WO2022141910A1 - Procédé dynamique de fusion et de segmentation de nuage de points de cinémomètre à laser de voie de circulation routière à base de champ de risque de sécurité de conduite - Google Patents

Procédé dynamique de fusion et de segmentation de nuage de points de cinémomètre à laser de voie de circulation routière à base de champ de risque de sécurité de conduite Download PDF

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WO2022141910A1
WO2022141910A1 PCT/CN2021/085146 CN2021085146W WO2022141910A1 WO 2022141910 A1 WO2022141910 A1 WO 2022141910A1 CN 2021085146 W CN2021085146 W CN 2021085146W WO 2022141910 A1 WO2022141910 A1 WO 2022141910A1
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point cloud
data
field
risk
bounding box
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PCT/CN2021/085146
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Chinese (zh)
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杜豫川
许军
暨育雄
赵聪
倪澜涛
沈煜
曹静
王金栋
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杜豫川
许军
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Priority to GB2316614.3A priority Critical patent/GB2621048A/en
Priority to CN202280026657.8A priority patent/CN117441197A/zh
Priority to PCT/CN2022/084738 priority patent/WO2022206942A1/fr
Publication of WO2022141910A1 publication Critical patent/WO2022141910A1/fr

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Definitions

  • the invention relates to a vehicle automatic driving perception assistance technology, in particular to a vehicle-road laser radar point cloud dynamic segmentation and fusion method based on a driving safety risk field, which is mainly oriented to infrastructure data collection and driving safety in a vehicle-road collaborative environment. Risk field calculation, data segmentation, data publishing, and data fusion to enhance the hazard perception capabilities of autonomous vehicles.
  • Autonomous driving technology is an emerging technology in the field of transportation that my country and the world are investing more in. All countries are trying to develop and establish a set of safe and efficient autonomous driving technology routes. Among them, my country is strongly supporting a technology.
  • the solution is a vehicle-road coordination model.
  • vehicle-road coordination model not only relies on the intelligent means of bicycles, but also expands the intelligent system to the entire traffic environment. Thereby improving the overall operational safety and efficiency of the entire transportation system.
  • the safety distance model is mainly used. When the following distance is less than the safe distance, the auxiliary system will issue an alarm and automatically brake.
  • Many safety distance models determine the safety state of the vehicle by analyzing the safety distance of the relative movement of the front and rear vehicles in real time.
  • the driver safety assistance algorithm is mainly based on the current position of the car (CCP), time to lane crossing (TLC) and variable rumble band (VRBS).
  • field theory has become an emerging direction in the field of autonomous driving safety, originally used to solve vehicle and robot navigation.
  • a distinct advantage is that it allows the vehicle to navigate autonomously using only its position and local sensor measurements.
  • the obstacles of the vehicle are modeled as repulsive potential fields (risk fields).
  • the vehicle can use the field strength gradient at its location to generate control actions to navigate while avoiding obstacles.
  • field theory is mainly applied to motion planning of autonomous vehicles and modeling of driver behavior in specific traffic scenarios, such as car following.
  • risk factors such as driver's personality, psychological and physiological characteristics, complex road conditions, etc., are not fully considered, and the driver-vehicle-road interaction is insufficiently described. Therefore, the practical application of these models is limited.
  • lidar technology is a widely used and effective technical means. It has the advantages of wide scanning range, intuitive results, and is not affected by ambient natural light. It is very suitable for the perception field of autonomous driving.
  • the output result of lidar is in point cloud format, which has relatively low-level data characteristics.
  • the scanned data is recorded in the form of points. Each point contains three-dimensional coordinates, and some may contain color information (RGB) or reflection intensity information (Intensity). Similar to the vigorous development of image processing technology, the processing methods of lidar point cloud data are gradually increasing, involving target detection, target tracking, semantic segmentation and other directions.
  • Semantic segmentation is a basic task in computer vision. Semantic segmentation can have a more fine-grained understanding of images, which is very important in the field of autonomous driving.
  • point cloud semantic segmentation it is an extension of image semantic segmentation in computer vision. Facing the original point cloud data, it perceives the target type, quantity and other characteristics in the scene, and renders the points of the same target into the same color.
  • 3D point cloud segmentation requires knowledge of both the global geometry and fine-grained details of each point. According to the different segmentation granularity, 3D point cloud segmentation methods can be divided into three categories: semantic segmentation, instance segmentation and partial segmentation.
  • the effect of point cloud segmentation has a lot to do with the quality of point cloud data.
  • the roadside equipment can provide unprocessed point cloud data to maximize the detection effect, but this will lead to the problem of excessive data transmission.
  • the latest V2V research shows that sending information on all road objects detected by onboard sensors can still lead to high load on the transmission network. Then, a dynamic screening mechanism is needed for road objects to screen out more representative "skeleton" point clouds that only lose a little or no feature information.
  • the specific implementation process is as follows: according to the theoretical derivation, formulate the point cloud value judgment standard; set a value for each point in the point cloud data; judge whether to input it into the transmission network according to the value.
  • the present invention provides a vehicle-road lidar point cloud dynamic segmentation and fusion method based on the driving safety risk field. Based on the theoretical idea of risk field calculation and specific point cloud data, a driving safety risk calculation mechanism that can be used for actual calculation and covers most of the objects affecting road safety is proposed. The point cloud of the risky object is used as the final transmission result; it is then fused with the point cloud collected by the target vehicle lidar, and the method is evaluated. It includes the following steps:
  • the point cloud of the traffic scene is obtained through lidar scanning. This data is the data source of all subsequent links.
  • the flow chart of the data acquisition module is shown in Figure 2.
  • A1 The first one is to use only the roadside lidar in the roadside perception unit to perform lidar scanning to construct the scene point cloud, then the construction of the safety risk field and the numerical calculation in the subsequent links only use the roadside Lidar point cloud;
  • a 2 The second is to use the roadside lidar in the roadside perception unit and the lidar mounted on the preset calibration vehicle in the scene to scan the lidar to construct the scene point cloud, so it is safe in the subsequent links.
  • the construction of the risk field and the numerical calculation both use the point cloud of the roadside lidar and the point cloud of the lidar mounted on the calibration vehicle, which play the role of mutual verification and proofreading.
  • the driving safety risk field calculation module includes a target detection sub-module and a safety field calculation sub-module.
  • the flow chart is shown in Figure 3.
  • B 1 target detection sub-module.
  • the scene point cloud obtained in (1) is subjected to deep learning 3D target detection, and the algorithm is PV-RCNN. That is, input the scene point cloud data to obtain the target detection result.
  • the data source is lidar point cloud data
  • the layout position of lidar determines the size and characteristics of the scene point cloud data, etc.
  • the bounding box is the bounding box of each target in the scene, and the attributes are position, length, height, width, Deflection angle, etc., as shown in Figure 4.
  • the scene acquisition sub-module is to obtain some features and information in the scene in advance before the target detection sub-module, so as to facilitate better target detection and subsequent safety field calculation.
  • This sub-module There are various options for this sub-module as follows:
  • the RGB information of the scene collected by the camera sensor and the corresponding horizontal and vertical boundaries are used to determine the type of the object, so as to assist in identifying the type of the static object.
  • B 3 Security field calculation sub-module.
  • the input to this submodule is the type of static object and the object detection bounding box. Drawing on the field theory methods such as gravity field and magnetic field in physics, everything that may cause risks in the traffic environment is regarded as the source of danger, and it spreads around it.
  • the field strength of the risk field can be understood as the distance from the source of danger.
  • the magnitude of the risk factor at a certain distance The closer the distance to the danger center, the greater the possibility of an accident, and the farther the distance, the lower the accident probability. When the distance approaches 0, it can be considered that there is a contact collision between the target vehicle and the source of danger, that is, a traffic accident has occurred. .
  • ES is the field strength vector of the driving safety risk field
  • ER is the field strength vector of the static risk field
  • EV is the field strength vector of the dynamic risk field.
  • the driving safety risk field model can be expressed as the traffic factor in the actual scene. potential driving risk. Risk is measured by the likelihood of an accident and the severity of the accident.
  • the driving safety risk field is divided into two categories according to the different sources, namely the static risk field source and the dynamic risk field source:
  • Static risk field The field source is a relatively static object in the traffic environment, mainly road markings such as lane dividing lines, and rigid separation facilities such as the central divider. This type of object has two characteristics: (1) Without considering road construction, this type of object is in a stationary state relative to the target vehicle; (2) Except for some rigid separation facilities, this type of object makes the driver intentionally stay away from its location based on legal effects. , but even if the driver actually crosses the lane line, it is not necessarily an immediate traffic accident.
  • LT a is the risk coefficient of different types of lane markings a 1 ;
  • R a is a constant greater than 0, representing the road condition influencing factor at (x a , ya a );
  • f d is the distance influencing factor of different types of lane marking a 1 ;
  • r aj is the distance vector between the lane mark a 1 and the target vehicle j, in this case (x j , y j ) is the centroid of the target vehicle j, (x a , y a ) means (x j , y j ) ) is the point where the vertical line intersects with the lane marking a 1 ;
  • k 1 is a constant greater than 0, which represents the amplification factor of the distance; r aj /
  • the field source is the relatively moving objects in the traffic environment, mainly vehicles, pedestrians, roadblocks, etc. This type of object also has two characteristics: (1) The moving target vehicle is used as the reference frame and has relative speed. 2It is strictly forbidden to collide with such objects, otherwise it will inevitably cause a serious traffic accident.
  • the field strength vector E V is:
  • r bj (x j -x b ,y j -y b )
  • r bj is the distance vector between the dynamic risk field source b and the target vehicle j; k 2 , k 3 and G are all constants greater than 0; R b has the same meaning as Ra ; T bj is the dynamic risk field source b and the target vehicle j Type correction coefficient between ; v bj is the relative velocity between dynamic risk field source b and target vehicle j, ⁇ is the angle between v bj and r bj directions, positive in the clockwise direction. The larger the EV value, the higher the risk caused by the dynamic risk field source b to the target vehicle j.
  • the present invention selects the point cloud data obtained by the roadside laser radar as the data source, and uses the roadside unobstructed point cloud scanning result as the calculation carrier.
  • step 3 Substitute the attributes such as the relative position and type of the target vehicle and other objects other than it, as well as parameters such as the distance of the static risk field source relative to the target vehicle in step 1), as well as traffic environment factors such as road conditions into the driving safety
  • the speed of the target vehicle in the relative speed is set as an unknown parameter, and the calculation process is delayed, then the relative speed is an expression with unknown parameters.
  • the safety risk of each object in the scanning range for the calculation target is obtained, so as to form the driving safety risk distribution with the target vehicle as the core.
  • the flow chart of the data segmentation module is shown in Figure 8.
  • the scene point cloud is divided into the point cloud inside the bounding box and the point cloud outside the bounding box.
  • an algorithm is designed to detect whether the point cloud is within the bounding box, so that the point cloud can be divided into two types: point cloud inside the bounding box and point cloud outside the bounding box.
  • C 1 Sampling scheme; the scene point cloud data P 1 obtained by the data acquisition sub-module; the target detection bounding box X 1 and the safety risk field data S 1 obtained by the driving safety risk field calculation module are input as the sub-module, and the condition is first judged , judging whether the points in the scene point cloud data P 1 are within the bounding box X 1 , and obtain point cloud data P 11 within the bounding box and point cloud data P 12 outside the bounding box. Then set the hyperparameters f 1 and f 2 , and randomly sample the point cloud data P 11 and P 12 inside and outside the bounding box according to f 1 and f 2 to obtain the point cloud data P 2 after data segmentation.
  • C 2 segmentation scheme; the scene point cloud data P 1 obtained by the data acquisition sub-module; the target detection bounding box X 1 and the safety risk field data S 1 obtained by the driving safety risk field calculation module are input as the sub-module, and the condition is first judged , judging whether the points in the scene point cloud data P 1 are within the bounding box X 1 , and obtain point cloud data P 11 within the bounding box and point cloud data P 12 outside the bounding box. Then, the point cloud data P 11 in the bounding box is selected, the point cloud data P 12 outside the bounding box is eliminated, and the point cloud data P 2 after data segmentation is obtained.
  • C3 Based on the safety risk field sampling scheme; the scene point cloud data P1 obtained by the data acquisition submodule ; the target detection bounding box X1 and the safety risk field data S1 obtained by the driving safety risk field calculation module are input as the submodules , First, through conditional judgment, it is judged whether the point in the scene point cloud data P 1 is within the bounding box X 1 , and the point cloud data P 11 within the bounding box and the point cloud data P 12 outside the bounding box are obtained. Then set the numerical threshold value f 3 of the security risk field, sample the point cloud data P 11 and P 12 inside and outside the bounding box according to the threshold value, and obtain the point cloud data P 2 after data segmentation.
  • the scene point cloud data P 1 obtained by the data acquisition sub-module; the target detection bounding box X 1 and the safety risk field data S 1 obtained by the driving safety risk field calculation module are input as the sub-modules, and the scene point cloud data is first judged after conditional judgment. Whether the point in P 1 is within the bounding box X 1 , the point cloud data P 11 in the bounding box and the point cloud data P 12 outside the bounding box are obtained. Then, set the numerical threshold value f3 of the security risk field to divide the point cloud data P11 and P12 inside and outside the bounding box according to the threshold value, and obtain the point cloud data P2 after data segmentation .
  • the specific method for extracting the dangerous range is: if the dangerous target is a static risk field source, then take the plane where the risk field source is located as the center, and the areas with a width of d/2 on the left and right are the dangerous range, where d is the width of the dangerous target; If the dangerous target is a dynamic risk field source, then take the centroid of the dangerous target as the center, and intercept a rectangular area with a width of 1.5d and a length of (0.5l+0.5l ⁇ k) as the dangerous range, where d is the width of the dangerous target, l is the length of the dangerous target, and k is the speed correction coefficient not less than 1; the dangerous range is extracted in sequence according to the size of the risk coefficient of the dangerous source, and the overlapping area of the dangerous range is only extracted once.
  • the final extracted total danger range result can be provided to the target vehicle as the perception assistance data of the target vehicle.
  • the flow chart of the data publishing module is shown in Figure 9. Based on the results of data segmentation, the data is compressed by the roadside sensing unit, and then a data transmission channel between the roadside sensing unit and the target vehicle is established. numbered vehicle. Then to determine whether the target vehicle is moving, there are two alternatives:
  • D 1 If the target vehicle is stationary, the point cloud, static risk field, and dynamic risk field vector sum after data segmentation can be directly released, that is, the safety risk field vector sum, and the modulus is a numerical value;
  • D 2 If the target vehicle moves, publish the point cloud, static risk field, and semi-finished product risk field data after data segmentation, and then substitute the speed of the target vehicle to obtain the vector sum of its safety risk field, and the modulus is a numerical value.
  • the point cloud of a certain area around the object with high safety field risk value obtained after data segmentation is fused with the point cloud scanned by the target vehicle's lidar, that is, the point cloud coordinate transformation matrix is designed to match the high-risk data point cloud between the vehicle and the roadside. and compress the data of the fused point cloud to obtain compressed point cloud data
  • V represents the original point cloud of the vehicle without processing
  • I represents the original point cloud obtained by the unprocessed roadside perception unit
  • I 1 represents the point cloud obtained by segmenting the original point cloud by the roadside perception unit using the segmentation in the data segmentation method
  • I 2 represents the point cloud obtained by the roadside perception unit segmenting the original point cloud using the sampling in the data segmentation method
  • I 1S represents the point cloud obtained by dividing the original point cloud by the roadside sensing unit using the segmentation based on the safety field in the data segmentation method
  • I 2S represents the point cloud obtained by segmenting the original point cloud by the roadside sensing unit using the sampling based on the safety field in the data segmentation method
  • Driving safety risk field the distribution of the driving safety risk of the static and dynamic objects in the scene to the driving vehicle. In the present invention, unless otherwise specified, it is synonymous with the safety risk field.
  • Lidar an active remote sensing device that uses a laser as the emission light source and adopts photoelectric detection technology.
  • Point Cloud A point cloud is a dataset of points in a coordinate system.
  • Point cloud data including three-dimensional coordinates X, Y, Z, color, intensity value, time, etc., that is, a structured matrix.
  • Target vehicle-side lidar L 1 lidar mounted on the target vehicle.
  • Roadside Lidar L 2 Lidar installed on the roadside.
  • Vehicle-side lidar point cloud Represents the point cloud acquired by the vehicle-side lidar.
  • Roadside lidar point cloud represents the point cloud acquired by roadside lidar.
  • Scene Point Cloud The point cloud of the traffic scene.
  • V2V Vehicles in motion provide end-to-end wireless communication, that is, through V2V communication technology, vehicle terminals exchange wireless
  • Convolution The result of summing two variables after multiplying them in a certain range.
  • CNN Convolutional Neural Network
  • Voxel short for volume element, a volume containing voxels can be rendered by volume rendering or by extracting polygons for a given threshold contour
  • the shape isosurface is displayed. It is the smallest unit of division of digital data in three-dimensional space.
  • MLP Multi-layer perceptron, also known as artificial neural network, in addition to the input and output layers, there can be multiple hidden layers in the middle, the simplest
  • the MLP contains only one hidden layer, that is, a three-layer structure.
  • V2X vehicle to everything, that is, the exchange of information between vehicles and the outside world.
  • RSU Road Side Unit, the roadside unit, is installed on the roadside and communicates with the vehicle-mounted unit.
  • OBU On board Unit, the on-board unit.
  • Skeleton point The key node of the 3D point cloud model.
  • Security risk threshold manually set the value according to the actual application scenario.
  • the safety risk value for the target vehicle is greater than the set threshold.
  • Field source all kinds of objects involved in the calculation process in the calculation of driving safety risk.
  • Point cloud registration For point cloud data in different coordinate systems, the transformation matrix, that is, the rotation matrix R and the translation matrix T, is obtained through registration, and the error is calculated to compare the matching results.
  • Data acquisition module A The function is data acquisition, the input is the traffic scene, and the output is the scene point cloud data P 1 .
  • Driving safety risk field calculation module B the function is driving safety risk field calculation, including target detection sub-module B 1 , scene acquisition sub-module B 2 , and safety field calculation sub-module B 3 , the input is the scene point cloud data P 1 , and the output is Target detection bounding box X 1 , safety risk field value S 1 /semi-finished product S 2 .
  • Bounding box The point cloud target detection result, the attributes are position, length, height, width, deflection angle, etc., such as the target detection bounding box X 1 .
  • Safety risk field value S 1 the modulus of the safety risk field vector sum of all risk sources in the scene for a certain object.
  • Semi-finished product risk field S 2 that is, when calculating the dynamic risk field, the speed of the target vehicle is set as an unknown parameter, and the expression with the unknown parameter and passed backwards is called semi-finished product.
  • Data segmentation module C the function is data segmentation, the input is the scene point cloud data P 1 , the target detection bounding box X 1 , the safety risk field value S 1 /semi-finished product risk field S 2 , and the output is the point cloud data P 2 after data segmentation .
  • Data release module D The function is data release, that is, the roadside perception unit releases data to the target vehicle in the scene, and the released data is the point cloud data P 2 after data segmentation, and the safety risk field/semi-finished product risk field data S 1 /S 2 .
  • Data fusion module E The function is to fuse the point cloud data P 2 after data segmentation with the point cloud data P 3 of the target vehicle to obtain the fused point cloud data P 4 , and obtain compressed point cloud data P 5 through data compression.
  • Method evaluation module F For the compressed point cloud data P 5 , the target detection result R 1 is obtained through the PV-RCNN deep learning target detection algorithm, and the evaluation is given for the target detection result R 1 , and the best data segmentation scheme is selected.
  • Roadside perception unit including but not limited to roadside lidar, cameras and other sensors.
  • Target vehicle The object of risk generated by each risk source in the driving safety risk field is also the object of data transmission by the drive test perception unit in data transmission, that is, ego car.
  • Target vehicle the target vehicle in the process of calculating the safety risk field.
  • Target vehicle lidar L 3 lidar mounted on the target vehicle.
  • Segmentation The method of data segmentation, which separates the point cloud of the detection target and the non-detection target, which is different from the meaning of data segmentation.
  • Sampling The method of data segmentation, randomly sampling the point cloud of the detection target and the non-detection target according to the weight, the default parameters are 0.8 and 0.2.
  • Figure 1 Flow chart of a dynamic segmentation and fusion method of vehicle and road LiDAR point cloud based on driving safety risk field
  • FIG. 4 Schematic diagram of the bounding box of the target detection result
  • FIG. 6 Schematic diagram of security risk field distribution
  • FIG. 7 Schematic diagram of the xoy plane projection of the security risk field
  • FIG. 12 Schematic diagram of the method evaluation reference system
  • a roadside lidar point cloud segmentation method based on the safety risk field mechanism its flowchart is shown in Figure 1, that is, six modules: data acquisition module A, driving safety risk field calculation module B, data segmentation module C, data Publishing module D, data fusion module E, and method evaluation module F.
  • a 1 In an autonomous driving traffic scene, only the roadside lidar in the roadside perception unit performs lidar scanning to obtain the point cloud data P1 of the traffic scene, which is the data source for all subsequent links.
  • the driving safety risk field calculation module includes a target detection sub-module and a safety field calculation sub-module.
  • the flow chart is shown in Figure 3.
  • B 1 target detection sub-module.
  • the scene point cloud obtained in A is used for deep learning 3D target detection, and the algorithm is PV-RCNN. That is, input the scene point cloud data to obtain the target detection result.
  • the data source is lidar point cloud data
  • the layout position of lidar determines the size and characteristics of the scene point cloud data, etc.
  • the bounding box is the bounding box X 1 of each target in the scene, and the attributes are position, length, height, width, deflection angle, etc., as shown in Figure 4.
  • the scene acquisition sub-module is to obtain some features and information in the scene in advance before the target detection sub-module, so as to facilitate better target detection and subsequent safety field calculation.
  • This sub-module There are various options for this sub-module as follows:
  • the RGB information of the scene collected by the camera and the corresponding horizontal and vertical boundaries are used to determine the type of the object, so as to assist in identifying the type of the static object.
  • B 3 Security field calculation sub-module.
  • the input to this submodule is the type of static object and the object detection bounding box. Drawing on the field theory methods such as gravity field and magnetic field in physics, everything that may cause risks in the traffic environment is regarded as the source of danger, and it spreads around it.
  • the field strength of the risk field can be understood as the distance from the source of danger.
  • the magnitude of the risk factor at a certain distance The closer the distance to the danger center, the greater the possibility of an accident, and the farther the distance, the lower the accident probability. When the distance approaches 0, it can be considered that there is a contact collision between the target vehicle and the source of danger, that is, a traffic accident has occurred. .
  • ES is the field strength vector of the driving safety risk field
  • ER is the field strength vector of the static risk field
  • EV is the field strength vector of the dynamic risk field.
  • the driving safety risk field model can be expressed as the traffic factor in the actual scene. potential driving risk. Risk is measured by the likelihood of an accident and the severity of the accident.
  • the driving safety risk field is divided into two categories according to the different sources, namely the static risk field source and the dynamic risk field source:
  • Static risk field The field source is a relatively static object in the traffic environment, mainly road markings such as lane dividing lines, and rigid separation facilities such as the central divider.
  • road markings such as lane dividing lines
  • rigid separation facilities such as the central divider.
  • traffic regulations stipulate that vehicles are not allowed to drive or cross in solid lanes. However, if the driver unintentionally leaves the current lane, perceiving the risk of violating the constraints of the lane markings, the driver will steer the vehicle back into the center of the lane. At the same time, the closer the vehicle is to the lane markings, the greater the risk.
  • Driving risk is also related to road conditions, and poor road conditions can lead to high risks.
  • the driving risk of relatively stationary objects is mainly affected by visibility, and the lower the visibility, the higher the driving risk.
  • This type of object has two characteristics: (1) Without considering road construction, this type of object is in a stationary state relative to the target vehicle, because its actual meaning is a dangerous boundary and does not have speed attributes; (2) Except for some rigid separation facilities, This type of object makes the driver intentionally stay away from its location based on legal effects, but even if the driver actually crosses the lane line, it may not necessarily cause a traffic accident immediately.
  • LT a is the risk factor of different lane marking a 1 types, which is determined by traffic laws, generally rigid separation facilities > can not cross the lane separation line > can cross the lane separation line.
  • the parameters of common facilities and lane lines are as follows: guardrail type or green belt type median: 20 to 25; sidewalk stones: 18 to 20; yellow solid line or dotted line: 15 to 18; white solid line: 10 ⁇ 15; white dotted line: 0-5.
  • R a is a constant greater than 0, which indicates the influencing factors of road conditions at (x a , y a ), which are determined by traffic environment factors such as road adhesion coefficient, road slope, road curvature and visibility in the vicinity of object a. Pick a fixed value for a section of road.
  • the data interval generally used is [0.5, 1.5].
  • f d is the influence factor of the distance of different types of lane markings a 1 , which is determined by the object type, object width, lane width and so on.
  • D is the lane width
  • d is the width of the target vehicle j
  • d generally takes the width of the bounding box of the target vehicle j.
  • r aj is the distance vector between the lane mark a 1 and the target vehicle j, in this case (x j , y j ) is the centroid of the target vehicle j, (x a , y a ) means (x j , y j ) ) to be the point where the vertical line intersects the lane marking a1.
  • k 1 is a constant greater than 0, representing the amplification factor of the distance, because the collision risk and the distance between the two objects do not change linearly in general. Generally, the value of k 1 ranges from 0.5 to 1.5.
  • represents the direction of the field strength, but in general practical applications, even if the field strength directions of the two safety risk field sources are opposite to a certain point, the risk size of the point cannot be considered to be reduced accordingly, so it is usually still The scalars are superimposed.
  • the field strength distribution results are shown in Fig. 5(a).
  • Dynamic risk field The field source is a relatively dynamic object in the traffic environment, and the magnitude and direction of its field strength vector are determined by the properties and states of the moving object and road conditions.
  • the dynamic objects here refer to dynamic objects that can actually collide with vehicles and cause heavy losses, mainly vehicles, pedestrians, and roadblocks.
  • This type of object also has two characteristics: (1) Although the above-mentioned objects may be stationary relative to the road, such as roadside parking, roadblock facilities, etc., they still have relative speed with the dynamic target vehicle as the reference frame. 2It is strictly forbidden to collide with such objects, otherwise it will inevitably cause a serious traffic accident.
  • the present invention assumes that the power function form of driving risk is a function of vehicle-target distance.
  • r bj (x j -x b ,y j -y b )
  • r bj is the distance vector between the dynamic risk field source b and the target vehicle j.
  • k 2 , k 3 and G are all constants greater than 0.
  • the meaning of k 2 is the same as that of k 1 above, and k 3 is the hazard correction for different speeds.
  • G is analogous to the electrostatic force constant and is used to describe an object with two units of mass at a unit distance. The size of the risk factor between. Generally, the value range of k 2 is 0.5 to 1.5, the value range of k 3 is 0.05 to 0.2, and the value of G is usually 0.001.
  • R b is the same as that of R a , and the data interval used is also [0.5, 1.5].
  • T bj is the type correction coefficient between the dynamic risk field source b and the target vehicle j.
  • the risk coefficients of vehicle-vehicle collision and vehicle-person collision are different.
  • Common types of correction parameters are as follows: vehicle-vehicle frontal collision: 2.5 to 3; vehicle-vehicle rear-end collision: 1 to 1.5; person-vehicle collision: 2 to 2.5; vehicle-to-barrier collision: 1.5 to 2.
  • v bj is the relative velocity between the dynamic risk field source b and the target vehicle j, that is, the vector difference between the velocity v b of the field source b and the velocity v j of the target vehicle j.
  • ⁇ is the angle between the v bj and r bj directions, positive in the clockwise direction.
  • the semi-finished product of relative velocity is:
  • the present invention selects the lidar point cloud data as the data source, and uses the roadside unobstructed point cloud scanning result as the calculation carrier.
  • n can range from 50 to 100;
  • the point cloud density of each statistical space is detected. If it is greater than the threshold ⁇ (related to the point cloud density, the general value is 1000), the point cloud in the space is randomly sampled to keep its density, and finally obtain a more ideal global static point cloud background.
  • the static risk field sources in the static scene are separated, including the lane dividing line, the median strip, the roadside area, etc., and the plane linear equation of each static risk field source is fitted by random sampling. Generally, it is required to collect more than 100 points evenly along the visual linear direction, and the collected points should not deviate too far from the target.
  • the speed is regarded as the standard speed.
  • the standard speed is the average vehicle speed in the point cloud scanning section under historical statistical conditions, and the direction is consistent with the lane where the target is located.
  • the flow chart of the data segmentation module is shown in Figure 8.
  • the scene point cloud is divided into the point cloud inside the bounding box and the point cloud outside the bounding box.
  • an algorithm is designed to detect whether the point cloud is within the bounding box, so that the point cloud can be divided into two types: point cloud inside the bounding box and point cloud outside the bounding box.
  • C 1 Sampling scheme; the scene point cloud data P 1 obtained by the data acquisition sub-module; the target detection bounding box X 1 and the safety risk field data S 1 obtained by the driving safety risk field calculation module are input as the sub-module, and the condition is first judged , judging whether the points in the scene point cloud data P 1 are within the bounding box X 1 , and obtain point cloud data P 11 within the bounding box and point cloud data P 12 outside the bounding box. Then set the hyperparameters f 1 and f 2 , and randomly sample the point cloud data P 11 and P 12 inside and outside the bounding box according to f 1 and f 2 to obtain the point cloud data P 2 after data segmentation. Taking the screened object as the center, combining sampling or segmentation to extract the point cloud of a certain area around it as the dangerous area.
  • the specific method of extracting the dangerous area is as follows:
  • the dangerous target is a static risk field source, take the plane where the risk field source is located as the center, and the left and right intercepted areas with a width of d/2 are the dangerous range, where d is the width of the target vehicle.
  • the dangerous target is a dynamic risk field source, take the centroid of the dangerous target as the center, and intercept a rectangular area with a width of 1.5d and a length of (0.5l+0.5l ⁇ k) as the dangerous range, of which the 0.5l part is far away from the target vehicle
  • the half side length of , 0.5l ⁇ k is the half side length close to the target vehicle.
  • d is the width of the dangerous target
  • l is the length of the dangerous target.
  • k is a speed correction coefficient not less than 1, which depends on the speed of the dangerous target.
  • Dangerous ranges are extracted in sequence according to the hazard coefficient of the hazard source, and the overlapping areas of the hazard ranges are only extracted once.
  • the final extracted total danger range result can be provided to the target vehicle as the perception assistance data of the target vehicle.
  • A2 In the traffic scene of automatic driving, the roadside lidar in the roadside perception unit and the lidar mounted on the preset calibration vehicle in the scene are used to scan the lidar to obtain the point cloud data P1 of the traffic scene , which is the data source for all subsequent links.
  • the driving safety risk field calculation module includes a target detection sub-module and a safety field calculation sub-module.
  • the flow chart is shown in Figure 3.
  • B 1 target detection sub-module.
  • the scene point cloud obtained in A is used for deep learning 3D target detection, and the algorithm is PV-RCNN. That is, input the scene point cloud data to obtain the target detection result.
  • the data source is lidar point cloud data
  • the layout position of lidar determines the size and characteristics of the scene point cloud data, etc.
  • the bounding box is the bounding box X 1 of each target in the scene, and the attributes are position, length, height, width, deflection angle, etc., as shown in Figure 4.
  • the scene acquisition sub-module is to obtain some features and information in the scene in advance before the target detection sub-module, so as to facilitate better target detection and subsequent safety field calculation.
  • This sub-module There are various options for this sub-module as follows:
  • the RGB information of the scene collected by the camera and the corresponding horizontal and vertical boundaries are used to determine the type of the object, so as to assist in identifying the type of the static object.
  • B 3 Security field calculation sub-module.
  • the input to this submodule is the type of static object and the object detection bounding box. Drawing on the field theory methods such as gravity field and magnetic field in physics, everything that may cause risks in the traffic environment is regarded as the source of danger, and it spreads around it.
  • the field strength of the risk field can be understood as the distance from the source of danger.
  • the magnitude of the risk factor at a certain distance The closer the distance to the danger center, the greater the possibility of an accident, and the farther the distance, the lower the accident probability. When the distance approaches 0, it can be considered that there is a contact collision between the target vehicle and the source of danger, that is, a traffic accident has occurred. .
  • ES is the field strength vector of the driving safety risk field
  • ER is the field strength vector of the static risk field
  • EV is the field strength vector of the dynamic risk field.
  • the driving safety risk field model can be expressed as the traffic factor in the actual scene. potential driving risk. Risk is measured by the likelihood of an accident and the severity of the accident.
  • the driving safety risk field is divided into two categories according to the different sources, namely the static risk field source and the dynamic risk field source:
  • Static risk field The field source is a relatively static object in the traffic environment, mainly road markings such as lane dividing lines, and rigid separation facilities such as the central divider.
  • road markings such as lane dividing lines
  • rigid separation facilities such as the central divider.
  • traffic regulations stipulate that vehicles are not allowed to drive or cross in solid lanes. However, if the driver unintentionally leaves the current lane, perceiving the risk of violating the constraints of the lane markings, the driver will steer the vehicle back into the center of the lane. At the same time, the closer the vehicle is to the lane markings, the greater the risk.
  • Driving risk is also related to road conditions, and poor road conditions can lead to high risks.
  • the driving risk of relatively stationary objects is mainly affected by visibility, and the lower the visibility, the higher the driving risk.
  • This type of object has two characteristics: (1) Without considering road construction, this type of object is in a stationary state relative to the target vehicle, because its actual meaning is a dangerous boundary and does not have speed attributes; (2) Except for some rigid separation facilities, This type of object makes the driver intentionally stay away from its location based on legal effects, but even if the driver actually crosses the lane line, it may not necessarily cause a traffic accident immediately.
  • LT a is the risk factor of different lane marking a 1 types, which is determined by traffic laws, generally rigid separation facilities > can not cross the lane separation line > can cross the lane separation line.
  • the parameters of common facilities and lane lines are as follows: guardrail type or green belt type median: 20 to 25; sidewalk stones: 18 to 20; yellow solid line or dotted line: 15 to 18; white solid line: 10 ⁇ 15; white dotted line: 0-5.
  • R a is a constant greater than 0, which indicates the influencing factors of road conditions at (x a , y a ), which are determined by traffic environment factors such as road adhesion coefficient, road slope, road curvature and visibility in the vicinity of object a. Pick a fixed value for a section of road.
  • the data interval generally used is [0.5, 1.5].
  • f d is the influence factor of the distance of different types of lane markings a 1 , which is determined by the object type, object width, lane width and so on.
  • v is the lane width
  • d is the width of the target vehicle j
  • d generally takes the width of the bounding box of the target vehicle j.
  • r aj is the distance vector between the lane mark a 1 and the target vehicle j, in this case (x j , y j ) is the centroid of the target vehicle j, (x a , y a ) means (x j , y j ) ) to be the point where the vertical line intersects the lane marking a1.
  • k 1 is a constant greater than 0, representing the amplification factor of the distance, because the collision risk and the distance between the two objects do not change linearly in general. Generally, the value of k 1 ranges from 0.5 to 1.5.
  • represents the direction of the field strength, but in general practical applications, even if the field strength directions of the two safety risk field sources are opposite to a certain point, the risk size of the point cannot be considered to be reduced accordingly, so it is usually still The scalars are superimposed.
  • the field strength distribution results are shown in Fig. 5(a).
  • Dynamic risk field The field source is a relatively dynamic object in the traffic environment, and the magnitude and direction of its field strength vector are determined by the properties and states of the moving object and road conditions.
  • the dynamic objects here refer to dynamic objects that can actually collide with vehicles and cause heavy losses, mainly vehicles, pedestrians, and roadblocks.
  • This type of object also has two characteristics: (1) Although the above-mentioned objects may be stationary relative to the road, such as roadside parking, roadblock facilities, etc., they still have relative speed with the dynamic target vehicle as the reference frame. 2It is strictly forbidden to collide with such objects, otherwise it will inevitably cause a serious traffic accident.
  • the present invention assumes that the power function form of driving risk is a function of vehicle-target distance.
  • r bj (x j -x b ,y j -y b )
  • r bj is the distance vector between the dynamic risk field source b and the target vehicle j.
  • k 2 , k 3 and G are all constants greater than 0.
  • the meaning of k 2 is the same as that of k 1 above, and k 3 is the hazard correction for different speeds.
  • G is analogous to the electrostatic force constant and is used to describe an object with two units of mass at a unit distance. The size of the risk factor between. Generally, the value range of k 2 is 0.5 to 1.5, the value range of k 3 is 0.05 to 0.2, and the value of G is usually 0.001.
  • R b is the same as that of R a , and the data interval used is also [0.5, 1.5].
  • T bj is the type correction coefficient between the dynamic risk field source b and the target vehicle j.
  • the risk coefficients of vehicle-vehicle collision and vehicle-person collision are different.
  • Common types of correction parameters are as follows: vehicle-vehicle frontal collision: 2.5 to 3; vehicle-vehicle rear-end collision: 1 to 1.5; person-vehicle collision: 2 to 2.5; vehicle-to-barrier collision: 1.5 to 2.
  • v bj is the relative velocity between the dynamic risk field source b and the target vehicle j, that is, the vector difference between the velocity v b of the field source b and the velocity v j of the object j.
  • ⁇ is the angle between the v bj and r bj directions, positive in the clockwise direction.
  • the semi-finished product of relative velocity is:
  • the present invention selects the lidar point cloud data as the data source, and uses the roadside unobstructed point cloud scanning result as the calculation carrier.
  • n can range from 50 to 100;
  • the point cloud density of each statistical space is detected. If it is greater than the threshold ⁇ (related to the point cloud density, the general value is 1000), the point cloud in the space is randomly sampled to keep its density, and finally obtain a more ideal global static point cloud background.
  • the static risk field sources in the static scene are separated, including lane dividing lines, median strips, roadside areas, etc., and the plane linear equations of each static risk field source are fitted by random sampling. Generally, it is required to collect more than 100 points evenly along the visual linear direction, and the collected points should not deviate too far from the target.
  • the speed is regarded as the standard speed.
  • the standard speed is the average vehicle speed in the point cloud scanning section under historical statistical conditions, and the direction is consistent with the lane where the target is located.
  • the flow chart of the data segmentation module is shown in Figure 8.
  • the scene point cloud is divided into the point cloud inside the bounding box and the point cloud outside the bounding box.
  • an algorithm is designed to detect whether the point cloud is within the bounding box, so that the point cloud can be divided into two types: point cloud inside the bounding box and point cloud outside the bounding box.
  • C3 Based on the safety risk field sampling scheme; the scene point cloud data P1 obtained by the data acquisition submodule ; the target detection bounding box X1 and the safety risk field data S1 obtained by the driving safety risk field calculation module are input as the submodules , First, through conditional judgment, it is judged whether the point in the scene point cloud data P 1 is within the bounding box X 1 , and the point cloud data P 11 within the bounding box and the point cloud data P 12 outside the bounding box are obtained. Then set the numerical threshold value f 3 of the security risk field, sample the point cloud data P 11 and P 12 inside and outside the bounding box according to the threshold value, and obtain the point cloud data P 2 after data segmentation.
  • the dangerous target is a static risk field source, take the plane where the risk field source is located as the center, and the left and right intercepted areas with a width of d/2 are the dangerous range, where d is the width of the target vehicle.
  • the dangerous target is a dynamic risk field source, take the centroid of the dangerous target as the center, and intercept a rectangular area with a width of 1.5d and a length of (0.5l+0.5l ⁇ k) as the dangerous range, of which the 0.5l part is far away from the target vehicle
  • the half side length of , 0.5l ⁇ k is the half side length close to the target vehicle.
  • d is the width of the dangerous target
  • l is the length of the dangerous target.
  • k is a speed correction coefficient not less than 1, which depends on the speed of the dangerous target.
  • Dangerous ranges are extracted in sequence according to the hazard coefficient of the hazard source, and the overlapping areas of the hazard ranges are only extracted once.
  • the final extracted total danger range result can be provided to the target vehicle as the perception assistance data of the target vehicle.
  • the flow chart of the data publishing module is shown in Figure 9. Based on the result of data segmentation, the data is compressed by the roadside sensing unit, and then the data transmission channel between the roadside sensing unit and the target vehicle is established. numbered vehicle. Then determine whether the target vehicle is moving:
  • D 2 point cloud data P 2 and semi-finished product risk field data S 2 after data segmentation are released, and the semi-finished product risk field data S 2 is substituted into the target vehicle speed to obtain safety risk field data S 1 .
  • the flow chart of the data fusion module is shown in Figure 10.
  • the point cloud data P 2 after data segmentation and the target vehicle point cloud data P 3 scanned by the target vehicle lidar are fused, that is, the point cloud coordinate transformation matrix is designed to register the high-risk data point cloud between the vehicle end and the road side, and the fusion result is obtained.
  • the point cloud data P 4 is obtained by data compression on the fused point cloud data P 4 to obtain the compressed point cloud data P 5 .
  • the flowchart of the method evaluation module is shown in Figure 11, which belongs to an optional sub-module.
  • the following describes the method evaluation sub-module for reference.
  • the target detection result R 1 is obtained through the PV-RCNN deep learning target detection algorithm
  • the target detection result R1 is evaluated.
  • the reference system is shown in Figure 12.
  • V represents the original point cloud of the vehicle without processing
  • I represents the original point cloud obtained by the unprocessed roadside perception unit
  • I 1 represents the point cloud obtained by segmenting the original point cloud by the roadside perception unit using the segmentation in the data segmentation method
  • I 2 represents the point cloud obtained by the roadside perception unit segmenting the original point cloud using the sampling in the data segmentation method
  • I 1S represents the point cloud obtained by dividing the original point cloud by the roadside sensing unit using the segmentation based on the safety field in the data segmentation method
  • I 2S represents the point cloud obtained by segmenting the original point cloud by the roadside sensing unit using the sampling based on the safety field in the data segmentation method

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Abstract

La présente invention concerne un procédé dynamique de fusion et de segmentation de nuage de points de cinémomètre à laser de voie de circulation routière à base d'un champ de risque de sûreté de conduite. Le procédé comprend les étapes suivantes : (1) proposer un mécanisme de calcul de champ de risque de sécurité de conduite et analyser de manière quantitative le niveau de risque d'objets statiques, tels que des véhicules stationnés sur le bord de la route, des blocs de route et des panneaux de signalisation, et des objets mobiles tels que des véhicules circulant, des véhicules non motorisés et des piétons, en ce qui concerne une certaine position ; (2) utiliser un procédé de calcul et utiliser des données de nuage de points de cinémomètre à laser d'une unité de détection de bord de route en tant que source de données, calculer le niveau de risque d'autres objets face à un véhicule cible, qui est un véhicule autonome, dans une plage de balayage, et construire une distribution de champs de risque de sécurité de conduite unifiée et centrée sur le véhicule cible ; (3) utiliser un seuil pour filtrer une zone qui présente de grands risques pour le véhicule cible, segmenter des données de nuage de points à partir des données d'origine en tant qu'informations de détection supplémentaire fournies au véhicule autonome ; et (4) traiter et fusionner des informations de niveau de nuage de points acquises par le cinémomètre à laser de l'unité de détection de bord de route avec des informations de niveau de nuage de points acquises par le cinémomètre à laser côté véhicule, et fournir un système d'évaluation de référence du procédé de fusion.
PCT/CN2021/085146 2021-01-01 2021-04-01 Procédé dynamique de fusion et de segmentation de nuage de points de cinémomètre à laser de voie de circulation routière à base de champ de risque de sécurité de conduite WO2022141910A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
GB2316614.3A GB2621048A (en) 2021-03-01 2021-04-01 Vehicle-road laser radar point cloud dynamic segmentation and fusion method based on driving safety risk field
CN202280026657.8A CN117441197A (zh) 2021-01-01 2022-04-01 一种基于行车安全风险场的激光雷达点云动态分割及融合方法
PCT/CN2022/084738 WO2022206942A1 (fr) 2021-01-01 2022-04-01 Procédé de segmentation et de fusion dynamiques de nuages de points de radar laser basé sur un champ de risque de sécurité au volant

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