)对所输出的第四轨迹数据与第五轨迹数据，执行步骤3)输出时空相似度数值；对所输出的时空相似度数值，执行步骤4)；5.2) to the outputted fourth track data and the fifth track data, execute step 3) output spatiotemporal similarity value; to output spatiotemporal similarity value, execute step 4);
    若判断路侧毫米波雷达的参数小于等于时空相似度阈值，则本次路侧毫米波雷达的标定参数满足要求；If it is judged that the parameters of the roadside millimeter-wave radar are less than or equal to the space-time similarity threshold, the calibration parameters of the roadside millimeter-wave radar this time meet the requirements;
    若所计算获得的时空相似度数值大于时空相似度阈值，则可按照以下三个备选方案进行处理：If the calculated spatiotemporal similarity value is greater than the spatiotemporal similarity threshold, it can be processed according to the following three alternatives:
    5.2.1)不对毫米波雷达的标定参数进行校准，即按照首次获取的雷达标定参数暂时作为雷达的标定参数；5.2.1) Do not calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the radar calibration parameters obtained for the first time as the calibration parameters of the radar;
    5.2.2)对毫米波雷达的标定参数进行校准，即按照更新后的雷达标定参数暂时作为雷达的标定参数；5.2.2) Calibrate the calibration parameters of the millimeter-wave radar, that is, temporarily use the updated radar calibration parameters as the radar calibration parameters;
    5.2.3)重新执行步骤1)至步骤4)，若步骤4)所得的时空相似度数值小于时空相似度阈值，则校准结束；若步骤4)所得的时空相似度数值大于等于时空相似度阈值，则将该毫米波雷达标定为故障雷达上报处理单元。5.2.3) Re-execute steps 1) to 4), if the spatiotemporal similarity value obtained in step 4) is less than the spatiotemporal similarity threshold, the calibration is over; if the spatiotemporal similarity value obtained in step 4) is greater than or equal to the spatiotemporal similarity threshold , the millimeter-wave radar is calibrated as the fault radar reporting processing unit.
  2. 如权利要求1所述的方法，其特征在于，所述路侧毫米波雷达的安装位置分为三类，即安装于道路左侧、安装于道路右侧以及安装于道路的中央；所述毫米波雷达具有识别移动车辆目标的能力，能够输出检测目标的定位数据。The method according to claim 1, wherein the installation positions of the roadside millimeter-wave radar are divided into three categories, namely, the installation positions on the left side of the road, the installation on the right side of the road, and the installation in the center of the road; Wave radar has the ability to identify moving vehicle targets and can output positioning data of detected targets.
  3. 如权利要求1所述的方法，其特征在于，所述带有车载定位装置的校准车辆按照车辆类型不同可划分为两类，一类指内嵌安装有高精度定位装置的自动驾驶车辆，自动驾驶车辆能够对车辆位置进行精准定位，输出实时的车辆定位数据；另一类指外带定位设备的校准车辆，校准车辆搭载高精度定位装置，能够对车辆位置进行精准定位，并通过处理单元输出实时的车辆定位数据；校准车辆带有的车载定位装置的采样频率不小于路侧毫米波雷达。The method according to claim 1, wherein the calibration vehicles with on-board positioning devices can be divided into two categories according to different types of vehicles. The driving vehicle can accurately locate the vehicle position and output real-time vehicle positioning data; the other type refers to the calibration vehicle with external positioning equipment. The calibration vehicle is equipped with a high-precision positioning device, which can accurately locate the vehicle position and output it through the processing unit. Real-time vehicle positioning data; the sampling frequency of the vehicle-mounted positioning device on the calibration vehicle is not less than that of the roadside millimeter-wave radar.
  4. 如权利要求1所述的方法，其特征在于，所述路侧毫米波雷达参数的校准，是指对路侧毫米波雷达校准参数矩阵的校准，即对毫米波雷达坐标系转换至世界坐标系坐标的转换参数的修正与更新。The method according to claim 1, wherein the calibration of the roadside millimeter-wave radar parameters refers to the calibration of the roadside millimeter-wave radar calibration parameter matrix, that is, converting the millimeter-wave radar coordinate system to the world coordinate system Correction and update of coordinate transformation parameters.
  5. 如权利要求1所的述方法，其特征在于，所述处理单元具有对数据进行收集、处理分析与上传的功能；同时所述处理单元调整带有车载定位装置的校准车辆与路侧毫米波雷达时间时钟，使之与处理单元的时钟时间严格同步，实现时钟同步。The method according to claim 1, wherein the processing unit has the functions of collecting, processing, analyzing and uploading data; at the same time, the processing unit adjusts the calibration vehicle with the vehicle-mounted positioning device and the roadside millimeter-wave radar The time clock is strictly synchronized with the clock time of the processing unit to achieve clock synchronization.
  6. 如权利要求5所的述方法，其特征在于，所述处理单元采用如下三种方式之一：The method of claim 5, wherein the processing unit adopts one of the following three methods:
    6.1)云端处理单元，与路侧毫米波雷达与带有车载定位装置的校准车辆连接；6.1) The cloud processing unit is connected to the roadside millimeter-wave radar and the calibration vehicle with the on-board positioning device;
    6.2)基站处理单元，作为独立基站在处理中心安装；6.2) The base station processing unit is installed in the processing center as an independent base station;
    6.3)内嵌式处理单元，内嵌于路侧毫米波雷达或校准车辆。6.3) Embedded processing unit, embedded in roadside millimeter wave radar or calibration vehicle.
  7. 如权利要求1所的述方法，其特征在于，所述第一轨迹数据与第二轨迹数据的获得方法为：在行驶的过程中，所述车载定位装置的校准车辆实时产生带有时间戳的目标车辆轨迹数据，记为第一轨迹数据，第一轨迹数据位于世界坐标系下，并将第一轨迹数据上传至处理单元；路侧毫米波雷达同步获取带有时间戳的目标车辆轨迹检测数据，记为第二轨迹数据，第二轨迹数据位于雷达坐标系下，并将第二轨迹数据上传至处理单元。The method according to claim 1, wherein the method for obtaining the first trajectory data and the second trajectory data is: in the process of driving, the calibration vehicle of the vehicle-mounted positioning device generates a time stamp in real time. The trajectory data of the target vehicle is recorded as the first trajectory data. The first trajectory data is located in the world coordinate system, and the first trajectory data is uploaded to the processing unit; the roadside millimeter-wave radar synchronously obtains the target vehicle trajectory detection data with timestamps , denoted as the second trajectory data, the second trajectory data is located in the radar coordinate system, and the second trajectory data is uploaded to the processing unit.
  8. 如权利要求1所的述方法，其特征在于，所述处理单元进行数据处理与数据分析，包含三个步骤：1)坐标转换，2)重采样，3)时空相似度计算。The method of claim 1, wherein the processing unit performs data processing and data analysis, including three steps: 1) coordinate transformation, 2) resampling, and 3) spatiotemporal similarity calculation.
  9. 如权利要求8所述的方法，其特征在于，所述坐标转换的方法包括：The method of claim 8, wherein the coordinate transformation method comprises:
    ①在第一轨迹数据与第二轨迹数据中，利用手动或自动的方式选取四组对应的特征点对；① In the first trajectory data and the second trajectory data, select four groups of corresponding feature point pairs manually or automatically;
    ②基于所述四组特征点对，分别将第一轨迹数据点(x r,y r,z r)，与第二轨迹数点(x v,y v,z v)代入下式中，计算雷达校准参数矩阵H： ② Based on the four sets of feature point pairs, the first trajectory data points (x r , y r , z r ) and the second trajectory data points (x v , y v , z v ) are respectively substituted into the following formulas to calculate Radar calibration parameter matrix H:
    Figure PCTCN2022084929-appb-100001
    Figure PCTCN2022084929-appb-100001
    ③根据所求解的雷达校准参数矩阵H，将第二轨迹数据点(x r,y r,z r)转换至世界坐标系下的(x′ r,y′ r,z′ r)，作为第三轨迹数据： ③ According to the solved radar calibration parameter matrix H, convert the second trajectory data points (x r , y r , z r ) to (x′ r , y′ r , z′ r ) in the world coordinate system, as the first Three track data:
    Figure PCTCN2022084929-appb-100002
    Figure PCTCN2022084929-appb-100002
    Figure PCTCN2022084929-appb-100003
    Figure PCTCN2022084929-appb-100003
    Figure PCTCN2022084929-appb-100004
    Figure PCTCN2022084929-appb-100004
    其中，参数x r表示路侧毫米波雷达在雷达坐标系中的X坐标，参数x′ r表示路侧毫米波雷 Among them, the parameter x r represents the X coordinate of the roadside millimeter-wave radar in the radar coordinate system, and the parameter x′ r represents the roadside millimeter-wave radar
    达在雷达坐标系中的X坐标转换到世界坐标系后新的坐标数据；The new coordinate data after the X coordinate in the radar coordinate system is converted to the world coordinate system;
    参数y r表示路侧毫米波雷达在基准坐标系中的Y坐标，参数y′ r表示路侧毫米波雷达在雷达坐标系中的Y坐标转换到世界坐标系后新的坐标数据； The parameter y r represents the Y coordinate of the roadside millimeter-wave radar in the reference coordinate system, and the parameter y′ r represents the new coordinate data after the Y coordinate of the roadside millimeter-wave radar in the radar coordinate system is converted to the world coordinate system;
    参数z r表示路侧毫米波雷达在基准坐标系中的Z坐标，参数z′ r表示路侧毫米波雷达在雷达坐标系中的Z坐标转换到世界坐标系后新的坐标数据。 The parameter z r represents the Z coordinate of the roadside millimeter-wave radar in the reference coordinate system, and the parameter z′ r represents the new coordinate data after the Z coordinate of the roadside millimeter-wave radar in the radar coordinate system is converted to the world coordinate system.
  10. 如权利要求8所述的方法，其特征在于，所述重采样的方法包括：The method of claim 8, wherein the resampling method comprises:
    ①对于第一轨迹数据与第三轨迹数据，求取第一轨迹数据与第三轨迹数据点集中采样时间点的并集T all：T 1∪T 2＝T all ①For the first trajectory data and the third trajectory data, obtain the union T all of the sampling time points in the first trajectory data and the third trajectory data points: T 1 ∪ T 2 =T all
    ②利用所求取的采样时间点的并集T all，检查第一轨迹数据和第三轨迹数据中分别缺失的时间点。对所检查出的缺失时间点，基于三次插值算法补缺横纵坐标；通过已知轨迹点拟合三次函数G x、G y： ②Using the obtained union of sampling time points T all , check the missing time points in the first trajectory data and the third trajectory data respectively. For the detected missing time points, fill in the missing horizontal and vertical coordinates based on the cubic interpolation algorithm; fit the cubic functions G x and G y through the known track points:
    G x＝ax 3+bx 2+cx+d G x =ax 3 +bx 2 +cx+d
    G y＝ex 3+fx 2+gx+h G y =ex 3 +fx 2 +gx+h
    ③以T all时刻点作为重采样的时间点，通过三次函数G x、G y实现重采样；此时两类轨迹处于同一坐标系且采样时间点一致，第一轨迹数据与路侧毫米波雷达所对应的第三轨迹数据点实现一对一匹配； ③ Take the time point of T all as the time point of resampling, and realize the resampling through the cubic functions G x and G y ; at this time, the two types of trajectories are in the same coordinate system and the sampling time points are the same, and the first trajectory data is the same as that of the roadside millimeter wave radar. The corresponding third trajectory data points realize one-to-one matching;
    其中，参数a,b,c,d代表用于拟合轨迹X坐标的三次插值函数G x的系数； Among them, the parameters a, b, c, d represent the coefficients of the cubic interpolation function G x used to fit the X coordinate of the trajectory;
    参数e,f,g,h代表用于拟合轨迹坐标Y的三次插值函数G y的系数。 The parameters e, f, g, h represent the coefficients of the cubic interpolation function G y used to fit the trajectory coordinate Y.
  11. 如权利要求8所述的方法，其特征在于，所述时空相似度计算，是指对经过坐标转换与重采样后的第一轨迹数据、第三轨迹数据进行时空相似度计算；依据时空相似度的表达式：The method according to claim 8, wherein the calculation of the spatiotemporal similarity refers to calculating the spatiotemporal similarity of the first trajectory data and the third trajectory data after coordinate transformation and resampling; expression:
    Figure PCTCN2022084929-appb-100005
    Figure PCTCN2022084929-appb-100005
    分别代入相同采样时间点下的第一轨迹数据点与第三轨迹数据点对，计算出第一轨迹数据与第二轨迹数据点的时空相似度值；Substitute the first trajectory data point and the third trajectory data point pair under the same sampling time point respectively, and calculate the spatiotemporal similarity value of the first trajectory data and the second trajectory data point;
    其中，参数
    Figure PCTCN2022084929-appb-100006
    代表空间相似度的权重，采用权重参数
    Figure PCTCN2022084929-appb-100007
    自由调节空间相似度与时间相似度所占比重；     Among them, the parameter
    Figure PCTCN2022084929-appb-100006
    The weight representing the spatial similarity, using the weight parameter
    Figure PCTCN2022084929-appb-100007
    Freely adjust the proportion of spatial similarity and temporal similarity;
    参数β代表非重采样点相似度的权重，采用权重参数β自由调节非重采样点相似度与重采样点相似度所占比重；The parameter β represents the weight of the similarity of the non-resampling point, and the weight parameter β is used to freely adjust the proportion of the similarity of the non-resampling point and the similarity of the resampling point;
    参数f S代表空间相似度，参数f T表示时间相似度，参数
    Figure PCTCN2022084929-appb-100008
    表示非重采样点之间的相似度，参数
    Figure PCTCN2022084929-appb-100009
    表示重采样点之间的相似度。     The parameter f S represents the spatial similarity, the parameter f T represents the temporal similarity, and the parameter
    Figure PCTCN2022084929-appb-100008
    Represents the similarity between non-resampled points, parameter
    Figure PCTCN2022084929-appb-100009
    Represents the similarity between resampling points.
  12. 如权利要求11所述的方法，其特征在于，若相似度
    Figure PCTCN2022084929-appb-100010
    小于等于相似度阈值d，则认为雷达校准参数矩阵H满足精度要求；若相似
    Figure PCTCN2022084929-appb-100011
    大于相似度阈值d，则认为雷达校准参数矩阵H不满足精度要求，路侧毫米波雷达需要进行重新校准。     The method of claim 11, wherein if the similarity
    Figure PCTCN2022084929-appb-100010
    is less than or equal to the similarity threshold d, the radar calibration parameter matrix H is considered to meet the accuracy requirements; if similar
    Figure PCTCN2022084929-appb-100011
    If it is greater than the similarity threshold d, it is considered that the radar calibration parameter matrix H does not meet the accuracy requirements, and the roadside millimeter-wave radar needs to be recalibrated.
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