A radar-based method for identifying front target vehicles in driver assistance systems

Abstract

The present invention discloses a method for identifying a target vehicle ahead of an assisted driving system based on radar, comprising the following steps: estimating the state of the vehicle; estimating the state of the target vehicle; and calculating the lateral offset of the target vehicle relative to the center line of the expected driving trajectory of the vehicle. The present invention uses a target identification method that estimates the vehicle state and the expected road driving trajectory of the vehicle through Kalman filtering, thereby improving the reliability of the entire assisted driving system from the environmental perception level; the entire target vehicle identification process is simple and efficient, with fewer participating link units and strong adaptability to lighting conditions and weather conditions; by making full use of information observed by the radar, such as the distance and azimuth of the target, the state of the target vehicle is estimated, including the lateral and longitudinal speeds and the yaw angular velocity, which can effectively reduce false alarms of stationary objects on the roadside, such as trees in green belts and road traffic signs, thereby enabling the normal operation of the assisted driving function.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle assisted driving, and in particular to a method for identifying a front target vehicle in an assisted driving system based on radar. Background Art

[0002] With the development of economy and the improvement of people's living standards, the number of cars is increasing day by day. Effectively increasing the comfort and safety of car driving is considered to be the development trend of the future automobile industry. Advanced Driving Assistant System (ADAS) is an active safety function integrated control system that uses radar, camera and other sensors to collect data about the surrounding environment of the car, identify and track static and dynamic objects, and analyze the data to make behavioral decisions, so that the driver or the self-driving vehicle can be aware of possible dangers and directly control the vehicle to avoid collision when necessary, which can effectively improve driving safety and comfort.

[0003] It is generally believed that the broad sense of advanced driver assistance systems includes navigation and real-time traffic, electronic police, car networking, adaptive cruise control, lane departure warning, lane keeping, forward collision warning, automatic emergency braking, night vision, adaptive lighting control, pedestrian protection, automatic parking, blind spot monitoring, driver fatigue monitoring and other main functional modules. From the above definition, it can be seen that the environmental perception ability of the car, especially the target detection and tracking ability, is particularly important for its realization of advanced driver assistance functions (especially adaptive cruise control, forward collision warning, automatic emergency braking, etc.). In existing research and applications, the most common target recognition technologies include two types: target recognition technology based on visual systems, which analyzes the images taken by the camera to determine the trajectory and position of the vehicle in front, and obtains the relative position and speed of the two vehicles through image algorithms; target recognition methods based on radar systems determine key state parameters such as the relative speed and relative position of surrounding vehicles through the characteristics of radar receiving waves, and further limit the state parameters to select the targets required for the auxiliary driving function and identify and track them.

[0004] The target recognition method based on the visual system can match and classify the objects observed by the camera through image feature extraction to detect and filter out specific types of targets required by the driving assistance function (vehicles and pedestrians in the lane in most scenarios). This method has certain disadvantages. It is easily affected by lighting conditions and weather conditions, and the measurement distance is short. It cannot meet the functional requirements when the vehicle speed is high. At present, the target recognition technology based on millimeter-wave radar can independently and continuously observe the distance and azimuth of the car under normal circumstances, and can continuously track multiple target vehicles at the same time, constantly update the target position and azimuth information, and provide data support for the decision-making planning of the assisted driving system. However, under complex working conditions such as straight road into curve, curve driving, curve into straight road, etc., the vehicle will suddenly accelerate because the driving assistance system cannot identify the target in front due to the limitation of the radar's own target detection principle, which seriously threatens the safety of the vehicle. On the other hand, the simple radar-based target recognition technology cannot distinguish the attribute characteristics of the target, and often misreports trees, traffic signs, buildings, etc. on both sides of the road as vehicles or pedestrians in the lane, which seriously affects driving comfort and greatly reduces road traffic volume.

[0005] The performance of the assisted driving system depends to a large extent on the accuracy of target recognition. Although the traditional target discrimination method based purely on millimeter-wave radar can achieve accurate target recognition under general working conditions, the actual road conditions are complex and changeable. Incorrect target recognition will not only seriously affect the realization of related functions of the assisted driving system, but may also pose a serious threat to vehicle driving safety.

[0006] Therefore, it is necessary to provide a radar-based method for identifying a target vehicle ahead of an assisted driving system to solve the above problems. Summary of the invention

[0007] In view of the deficiencies in the prior art, an embodiment of the present invention aims to provide a method for identifying a target vehicle ahead of a driver assistance system based on radar.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for identifying a target vehicle ahead of a driver assistance system based on radar, comprising the following steps:

[0009] Step 1, vehicle state estimation: read specific vehicle state parameter values ​​from the vehicle body controller, establish a three-degree-of-freedom vehicle model, input the vehicle motion state parameters, and then select the required yaw rate and center-of-mass sideslip angle from the vehicle state parameter values ​​output by the three-degree-of-freedom vehicle model, and finally perform Kalman filtering on the yaw rate and center-of-mass sideslip angle to calculate the estimated values ​​of the yaw rate and center-of-mass sideslip angle and estimate the instantaneous curvature of the current position of the vehicle;

[0010] Step 2: Target vehicle state estimation: Use radar to measure the motion information data of the target vehicle, and use the motion information data of the target vehicle to perform vehicle motion compensation; then, establish a target state estimation filter and use Kalman filtering to estimate the motion state information of the target vehicle, calculate the current road curvature of the target vehicle based on the motion state information of the target vehicle, and use the longitudinal velocity v of the target vehicle to calculate the target vehicle state. yi and the lateral velocity v xi According to the formula ψ=arctan(v yi / v xi ) Calculate the heading angle of the target vehicle;

[0011] Step 3. Calculate the lateral offset of the target vehicle relative to the center line of the expected driving trajectory of the vehicle: Calculate the lateral offset of the target vehicle relative to the center line of the future expected driving trajectory of the vehicle after the path angle correction, and determine whether the target vehicle in front is currently in the lane or in the adjacent lane: If the lateral offset is greater than or equal to half the lane width, the target in front is not in the lane; if the lateral offset is less than half the lane width and is closest to the vehicle, the target is a valid target in the assisted driving system.

[0012] As a further solution of the present invention, in step one, reading the specific vehicle body state parameter value from the vehicle body controller is to read the front wheel steering angle value and the motor torque value of the vehicle.

[0013] As a further solution of the present invention, in step 2, the motion information data of the target vehicle measured by the radar of the own vehicle includes the relative speed, relative distance and azimuth of the target vehicle.

[0014] As a further solution of the present invention, in step 2, the step of the vehicle motion compensation is: decomposing the vehicle motion compensation into rotation compensation and translation compensation, performing rotation compensation first and then translation compensation.

[0015] As a further solution of the present invention, the specific execution steps are: converting the data observed by the radar at time k to the vehicle coordinate system at time k+1, and the coordinates of the target vehicle in the vehicle coordinate system at time k are (x k ,y k ), the longitudinal velocity of the vehicle is v y , the lateral velocity is v x , the yaw rate is ω, and the angle rotated in a sampling period T is φ = ωT. The vehicle motion is compensated for rotation and translation respectively. In one period, the coordinates after rotation compensation are (x k '=xcosφ+ysinφ,y k '-xsinφ+ycosφ), then perform translation compensation to convert the coordinates of the target vehicle to (x k+1 =xk '-v x T,y k+1 =y k '-v y T). In step 2, the step of using the target state estimation filter Kalman filter is: inputting the target vehicle coordinates and longitudinal and lateral velocities after the motion compensation of the vehicle into the target state estimation filter as input values, obtaining the target vehicle coordinates and longitudinal and lateral velocities estimation values ​​of the target state estimation filter through calculation, and after obtaining the longitudinal and lateral velocities estimation values ​​of the target vehicle, calculating the heading angle of the target vehicle.

[0016] As a further solution of the present invention, the specific steps of calculating the heading angle of the target vehicle are: using the longitudinal velocity v of the target vehicle yi and the lateral velocity v xi According to the formula ψ=arctan(v yi / v xi )calculate.

[0017] As a further solution of the present invention, in step three, the following steps are included:

[0018] Road curvature parameter calculation: The yaw rate and longitudinal velocity of the vehicle at the current moment can be obtained by the vehicle state estimation step. According to the formula c(x i )=φ i / v xi Calculate the vehicle's position at X i The road curvature at the location, where φ i is the yaw angular velocity, v xi represents the longitudinal velocity, taking φ at different times i and v xi Then we can get c(x i ), and then determine the road curvature model parameters of the lane where the vehicle is located;

[0019] Path angle compensation: When determining the expected future driving trajectory of the vehicle, appropriate corrections need to be made to rotate the original expected driving trajectory by η, where η is the path angle of the vehicle at the current moment.

[0020] The lateral offset of the target vehicle is calculated. The difference between the lateral coordinate of the target vehicle at the current moment and the lateral coordinate of the center line of the expected future driving trajectory of the vehicle is the lateral offset of the target vehicle.

[0021] As a further solution of the present invention, the road curvature parameter is calculated specifically as follows: the road model adopts a clothoid curve model, the mathematical formula of the model is c(x)=c0+C1*x, and the parameters c0 and C1 calculate the road characteristics.

[0022] As a further solution of the present invention, the radar is a millimeter wave radar for front target recognition using an automobile driving assistance system.

[0023] In summary, compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0024] The present invention proposes a method for identifying a target vehicle ahead of an assisted driving system based on measuring target vehicle information by an on-board radar and estimating the vehicle state and the expected road driving trajectory of the vehicle through Kalman filtering, thereby improving the reliability of the entire assisted driving system from the environmental perception level; the entire target vehicle identification process is simple and efficient, with fewer participating link units and strong adaptability to lighting conditions and weather conditions; full use of information observed by the radar, such as the distance and azimuth of the target, estimates the state of the target vehicle including the lateral and longitudinal speeds and yaw angular velocity, which can effectively reduce false alarms of stationary roadside objects such as trees in green belts and road traffic signs, thereby allowing the assisted driving function to operate normally; an estimate of the road curvature is obtained through the ratio of the vehicle's yaw angular velocity and longitudinal velocity, which is more efficient and accurate; whether the lateral offset is less than half the lane width is used to determine whether the target vehicle is still in the same lane as the vehicle at the next moment, and then determine whether the target vehicle is a target that the assisted driving system needs to accurately identify at the next moment, which greatly simplifies the road scene and enables it to adapt to various complex and changeable actual road conditions.

[0025] In order to more clearly illustrate the structural features and effects of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for identifying a target vehicle ahead of a radar-based driver assistance system in an embodiment of the invention;

[0027] Figure 2 This is a schematic diagram of the vehicle state estimation process in an embodiment of the invention;

[0028] Figure 3 This is a schematic diagram of the target vehicle state estimation process in an embodiment of the invention;

[0029] Figure 4 Schematic diagram of a method for calculating the lateral offset of a target vehicle relative to the center line of the expected driving trajectory of the vehicle in an embodiment of the invention;

[0030] Figure 5 This is a schematic diagram of the process of calculating the lateral offset of the target vehicle by calculating the road curve rate parameter in an embodiment of the invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0032] See also Figure 1 to Figure 5 The method for identifying a target vehicle ahead of the auxiliary driving system based on radar comprises the following steps:

[0033] A method for identifying a target vehicle ahead of a driver assistance system based on radar comprises the following steps:

[0034] Step 1, vehicle state estimation: read a specific vehicle state parameter value δ from the vehicle body controller, the vehicle state parameter value is to read the front wheel angle value and the motor torque value of the vehicle, establish a three-degree-of-freedom vehicle model, input the vehicle motion state parameter (front wheel angle value δ), and then select the required yaw rate ω and center of mass sideslip angle β from the vehicle state parameter values ​​output by the three-degree-of-freedom vehicle model, and finally perform an extended Kalman filter on the yaw rate and center of mass sideslip angle to calculate the estimated values ​​of the yaw rate and center of mass sideslip angle and estimate the instantaneous curvature of the current position of the vehicle;

[0035] Step 2: Target vehicle state estimation: Use radar to measure the motion information data of the target vehicle, the motion information data includes the relative speed, relative distance and azimuth of the target vehicle, and perform vehicle motion compensation on the motion information data of the target vehicle; then, establish a target state estimation filter and use Kalman filtering to estimate the motion state information of the target vehicle, calculate the current road curvature of the target vehicle based on the motion state information of the target vehicle, and use the longitudinal speed v of the target vehicle to calculate the target vehicle state. yi and the lateral velocity v xi According to the formula ψ=arctan(v yi / v xi ) Calculate the heading angle of the target vehicle;

[0036] Step 3. Calculate the lateral offset of the target vehicle relative to the center line of the expected driving trajectory of the vehicle: Calculate the lateral offset of the target vehicle relative to the center line of the future expected driving trajectory of the vehicle after the path angle correction, and determine whether the target vehicle in front is currently in the lane or in the adjacent lane: If the lateral offset is greater than or equal to half the lane width, the target in front is not in the lane; if the lateral offset is less than half the lane width and is closest to the vehicle, the target is a valid target in the assisted driving system.

[0037] In step 2, the step of the vehicle motion compensation is: decomposing the vehicle motion compensation into rotation compensation and translation compensation, first performing rotation compensation and then translation compensation.

[0038] As a further solution of the present invention, the specific execution steps are: converting the data observed by the radar at time k to the vehicle coordinate system at time k+1, and the coordinates of the target vehicle in the vehicle coordinate system at time k are (x k ,y k), the longitudinal velocity of the vehicle is v y , the lateral velocity is v x , the yaw rate is ω, and the angle rotated in a sampling period T is φ = ωT. The vehicle motion is compensated for rotation and translation respectively. In one period, the coordinates after rotation compensation are (x k '=xcosφ+ysinφ,y k '=-xsinφ+ycosφ), then perform translation compensation to convert the coordinates of the target vehicle into (x k+1 =x k '-v x T,y k+1 =y k '-v y T). The target vehicle coordinates and longitudinal and lateral velocities after the motion compensation of the vehicle are input as input values ​​into the target state estimation filter, and the target vehicle coordinates and longitudinal and lateral velocities estimation values ​​of the target state estimation filter are obtained through calculation. After the longitudinal and lateral velocities estimation values ​​of the target vehicle are obtained, the heading angle of the target vehicle is calculated.

[0039] The specific steps of calculating the target vehicle heading angle are as follows: using the target vehicle longitudinal velocity v yi and the lateral velocity v xi According to the formula ψ=arctan(v yi / v xi )calculate.

[0040] In step three, the following steps are included:

[0041] Road curvature parameter calculation: The yaw rate and longitudinal velocity of the vehicle at the current moment can be obtained by the vehicle state estimation step. According to the formula c(x i )=φ i / v xi Calculate the vehicle's position at x i The road curvature at the location, where φ i is the yaw angular velocity, v xi represents the longitudinal velocity, taking φ at different times i and v xi Then we can get c(x i ), and then determine the road curvature model parameters of the lane where the vehicle is located;

[0042] Path angle compensation: When determining the expected future driving trajectory of the vehicle, appropriate corrections need to be made to rotate the original expected driving trajectory by η, where η is the path angle of the vehicle at the current moment.

[0043] The lateral offset of the target vehicle is calculated. The difference between the lateral coordinate of the target vehicle at the current moment and the lateral coordinate of the center line of the expected future driving trajectory of the vehicle is the lateral offset of the target vehicle.

[0044] The specific calculation of road curvature parameters is that the road model adopts a clothoid curve model, and the mathematical formula of the model is c(x)=c0+c1*x. The parameters c0 and C1 calculate the road characteristics. In a shorter preview range, the general road is simplified into a clothoid curve, and the road model formula is c(x)=c0+c1*x, where c0 represents the curvature of the curve at the current position of the vehicle, C1 represents the curvature coefficient, that is, the coefficient of curvature change with the distance from the target to the vehicle, and x represents the distance from the target to the vehicle. In a decision cycle of the assisted driving system, a series of moments separated by the same time interval are taken, and c(xi) of the vehicle at different positions at different times is calculated. The curve is fitted based on the least squares method to determine the model parameters c0 and C1 in the clothoid curve road model.

[0045] The radar is a millimeter wave radar used for front target recognition in a vehicle driving assistance system.

[0046] The specific embodiment method reference steps may be:

[0047] First, a three-degree-of-freedom motion model of the vehicle is established to estimate the motion state of the vehicle, including the yaw rate and the sideslip angle of the center of mass. The instantaneous curvature of the vehicle's current position can be obtained from the yaw rate and speed of the vehicle.

[0048] Secondly, based on the distance and azimuth data measured by the millimeter-wave radar to the target vehicle, a Kalman filter for estimating the state of the target vehicle is established to estimate the lateral and longitudinal speeds and yaw rate of the target vehicle. The current road curvature of the target is calculated based on the yaw rate and vehicle speed of the target. Then, based on a certain road model, the future road shape of the vehicle can be determined, and certain corrections can be made to obtain the expected driving trajectory of the vehicle in the next time period.

[0049] Finally, the lateral offset between the center line of the vehicle's expected driving trajectory and each target car is calculated. If the lateral offset is less than half the lane width and is closest to the vehicle, then the target is a valid target in the assisted driving system.

[0050] The technical principle of the present invention is described above in combination with the specific embodiments, which are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments, and all technical solutions under the idea of ​​the present invention belong to the protection scope of the present invention. Those skilled in the art can think of other specific implementations of the present invention without creative work, and these methods will fall within the protection scope of the present invention.

Claims

1. A method for identifying a target vehicle ahead of a radar-based driver assistance system, characterized in that: The following steps are involved: Step 1, vehicle state estimation: read the vehicle state parameter value from the vehicle body controller, the vehicle state parameter value is to read the front wheel angle value and the motor torque value of the vehicle, establish a three-degree-of-freedom vehicle model, input the vehicle motion state parameter, and then select the required yaw rate and center of mass sideslip angle from the vehicle state parameter values ​​output by the three-degree-of-freedom vehicle model, and finally perform Kalman filtering on the yaw rate and center of mass sideslip angle, calculate the estimated values ​​of the yaw rate and center of mass sideslip angle, and estimate the instantaneous curvature of the current position of the vehicle; Step 2: Target vehicle state estimation: The target vehicle motion information data is measured by radar, and the target vehicle motion compensation is performed on the target vehicle motion information data; then, the target vehicle motion state information is estimated by using Kalman filtering through the establishment of a target state estimation filter, and the current road curvature of the target vehicle is calculated based on the target vehicle motion state information. The longitudinal velocity V yi and the lateral velocity V xi According to the formula ψ=arctan(V yi / V xi ) Calculate the heading angle of the target vehicle; Step 3. Calculate the lateral offset of the target vehicle relative to the center line of the expected driving trajectory of the vehicle: Calculate the lateral offset of the target vehicle relative to the center line of the future expected driving trajectory after the path angle correction of the vehicle, and determine whether the target vehicle in front is currently in the lane or in the adjacent lane: If the lateral offset is greater than or equal to half the lane width, the target in front is not in the lane; if the lateral offset is less than half the lane width and is closest to the vehicle, the target is a valid target in the assisted driving system.

2. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 1, characterized in that: In step 2, the motion information data of the target vehicle measured by the radar of the own vehicle includes the relative speed, relative distance and azimuth of the target vehicle.

3. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 1, characterized in that: In step 2, the step of the vehicle motion compensation is: decomposing the vehicle motion compensation into rotation compensation and translation compensation, first performing rotation compensation and then translation compensation.

4. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 3, characterized in that: The specific execution steps are as follows: the data observed by the radar at time k is converted to the vehicle coordinate system at time k+1. The coordinates of the target vehicle in the vehicle coordinate system at time k are (x k ,y k ), the longitudinal speed of the vehicle is V y , the lateral velocity is V x , the yaw rate is ω, and the angle rotated in a sampling period T is φ = ωT. The vehicle motion is compensated for rotation and translation respectively. In one period, the coordinates after rotation compensation are (x k '=xcosφ+ysinφ,y k '=-xsinφ+ycosφ), then perform translation compensation to convert the coordinates of the target vehicle into (x k+1 =x k '-v x T,y k+1 =y k '-v y T).

5. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 1, characterized in that: In step 2, the steps of using the target state estimation filter Kalman filter are as follows: inputting the target vehicle coordinates and longitudinal and lateral velocities after the motion compensation of the vehicle into the target state estimation filter as input values, obtaining the target vehicle coordinates and longitudinal and lateral velocity estimation values ​​of the target state estimation filter through calculation, and after obtaining the longitudinal and lateral velocity estimation values ​​of the target vehicle, calculating the heading angle of the target vehicle.

6. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 5, characterized in that: The specific steps of calculating the target vehicle heading angle are as follows: using the target vehicle longitudinal velocity V yi and the lateral velocity V xi According to the formula ψ=arctan(V yi / V xi )calculate.

7. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 1, characterized in that: In step three, the following steps are included: Road curvature parameter calculation: The yaw rate and longitudinal velocity of the vehicle at the current moment can be obtained by the vehicle state estimation step. According to the formula c(X i )=φ i / V xi Calculate the vehicle's position at X i The road curvature at the location, where φ i is the yaw rate, V xi represents the longitudinal velocity, taking φ at different times i and V xi Then we can get c(X i ), and then determine the road curvature model parameters of the lane where the vehicle is located; Path angle compensation: When determining the expected future driving trajectory of the vehicle, appropriate corrections need to be made to rotate the original expected driving trajectory by η, where η is the path angle of the vehicle at the current moment. The lateral offset of the target vehicle is calculated. The difference between the lateral coordinate of the target vehicle at the current moment and the lateral coordinate of the center line of the expected future driving trajectory of the vehicle is the lateral offset of the target vehicle.

8. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to claim 7, characterized in that: The road curvature parameter calculation is specifically as follows: the road model adopts a clothoid curve model, the mathematical formula of the model is c(x)=c0+c1*x, and the parameters c0 and c1 calculate the road characteristics.

9. The method for identifying a target vehicle ahead of a driver assistance system based on radar according to any one of claims 1 to 8, characterized in that: The radar is a millimeter wave radar used for front target recognition in a vehicle driving assistance system.

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