CN114993324A - Vehicle positioning method, device and equipment - Google Patents
Vehicle positioning method, device and equipment Download PDFInfo
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Abstract
The method comprises the step of calculating second positioning data through dead reckoning after first positioning data of target equipment on a vehicle, which are initial at the current moment, are obtained. And fusing the first positioning data and the second positioning data through a Kalman filtering algorithm to obtain third positioning data, wherein the precision of the third positioning data is obviously higher than that of the second positioning data. And then acquiring a high-precision map within the preset range of the third positioning data, projecting the third positioning data onto the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises a current lane positioning result, so that lane-level positioning is realized, and the accuracy of vehicle positioning is improved. In addition, the third positioning data can be compensated based on the visual data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
Description
Technical Field
The application relates to the technical field of computers, in particular to a vehicle positioning method, device and equipment.
Background
The vehicle can be positioned through a global navigation satellite system, and the requirement on the accuracy of vehicle positioning is higher and higher in some application scenes. For example, with the development of autonomous driving technology, if an autonomous vehicle can travel on a road, it is first necessary to solve the vehicle positioning problem. The vehicle positioning of the navigation software can only be positioned on a certain road generally, and the accuracy is far from enough for automatic driving. For another example, if a more accurate vehicle position can be provided when an accident occurs during daily travel, rescue can be facilitated and the accident scene can be quickly reached. Therefore, how to improve the accuracy of vehicle positioning is an urgent technical problem to be solved.
Disclosure of Invention
In view of this, embodiments of the present application provide a vehicle positioning method, apparatus, and device to improve accuracy of vehicle positioning.
In order to solve the above problem, the technical solution provided by the embodiment of the present application is as follows:
a vehicle localization method, the method comprising:
acquiring first positioning data of target equipment at the current moment, wherein the target equipment is arranged on a vehicle;
calculating second positioning data of the current moment from the first positioning data by using dead reckoning;
fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm, and calculating third positioning data of the current moment;
acquiring a high-precision map within the preset range of the third positioning data, wherein the high-precision map comprises lane information;
positioning the third positioning data on the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises lane information corresponding to the fourth positioning data;
acquiring visual data in front of the vehicle;
generating compensated third positioning data according to the visual data and the fourth positioning data;
and when the first positioning data of the target device at the current moment and the subsequent steps are executed next time, acquiring the compensated third positioning data as the first positioning data of the target device at the current moment.
In a possible implementation manner, the generating compensated third positioning data according to the visual data and the fourth positioning data includes:
determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from lane information included in the fourth positioning data, and acquiring a first lane boundary line color of the optimal lane and a lane boundary line color of the adjacent lane from the high-precision map;
identifying the color of a second lane boundary line of the lane where the vehicle is located from the visual data;
and if the color of the second lane boundary line is not consistent with that of the first lane boundary line and the color of the second lane boundary line is consistent with that of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
In a possible implementation manner, the generating compensated third positioning data according to the visual data and the fourth positioning data includes:
determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from lane information included in the fourth positioning data, and acquiring a first lane boundary line type of the optimal lane and a lane boundary line type of the adjacent lane from the high-precision map;
identifying a second lane boundary line type of the lane where the vehicle is located from the visual data;
and if the second lane boundary line type is not consistent with the first lane boundary line type and the second lane boundary line type is consistent with the lane boundary line type of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
In one possible implementation, after acquiring the visual data in front of the vehicle, the method further includes:
determining a first transverse distance between the vehicle and a lane boundary line where the vehicle is located according to the third positioning data;
identifying a second transverse distance from the visual data to a boundary line of a lane where the vehicle is located;
determining a difference between the first lateral distance and the second lateral distance as a lateral error;
and compensating the transverse error to the third positioning data to generate compensated third positioning data.
In one possible implementation, the method further includes:
acquiring radar data in front of the vehicle;
calculating a longitudinal error according to the radar data and the fourth positioning data;
and compensating the longitudinal error to the third positioning data to generate compensated third positioning data.
In a possible implementation manner, the calculating a longitudinal error according to the radar data and the fourth positioning data includes:
extracting a first longitudinal distance of the vehicle to a target object from the radar data;
acquiring a second longitudinal distance from the position of the fourth positioning data to the target object from the high-precision map;
determining a distance between the first longitudinal distance and the second longitudinal distance as a longitudinal error.
In a possible implementation manner, the positioning the third positioning data on the high-precision map to obtain fourth positioning data includes:
acquiring candidate lanes on the high-precision map;
projecting the third positioning data to the central line of the candidate lane to obtain projection points of the candidate lanes;
determining an optimal lane from the candidate lanes according to the deviation between the direction of the vehicle head in the third positioning data and the direction of the candidate lane and the distance between the position of the third positioning data and the projection point of the candidate lane;
and determining the projection point position of the third positioning data in the optimal lane as the position of fourth positioning data, and simultaneously adding lane information of the optimal lane into the fourth positioning data.
In one possible implementation, the method further includes:
calculating the total length of the target road section according to the high-precision map;
calculating a first accumulated traveling distance of the vehicle on the target road section according to the vehicle speed;
calculating a second accumulated traveling distance from the starting point of the target road section along the optimal lane to the position of the projection point of the third positioning data on the optimal lane;
calculating the first accumulated travelling distance to be divided by the total length to obtain a first vehicle travelling distance proportion, and obtaining a corresponding position of the optimal lane according to the first vehicle travelling distance proportion;
calculating a distance difference value between the second accumulated running distance and the first accumulated running distance;
and if the distance difference is larger than a distance threshold value, dividing the distance difference by the total length after adding the first accumulated running distance to obtain a second vehicle running distance proportion, and correcting the corresponding position of the optimal lane according to the second vehicle running distance proportion to obtain corrected fourth positioning data.
A vehicle locating device, the device comprising:
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring first positioning data of target equipment at the current moment, and the target equipment is arranged on a vehicle;
a first calculation unit configured to calculate second positioning data of the current time from the first positioning data by dead reckoning;
the second calculation unit is used for fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm and calculating third positioning data of the current moment;
the second acquisition unit is used for acquiring a high-precision map within a preset range of the third positioning data, and the high-precision map comprises lane information;
the positioning unit is used for positioning the third positioning data on the high-precision map to obtain fourth positioning data, and the fourth positioning data comprises lane information corresponding to the fourth positioning data;
a third acquisition unit configured to acquire visual data in front of the vehicle;
the first compensation unit is used for generating compensated third positioning data according to the visual data and the fourth positioning data;
and the circulating unit is used for acquiring the compensated third positioning data, serving as the first positioning data of the target device at the current moment, and returning to the first acquiring unit.
An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing a vehicle localization method as described above.
A computer-readable storage medium having stored therein instructions which, when run on a terminal device, cause the terminal device to perform a vehicle localization method as described above.
Therefore, the embodiment of the application has the following beneficial effects:
in the embodiment of the application, after the first positioning data of the target device on the vehicle at the initial moment is acquired, the second positioning data is calculated through dead reckoning. And fusing the first positioning data and the second positioning data through a Kalman filtering algorithm to obtain third positioning data, wherein the precision of the third positioning data is obviously higher than that of the second positioning data. And then acquiring a high-precision map within the preset range of the third positioning data, projecting the third positioning data onto the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises a current lane positioning result, so that lane-level positioning is realized, and the accuracy of vehicle positioning is improved. In addition, the third positioning data can be compensated based on the visual data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
Drawings
Fig. 1 is a schematic diagram of an exemplary application scenario provided in an embodiment of the present application;
FIG. 2 is a flowchart of a vehicle positioning method according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of yet another method for locating a vehicle according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying figures and detailed description thereof are described in further detail below.
In order to facilitate understanding of the technical solutions provided in the embodiments of the present application, the following description will first describe the background art related to the embodiments of the present application.
The positioning of the vehicle can be realized through a global navigation satellite system, and the vehicle can be positioned by Dead Reckoning (DR) at present. The dead reckoning working principle is that the initial positioning of a vehicle is obtained and used as the position of the vehicle at the previous moment, the speed and the angular speed of the vehicle are calculated, the deviation of the position of the vehicle at the previous moment is used, and the position deviation is accumulated to the positioning position of the vehicle at the previous moment, so that the position of the current vehicle is obtained. Since the dead reckoning is position information obtained by an accumulation method, an error of the position information is also accumulated. The error increases as the moving time and distance of the vehicle increases. The positioning precision is not high, and the method can be used for road level positioning and hardly realizes lane level positioning.
In some application scenarios, the requirement on the accuracy of vehicle positioning is higher and higher, and lane-level positioning needs to be realized. Based on this, the embodiment of the application provides a vehicle positioning method, device and equipment, after first positioning data of a target device on a vehicle at the initial moment is obtained, second positioning data is calculated through dead reckoning. And fusing the first positioning data and the second positioning data through a Kalman filtering algorithm to obtain third positioning data, wherein the precision of the third positioning data is obviously higher than that of the second positioning data. And then acquiring a high-precision map within the preset range of the third positioning data, projecting the third positioning data onto the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises a current lane positioning result, so that lane-level positioning is realized, and the accuracy of vehicle positioning is improved. In addition, the third positioning data can be compensated based on the visual data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
The lane-level positioning of the embodiment of the application can be applied to the following scenes:
1. general way
The high-precision map stores general road data (the general road data comprises road information such as road center line longitude and latitude, lane boundary line color, lane boundary line type and the like).
2. Expressway
The high-precision map stores highway data (the highway data comprises lane information such as road center line longitude and latitude, lane boundary line color, lane boundary line type and the like).
In order to facilitate understanding of the vehicle positioning method provided in the embodiment of the present application, the following description is made with reference to a scenario example shown in fig. 1. Referring to fig. 1, the figure is a schematic diagram of a framework of an exemplary application scenario provided in an embodiment of the present application. The method can be applied to target equipment arranged on a vehicle, and the target equipment can be terminal equipment such as a mobile phone and the like and can also be vehicle-mounted equipment such as vehicle-mounted navigation and the like.
First, first positioning data of the target device at the current time is obtained, and the first positioning data can be understood as initial positioning data to be corrected. And obtaining second positioning data from the first positioning data by dead reckoning. And fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm to obtain more accurate third positioning data. And positioning the third positioning data to a high-precision map of a corresponding position to obtain fourth positioning data comprising lane information, so that lane-level positioning is realized. In addition, the third positioning data can be compensated based on the visual data and the fourth positioning data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
Those skilled in the art will appreciate that the block diagram shown in fig. 1 is only one example in which embodiments of the present application may be implemented. The scope of applicability of the embodiments of the present application is not limited in any way by this framework.
Based on the above description, the vehicle positioning method provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Referring to fig. 2, which is a flowchart of a vehicle positioning method provided in an embodiment of the present application, as shown in fig. 2, the vehicle positioning method may include steps S201 to S208:
s201: the method comprises the steps of obtaining first positioning data of a target device at the current moment, wherein the target device is arranged on a vehicle.
First, first positioning data of the target device at the current time is acquired. Since the target device is disposed on the vehicle, the location for the target device may characterize the location of the vehicle. The first positioning data can be understood as initial positioning data. The first positioning data may include location latitude and longitude, vehicle heading direction, and the like.
In one possible scenario, the first positioning data may be positioning data of a GNSS (Global Navigation Satellite System) received by the target device. GNSS is a space-based radio navigation positioning system capable of providing users with all-weather three-dimensional coordinates and speed and time information at any place on the earth's surface or in the near-earth space, including but not limited to Global Positioning System (GPS) in the united states, GLONASS satellite navigation system (GLONASS) in russia, GALILEO satellite navigation system (GALILEO) in the european union, and beidou satellite navigation system (BDS) in china, etc.
In another possible scenario, if the positioning data of the GNSS cannot be received, the first positioning data may be the positioning data of the last time the target device is powered on, and after the positioning is successful, the target device is powered off. For example, in a scenario such as an underground parking lot where positioning data of GNSS cannot be received, the first positioning data may use the positioning data that is retained when the target device was last powered off.
In yet another possible scenario, the first positioning data may also be the third positioning data compensated at the previous time. After the first positioning data is obtained each time, the second positioning data can be obtained according to the first positioning data, and then the third positioning data with higher precision can be obtained. And the third positioning data can be continuously corrected to obtain compensated third positioning data. The compensated third positioning data may be used as the first positioning data at the next time. For a detailed description of this scenario, reference may be made to the following embodiments.
After the first positioning data is obtained, more accurate positioning data can be calculated according to the first positioning data.
S202: and calculating second positioning data of the current moment from the first positioning data by using dead reckoning.
The second positioning data is positioning data for dead reckoning using information such as the vehicle sensor system and the vehicle speed received by the target device. Specifically, the deviation from the first positioning data may be calculated using the vehicle speed and the angular velocity, and the positional deviation may be accumulated to obtain the second positioning data. Vehicle speed is derived from a wheel speed sensor and angular velocity is derived from an IMU (Inertial Measurement Unit). The wheel speed sensor is a sensor for measuring the rotation speed of the wheels of the automobile, and the IMU is a sensor mainly used for measuring the acceleration and the angular speed of the automobile.
S203: and fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm to calculate third positioning data at the current moment.
And fusing the first positioning data and the second positioning data by using a Kalman (Kalman) filtering algorithm to calculate third positioning data of the current moment. The positioning accuracy of the third positioning data is higher than the accuracy of both the first positioning data and the second positioning data.
In practical applications, the kalman filter model is as follows:
wherein the content of the first and second substances,
is the second positioning data, and the second positioning data,
the second positioning data at the previous moment comprise vehicle sensor parameters such as gyroscope zero offset, gyroscope correction factors and vehicle speed correction factors, the direction of the vehicle head and the longitude and latitude of the vehicle position of dead reckoning.
A is
Bu of k-1 Can be considered to be 0.
P k-1 Is the error covariance matrix of the previous time instant, Q is the predicted noise covariance matrix, H T As a transpose of the transfer matrix, K k For Kalman gain, R is the measurement noise covariance matrix.
z k The first positioning data comprises longitude and latitude, speed, vehicle head direction and the like, and can also be the longitude and latitude, the vehicle head direction and the like after map matching feedback compensation at the last moment.
I. H is a unit matrix, P k Is an error covariance matrix corrected by the observed quantity.
The third positioning data with higher precision can be obtained through the Kalman filtering model.
S204: and acquiring a high-precision map within a preset range of the third positioning data, wherein the high-precision map comprises lane information.
According to the position of the third positioning data, a high-precision map around the position of the third positioning data can be obtained. The high-precision map is an electronic map with higher precision and more data dimensions, the higher precision is realized by being accurate to a centimeter level, and the data dimensions are more embodied by including lane information and surrounding static information related to traffic, such as traffic sign information and the like.
In practical application, the position of the third positioning data can be used as the center, the fixed distance can be used as the radius to draw a circle, the inscribed rectangle of the circle is used as the preset range, the high-precision map in the rectangle can be obtained, and the rectangle frame can be updated along with the updating of the position. It can be understood that, the embodiment of the present application does not limit how to determine the preset range, and the preset range may be set according to actual situations.
S205: and positioning the third positioning data on a high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises lane information corresponding to the fourth positioning data.
The high-precision map within the preset range of the third positioning data may include one or more candidate lanes, the third positioning data are projected onto center lines of the candidate lanes respectively, the projection adopts a mode that a point is perpendicular to a straight line, and the perpendicular point is the projection point.
The optimal lane may be determined from the candidate lanes according to the projected point of the third positioning data, for example, the lane direction may be consistent with the direction of the vehicle head in the third positioning data, and the candidate lane corresponding to the position of the third positioning data closest to the projected point may be determined as the optimal lane. The projection point position of the third positioning data on the optimal lane is determined as the position of the fourth positioning data, and the fourth positioning data further includes lane information corresponding to the fourth positioning data, for example, the lane information of the optimal lane. That is, the fourth positioning data is lane-level positioning data, and lane-level positioning is realized. The fourth positioning data may include the lane line, the longitude and latitude, the direction of the vehicle head, and the like. In addition, in some possible implementations, the fourth positioning data may also include projection point positions of the third positioning data in other candidate lanes and lane information of the candidate lanes.
S206: visual data is acquired in front of the vehicle.
In order to further improve the vehicle positioning accuracy, after the fourth positioning data is obtained, the third positioning data can be compensated according to the fourth positioning data and the visual data, so that the compensated third positioning data enters the next positioning calculation, and the accuracy of the next fourth positioning data is higher.
In this embodiment, the visual data in front of the vehicle may be collected by the target device, or may be collected by other devices, such as a driving recorder. The visual data can acquire the contents of the lane where the vehicle is located, the lane color and the line type of the adjacent lane, the distance from the vehicle to the lane boundary and the like.
S207: and generating compensated third positioning data according to the visual data and the fourth positioning data.
And using the content in the visual data to determine whether the lane positioned by the vehicle is accurate or not, and if not, compensating the third positioning data to generate the compensated third positioning data. If the lane positioning is accurate, the lateral error of the fourth positioning data can also be obtained using the content in the visual data, for example, the lateral error can be calculated from the vehicle-to-lane boundary line distance and the fourth positioning data. And compensating the third positioning data by utilizing the transverse error to generate compensated third positioning data. So that the third positioning data after compensation has higher accuracy.
Specific implementation of generating the compensated third positioning data according to the visual data and the fourth positioning data can be seen in the following embodiments.
S208: and acquiring the compensated third positioning data as the first positioning data of the target device at the current moment, and returning to the step S201.
When the first positioning data and the subsequent steps of the target device at the current moment are obtained next time, the compensated third positioning data can be obtained and used as the first positioning data of the target device at the current moment, the compensated third positioning data enters next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
Based on the contents of S201 to S208, after the first positioning data of the target device on the vehicle at the current time is acquired, the second positioning data is calculated by dead reckoning. And fusing the first positioning data and the second positioning data through a Kalman filtering algorithm to obtain third positioning data, wherein the precision of the third positioning data is obviously higher than that of the second positioning data. And then acquiring a high-precision map within the preset range of the third positioning data, projecting the third positioning data onto the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises a current lane positioning result, so that lane-level positioning is realized, and the accuracy of vehicle positioning is improved. In addition, the third positioning data can be compensated based on the visual data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
In a possible implementation manner, the S207, according to the visual data and the fourth positioning data, generating the compensated third positioning data specifically may include a 1-A3:
a1: and determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from the lane information included in the fourth positioning data, and acquiring a first lane boundary line color of the optimal lane and a lane boundary line color of the adjacent lane from the high-precision map.
A2: the second lane boundary line color of the lane in which the vehicle is located is identified from the visual data.
The fourth positioning data includes related information of an optimal lane corresponding to the vehicle, and then the lane boundary line color (i.e., the first lane boundary line color) of the lane, such as yellow, white, etc., can be obtained from the high-precision map. The fourth positioning data also comprises related information of adjacent lanes within a preset range from the optimal lane, and the lane boundary line colors of the adjacent lanes can be obtained from the high-precision map.
Meanwhile, the color of the lane boundary line of the current vehicle in the lane (namely, the color of the second lane boundary line) can be identified from the visual data, and whether the current lane is positioned accurately can be determined by comparing whether the color of the lane boundary line of the current vehicle in the lane is consistent with the color of the second lane boundary line.
A3: and if the color of the second lane boundary line is not consistent with that of the first lane boundary line and the color of the second lane boundary line is consistent with that of the lane boundary line of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
If the color of the first lane boundary line is inconsistent with the color of the second lane boundary line, which represents that the current lane is inaccurately positioned, the lane where the current vehicle is located can be found again by comparing the colors of the lane boundary lines of the adjacent lanes, and the vehicle is positioned to the center line position of the lane, so as to generate the compensated third positioning data.
In a possible implementation manner, the S207, according to the visual data and the fourth positioning data, generating compensated third positioning data specifically may include B1-B3:
b1: and determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from the lane information included in the fourth positioning data, and acquiring a first lane boundary line type of the optimal lane and a lane boundary line type of the adjacent lane from the high-precision map.
B2: a second lane boundary line type of the lane in which the vehicle is located is identified from the visual data.
Similarly, if the fourth positioning data includes information related to the optimal lane corresponding to the vehicle, a lane boundary line type (i.e., a first lane boundary line type) of the lane may be obtained from the high-precision map, such as a dashed line, a solid line, and the like. The fourth positioning data also comprises related information of adjacent lanes within a preset range from the optimal lane, and lane boundary line types of the adjacent lanes can be obtained from the high-precision map.
Meanwhile, the lane boundary line type (namely, the second lane boundary line type) of the lane where the current vehicle is located can be identified from the visual data, and whether the current lane is positioned accurately can be determined by comparing whether the two types of lane boundary line types are consistent.
B3: and if the second lane boundary line is not consistent with the first lane boundary line and the second lane boundary line is consistent with the lane boundary line of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
If the first lane boundary line type is inconsistent with the second lane boundary line type and represents that the current lane is inaccurately positioned, the lane where the current vehicle is located can be found again by comparing the lane boundary line types of the adjacent lanes, and the vehicle is positioned to the center line position of the lane, so as to generate the compensated third positioning data.
In one possible implementation, after acquiring the visual data in front of the vehicle at S206, C1-C4 may be further included:
c1: and determining a first transverse distance between the vehicle and the boundary line of the lane where the vehicle is located according to the third positioning data.
C2: a second lateral distance of the vehicle from a boundary line of the lane is identified from the visual data.
C3: the difference between the first lateral distance and the second lateral distance is determined as the lateral error.
C4: and compensating the transverse error to the third positioning data to generate compensated third positioning data.
And calculating a first transverse distance from the third positioning data to the lane boundary line, identifying a second transverse distance from the vehicle to the lane boundary line through visual data, and compensating the third positioning data by using a difference value between the first transverse distance and the second transverse distance as a transverse error, so that the compensated third positioning data has higher accuracy.
Referring to fig. 3, based on the above embodiment corresponding to fig. 2, the lane positioning method provided in the embodiment of the present application may further include S301 to S303:
s301: radar data in front of the vehicle is acquired.
Radar data, such as millimeter wave radar point cloud data, is collected by a radar device in front of the vehicle. The radar data may be collected as the longitudinal distance of the radar to a static object, such as a guideboard or the like.
S302: and calculating a longitudinal error according to the radar data and the fourth positioning data.
Using the radar data, a longitudinal error of the fourth positioning data may be obtained. For example, the longitudinal distance between the current position and the guideboard in the high-precision map is calculated, and the longitudinal error of the vehicle is calculated by comparing the longitudinal distance between the radar and the guideboard.
Then, in a possible implementation, calculating the longitudinal error according to the radar data and the fourth positioning data may specifically include D1-D3:
d1: a first longitudinal distance of the vehicle to the target object is extracted from the radar data.
D2: and acquiring a second longitudinal distance from the position of the fourth positioning data to the target object from the high-precision map.
D3: the distance between the first longitudinal distance and the second longitudinal distance is determined as the longitudinal error.
A first longitudinal distance from the vehicle to a target object is extracted from the radar data, and a second longitudinal distance from the position of the fourth positioning data to the target object can be acquired from the high-precision map. The distance between the two is then the longitudinal error.
S303: and compensating the longitudinal error to third positioning data to generate compensated third positioning data.
The longitudinal error is compensated to the third positioning data so that the third positioning data with longitudinal error compensation is more accurate.
Based on the above-described embodiment, the compensation of the third positioning data can also be achieved using radar data. In a possible implementation manner, when the next step of obtaining the first positioning data of the target device at the current time and the subsequent steps are executed, the compensated third positioning data may be obtained as the first positioning data of the target device at the current time.
Through visual data and/or radar data can promote the positioning data accuracy, horizontal error and/or vertical error compensation give the third positioning data, and the third positioning data after the compensation gets into next positioning calculation, and the fourth positioning data accuracy of next that obtains is also higher.
In one possible implementation manner, S205 locates the third positioning data on the high-precision map, and obtaining the fourth positioning data may include E1-E4:
e1: and acquiring candidate lanes on the high-precision map.
E2: and projecting the third positioning data to the central line of the candidate lane to obtain the projection point of each candidate lane.
The high-precision map within the preset range of the third positioning data may include one or more candidate lanes, and the candidate lanes may be understood as lanes where vehicles around the position of the third positioning data may travel. And respectively projecting the third positioning data to the central line of the candidate lane, wherein the projection adopts a mode of making a perpendicular line from a point to a straight line, and the perpendicular on the central line of the candidate lane is just the projection point of the candidate lane.
E3: and determining the optimal lane from the candidate lanes according to the deviation between the direction of the vehicle head in the third positioning data and the direction of the candidate lane and the distance between the position of the third positioning data and the projection point of the candidate lane.
Since the lane closer to the position of the third positioning data may be the lane opposite to the lane where the vehicle is located, the optimal lane where the vehicle is most likely to travel needs to be estimated from both the deviation between the direction of the vehicle head and the direction of the candidate lane and the distance between the position of the third positioning data and the projection point of the candidate lane.
E4: and determining the projection point position of the third positioning data in the optimal lane as the position of the fourth positioning data, and simultaneously adding the lane information of the optimal lane into the fourth positioning data.
After the optimal lane is determined, the projection point position of the third positioning data on the optimal lane can be used as the position of the fourth positioning data, and meanwhile, the fourth positioning data also comprises lane information of the optimal lane. In addition, the fourth positioning data may also include projection point positions of the third positioning data on other candidate lanes and lane information of the candidate lanes. Thereby, the lane-level positioning is realized, and the accuracy of vehicle positioning is improved.
The fourth positioning data may be further modified, and the vehicle positioning method provided in the embodiment of the present application further includes:
calculating the total length of the target road section according to the high-precision map;
calculating a first accumulated traveling distance of the vehicle on the target road section according to the vehicle speed;
calculating a second accumulated traveling distance from the starting point of the target road section along the optimal lane to the projection point position of the third positioning data on the optimal lane;
calculating the first accumulated traveling distance divided by the total length to obtain a first vehicle traveling distance proportion, and obtaining a corresponding position in the optimal lane according to the first vehicle traveling distance proportion;
calculating a distance difference value between the second accumulated traveling distance and the first accumulated traveling distance;
and if the distance difference is larger than the distance threshold, the sum of the first accumulated running distance and the distance difference is divided by the total length to obtain a second vehicle running distance proportion, and the corresponding position of the optimal lane is corrected according to the second vehicle running distance proportion to obtain corrected fourth positioning data.
In a high-precision map, a road may be divided into a plurality of segments, for example, a 10 km road, and divided into 5 2 km segments. And calculating the total length of each road section according to the latitude and longitude stored in the high-precision map. The target device can calculate a first accumulated travel distance of the vehicle on a road section corresponding to the current position (namely, a target road section) according to the related information such as the vehicle speed.
Meanwhile, a second accumulated traveling distance of the projection point on the target road section is calculated according to the projection point of the third positioning data on the high-precision map, namely, the second accumulated traveling distance is obtained by advancing to the projection point position of the optimal lane of the third positioning data along the central line of the optimal lane from the starting point of the target road section. The first vehicle distance ratio is a first cumulative distance traveled/total length, from which a vehicle position in the target road section can be determined. And then calculating the second accumulated running distance to the first accumulated running distance to obtain a distance difference value, and correcting the first accumulated running distance by using the second accumulated running distance when the distance difference value is greater than the distance threshold value. That is, the second vehicle travel distance ratio (the first accumulated travel distance + the distance difference)/the total length is calculated, and a vehicle position on the target road segment can be retrieved according to the ratio and used as the corrected fourth positioning data, so that the vehicle position is more accurate.
Based on the vehicle positioning method provided by the method embodiment, the embodiment of the application also provides a vehicle positioning device, and the vehicle positioning device is described with reference to the accompanying drawings.
Referring to fig. 4, the figure is a schematic structural diagram of a vehicle positioning device provided in an embodiment of the present application. As shown in fig. 4, the vehicle positioning apparatus includes:
a first obtaining unit 401, configured to obtain first positioning data of a target device at a current time, where the target device is disposed on a vehicle;
a first calculation unit 402 configured to calculate second positioning data of the current time from the first positioning data by dead reckoning;
a second calculating unit 403, configured to fuse the first positioning data and the second positioning data by using a kalman filter algorithm, and calculate third positioning data of the current time;
a second obtaining unit 404, configured to obtain a high-precision map within a preset range of the third positioning data, where the high-precision map includes lane information;
a positioning unit 405, configured to position the third positioning data on the high-precision map to obtain fourth positioning data, where the fourth positioning data includes lane information corresponding to the fourth positioning data;
a third acquiring unit 406, configured to acquire visual data in front of the vehicle;
a first compensation unit 407, configured to generate compensated third positioning data according to the visual data and the fourth positioning data;
the circulation unit 408 is configured to acquire the compensated third positioning data, as the first positioning data of the target device at the current time, and return to the first acquisition unit.
In a possible implementation manner, the first compensation unit includes:
a first determining subunit, configured to determine, from lane information included in the fourth positioning data, an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane, and acquire, from the high-precision map, a first lane boundary line color of the optimal lane and a lane boundary line color of the adjacent lane;
the first identification subunit is used for identifying the color of a second lane boundary line of the lane where the vehicle is located from the visual data;
and the first generating subunit is configured to generate compensated third positioning data according to the center line position of the adjacent lane if the color of the second lane boundary line is inconsistent with the color of the first lane boundary line and the color of the second lane boundary line is consistent with the color of the lane boundary line of the adjacent lane.
In a possible implementation manner, the first compensation unit includes:
a second determining subunit, configured to determine, from lane information included in the fourth positioning data, an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane, and acquire, from the high-precision map, a first lane boundary line type of the optimal lane and a lane boundary line type of the adjacent lane;
the second identification subunit is used for identifying a second lane boundary line type of the lane where the vehicle is located from the visual data;
and the second generating subunit is used for generating compensated third positioning data according to the center line position of the adjacent lane if the second lane boundary line type is not consistent with the first lane boundary line type and the second lane boundary line type is consistent with the lane boundary line type of the adjacent lane in the high-precision map.
In a possible implementation manner, the apparatus further includes a second compensation unit, and the second compensation unit includes:
a third determining subunit, configured to determine, according to the third positioning data, a first lateral distance between the vehicle and a lane boundary line where the vehicle is located;
a third identifying subunit, configured to identify, from the visual data, a second lateral distance from the vehicle to a lane boundary line where the vehicle is located;
a fourth determining subunit, configured to determine a difference between the first lateral distance and the second lateral distance as a lateral error;
and the third generating subunit is used for compensating the lateral error to the third positioning data and generating the compensated third positioning data.
In one possible implementation, the apparatus further includes:
a fourth acquisition unit configured to acquire radar data ahead of the vehicle;
a third calculation unit, configured to calculate a longitudinal error according to the radar data and the fourth positioning data;
and the third compensation unit is used for compensating the longitudinal error to the third positioning data and generating the compensated third positioning data.
In one possible implementation manner, the third computing unit includes:
an extraction subunit configured to extract a first longitudinal distance of the vehicle to a target object from the radar data;
a first acquiring subunit, configured to acquire a second longitudinal distance from the position of the fourth positioning data to the target object from the high-precision map;
a fifth determining subunit, configured to determine a distance between the first longitudinal distance and the second longitudinal distance as a longitudinal error.
In one possible implementation manner, the positioning unit includes:
the second acquisition subunit is used for acquiring the candidate lanes on the high-precision map;
the projection subunit is configured to project the third positioning data onto a center line of the candidate lane, so as to obtain projection points of each candidate lane;
a sixth determining subunit, configured to determine an optimal lane from the candidate lanes according to a deviation between a vehicle head direction in the third positioning data and a direction of the candidate lane and a distance between a position of the third positioning data and a projection point of the candidate lane;
a seventh determining subunit, configured to determine the projection point position of the third positioning data on the optimal lane as the position of fourth positioning data, and add the lane information of the optimal lane to the fourth positioning data.
In one possible implementation, the apparatus further includes:
the fourth calculating unit is used for calculating the total length of the target road section according to the high-precision map;
a fifth calculation unit, configured to calculate a first accumulated travel distance of the vehicle on the target road segment according to a vehicle speed;
a sixth calculating unit, configured to calculate a second accumulated traveling distance from the starting point of the target road segment along the optimal lane to the projection point position of the third positioning data in the optimal lane;
the seventh calculation unit is used for calculating the first accumulated travelling distance to be divided by the total length to obtain a first vehicle travelling distance proportion, and obtaining a corresponding position of the optimal lane according to the first vehicle travelling distance proportion;
an eighth calculation unit configured to calculate a distance difference between the second accumulated running distance and the first accumulated running distance;
and the correction unit is used for obtaining a second vehicle running distance proportion by dividing the distance difference value added to the first accumulated running distance by the total length if the distance difference value is larger than a distance threshold value, and correcting the corresponding position of the optimal lane according to the second vehicle running distance proportion to obtain corrected fourth positioning data.
In addition, an embodiment of the present application further provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing a vehicle localization method as described above.
In addition, the embodiment of the application also provides a computer-readable storage medium, and the computer-readable storage medium stores instructions, and when the instructions are run on the terminal device, the terminal device is caused to execute the vehicle positioning method.
The embodiment of the application provides a vehicle positioning device and equipment, and after first positioning data of target equipment on a vehicle at the initial moment is obtained, second positioning data are calculated through dead reckoning. And fusing the first positioning data and the second positioning data through a Kalman filtering algorithm to obtain third positioning data, wherein the precision of the third positioning data is obviously higher than that of the second positioning data. And then acquiring a high-precision map within the preset range of the third positioning data, projecting the third positioning data onto the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises a current lane positioning result, so that lane-level positioning is realized, and the accuracy of vehicle positioning is improved. In addition, the third positioning data can be compensated based on the visual data, the compensated third positioning data enters the next positioning calculation, and the accuracy of the obtained next fourth positioning data is higher.
It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system or the device disclosed by the embodiment, the description is simple because the system or the device corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" is used to describe the association relationship of the associated object, indicating that there may be three relationships, for example, "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c may be single or plural.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (11)
1. A vehicle positioning method, characterized in that the method comprises:
acquiring first positioning data of target equipment at the current moment, wherein the target equipment is arranged on a vehicle;
calculating second positioning data of the current time from the first positioning data by dead reckoning;
fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm, and calculating third positioning data of the current moment;
acquiring a high-precision map within the preset range of the third positioning data, wherein the high-precision map comprises lane information;
positioning the third positioning data on the high-precision map to obtain fourth positioning data, wherein the fourth positioning data comprises lane information corresponding to the fourth positioning data;
acquiring visual data in front of the vehicle;
generating compensated third positioning data according to the visual data and the fourth positioning data;
and when the first positioning data of the target device at the current moment and subsequent steps are executed next time, acquiring the compensated third positioning data as the first positioning data of the target device at the current moment.
2. The method of claim 1, wherein generating compensated third positioning data based on the visual data and the fourth positioning data comprises:
determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from lane information included in the fourth positioning data, and acquiring a first lane boundary line color of the optimal lane and a lane boundary line color of the adjacent lane from the high-precision map;
identifying the color of a second lane boundary line of the lane where the vehicle is located from the visual data;
and if the color of the second lane boundary line is not consistent with that of the first lane boundary line and the color of the second lane boundary line is consistent with that of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
3. The method of claim 1, wherein generating compensated third positioning data based on the visual data and the fourth positioning data comprises:
determining an optimal lane corresponding to the fourth positioning data and an adjacent lane corresponding to the optimal lane from lane information included in the fourth positioning data, and acquiring a first lane boundary line type of the optimal lane and a lane boundary line type of the adjacent lane from the high-precision map;
identifying a second lane boundary line type of the lane where the vehicle is located from the visual data;
and if the second lane boundary line type is not consistent with the first lane boundary line type and the second lane boundary line type is consistent with the lane boundary line type of the adjacent lane, generating compensated third positioning data according to the center line position of the adjacent lane.
4. The method of claim 1, wherein after acquiring the visual data forward of the vehicle, the method further comprises:
determining a first transverse distance between the vehicle and a lane boundary line where the vehicle is located according to the third positioning data;
identifying a second transverse distance from the visual data to a boundary line of a lane where the vehicle is located;
determining a difference between the first lateral distance and the second lateral distance as a lateral error;
and compensating the transverse error to the third positioning data to generate compensated third positioning data.
5. The method of claim 1, further comprising:
acquiring radar data in front of the vehicle;
calculating a longitudinal error according to the radar data and the fourth positioning data;
and compensating the longitudinal error to the third positioning data to generate compensated third positioning data.
6. The method of claim 5, wherein calculating a longitudinal error based on the radar data and the fourth positioning data comprises:
extracting a first longitudinal distance of the vehicle to a target object from the radar data;
acquiring a second longitudinal distance from the position of the fourth positioning data to the target object from the high-precision map;
determining a distance between the first longitudinal distance and the second longitudinal distance as a longitudinal error.
7. The method of claim 1, wherein said positioning the third positioning data on the high-precision map to obtain fourth positioning data comprises:
acquiring a candidate lane on the high-precision map;
projecting the third positioning data to the central line of the candidate lane to obtain the projection point of each candidate lane;
determining an optimal lane from the candidate lanes according to the deviation between the direction of the vehicle head in the third positioning data and the direction of the candidate lane and the distance between the position of the third positioning data and the projection point of the candidate lane;
and determining the projection point position of the third positioning data in the optimal lane as the position of fourth positioning data, and simultaneously adding lane information of the optimal lane into the fourth positioning data.
8. The method of claim 7, further comprising:
calculating the total length of a target road section according to the high-precision map;
calculating a first accumulated traveling distance of the vehicle on the target road section according to the vehicle speed;
calculating a second accumulated traveling distance from the starting point of the target road section along the optimal lane to the position of the projection point of the third positioning data on the optimal lane;
calculating the first accumulated travelling distance to be divided by the total length to obtain a first vehicle travelling distance proportion, and obtaining a corresponding position of the optimal lane according to the first vehicle travelling distance proportion;
calculating a distance difference between the second accumulated running distance and the first accumulated running distance;
and if the distance difference is larger than a distance threshold value, dividing the distance difference by the total length after adding the first accumulated running distance to obtain a second vehicle running distance proportion, and correcting the corresponding position of the optimal lane according to the second vehicle running distance proportion to obtain corrected fourth positioning data.
9. A vehicle locating apparatus, characterized in that the apparatus comprises:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring first positioning data of target equipment at the current moment, and the target equipment is arranged on a vehicle;
a first calculation unit configured to calculate second positioning data of the current time from the first positioning data by dead reckoning;
the second calculation unit is used for fusing the first positioning data and the second positioning data by using a Kalman filtering algorithm and calculating third positioning data of the current moment;
the second acquisition unit is used for acquiring a high-precision map within the preset range of the third positioning data, and the high-precision map comprises lane information;
the positioning unit is used for positioning the third positioning data on the high-precision map to obtain fourth positioning data, and the fourth positioning data comprises lane information corresponding to the fourth positioning data;
a third acquisition unit configured to acquire visual data in front of the vehicle;
the first compensation unit is used for generating compensated third positioning data according to the visual data and the fourth positioning data;
and the circulating unit is used for acquiring the compensated third positioning data, serving as the first positioning data of the target device at the current moment, and returning to the first acquiring unit.
10. An electronic device, comprising: memory, a processor, and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing a vehicle localization method as claimed in any one of claims 1-8.
11. A computer-readable storage medium having stored therein instructions that, when run on a terminal device, cause the terminal device to perform the vehicle positioning method according to any one of claims 1-8.
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