CN118004275B - Transverse control method and structure for vehicles moving straight at intersection - Google Patents
Transverse control method and structure for vehicles moving straight at intersection Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B62D5/0457—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear characterised by control features of the drive means as such
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Abstract
The application provides a vehicle transverse control method for straight running at an intersection, which can improve the precision of transverse control when a straight running vehicle passes through the central area of the intersection based on lower cost. By fusing the lane center line track, the self-vehicle motion track and the front vehicle motion track, a more accurate vehicle running reference track is obtained, and the occurrence probability of abnormal situations that the driving auxiliary system moves straight at the intersection is reduced. Meanwhile, the application also discloses a vehicle transverse control structure for straight running at the intersection.
Description
Technical Field
The invention relates to the technical field of driving assistance, in particular to a vehicle transverse control method and structure for straight running at an intersection.
Background
The autopilot technology refers to a vehicle capable of autonomously performing operations such as acceleration, steering, braking, and avoiding obstacles without intervention of a human driver. Currently, many automobile manufacturers have introduced home automobiles equipped with L2-level (partially automated) driving functions. These vehicles are often equipped with advanced driving assistance systems, such as adaptive cruise control (Adaptive Cruise Control, ACC), lane keeping assistance system (LANE KEEPINGASSIST, LKA), traffic congestion assistance (Traffic Jam Assistant, TJA), pilot assistance (Navigateon Autopilot, NOA), etc., which enable a degree of automated driving under certain conditions. The current driving assistance system can monitor the position of the vehicle on the road by using a camera, a laser radar and other sensors, and can automatically adjust the direction of the vehicle, so that the driver is helped to keep the vehicle from deviating from the lane in the driving process. In general, a driving assistance system is capable of operating in a speed range of a vehicle, and generally functions from a low speed to a high speed. The technology can improve the driving safety, and has a certain effect especially in long-distance high-speed driving or fatigue driving.
The control of the vehicle by the driving assistance system may be classified into lateral control for keeping the vehicle traveling straight, controlling the steering of the vehicle, changing the lane in which the vehicle is located, and longitudinal control for controlling the speed of the vehicle as a control target.
However, as shown in fig. 1, when the vehicle of the driving support system is depicted as a host vehicle 2 traveling straight at an intersection of a urban road, after entering the center area 1 of the intersection, the center area 1 is not marked with a lane line, that is, the lane line data is lost for the vehicle support system, but the vehicle and road conditions in the center area are more complicated, so that an abnormal situation is very likely to occur.
For the scenario shown in fig. 1, the lateral control of the existing L2-level driving assistance system generally adopts three schemes to cope with the scenario of losing the middle lane line in straight crossing. The first scheme is to latch the lane center line information before the lane line is lost as a reference track for the vehicle to travel straight until the lane line information is re-acquired. The second solution is to keep the lateral control attitude of the vehicle unchanged until the intersection is passed. The third scheme is to plan the reference track of the vehicle running by using the road lane line information provided by the high-precision map or the laser radar.
The problems with the current solution are: when the first scheme and the second scheme pass through the intersection, the information before the lane line is lost has deviation from the real road information, the central line information before the lane line is lost is used as a reference track for transverse control, or the transverse control gesture information before the lane line is lost is directly used, so that the control is not accurate enough, and the vehicle can possibly deviate from the lane. The use of high-precision maps and lidars increases the cost of the whole vehicle, and only the high-precision map data of partial cities cannot cover the road scenes of all cities at present.
Disclosure of Invention
In order to solve the problem that a driving auxiliary system in the prior art cannot realize accurate transverse control when data of straight-going lane lines of an intersection are lost, the application provides a transverse control method for vehicles in straight-going of the intersection, which can improve the accuracy of transverse control when the straight-going vehicles pass through the central area of the intersection based on lower cost. Meanwhile, the application also discloses a vehicle transverse control structure for straight running at the intersection.
The technical scheme of the invention is as follows: a vehicle transverse control method for straight running at an intersection is characterized by comprising the following steps:
S1: based on the vehicle-mounted camera, monitoring the current running scene of the vehicle in real time;
The camera is arranged in front of the current vehicle and used for detecting the environmental information in front of the vehicle; the environment information includes: lane lines, vehicles, pedestrians, obstacles;
S2: confirming whether the current scene is an intersection straight scene or not;
if yes, executing step S3, and entering a vehicle transverse control flow of the intersection;
Otherwise, circularly executing the steps S1-S2;
s3: collecting lane line information and calculating lane center line tracks;
s4: calculating the predicted motion trail of the current vehicle according to the own vehicle motion information of the VCU;
S5: judging whether a preceding vehicle exists in the current vehicle or not;
If yes, after calculating the historical motion trail of the front vehicle according to the historical motion information of the front vehicle, executing a step S6;
Otherwise, after the historical motion trail of the front vehicle is assigned to be empty, executing the step S6;
S6: judging whether the historical motion trail of the front vehicle is empty or not;
If so, fusing the lane center line track and the predicted motion track by using an extended Kalman filtering method to obtain a final vehicle running reference path track;
otherwise, fusing the lane center line track, the predicted motion track and the front vehicle historical motion track by using an extended Kalman filtering method to obtain a final vehicle running reference path track;
the track equation of the fused vehicle running reference path is as follows:
ys=ds+as×xs+bs×x2 s+cs×x3 s;
the track calculation is based on a Cartesian vehicle coordinate system, an origin O is the center of a rear axle of the vehicle when the track calculation starts, an X-axis is consistent with the advancing direction of the vehicle, a Y-axis is consistent with the right direction of the vehicle, Y s is the Y-axis coordinate of a reference path track point, X s is the X-axis coordinate of the reference path track point, and a s、bs、cs and d s are coefficients of a track equation;
S7: calculating a target steering wheel angle by using a transverse track tracking PID based on pre-aiming;
ΦST=Φf+Φpi+ΦZ；
Wherein, phi ST is the target steering wheel angle, phi f is the feed-forward steering wheel angle, phi pi is the feedback pid control steering wheel angle, and phi Z is the zero offset correction angle of the pre-calibrated steering wheel installation;
Φf= Kf*i*(180/π)*arctan(L*bF)；
K f is a feedforward rotation angle calibration coefficient and is determined by the speed of the vehicle and the comprehensive deviation; i is the transmission ratio of the steering system; l is the wheelbase of the vehicle, b F is the curvature of a reference point on a reference track, and the reference point is the track point closest to the pre-aiming point on the reference track;
；
Wherein K PY is a coefficient of a proportional term for feedback control of the lateral deviation distance, and d Y is a lateral deviation distance between the pre-aiming point and the reference point; k PH is a course angle deviation feedback control proportional term coefficient, d F is a pre-aiming distance, and is determined by the speed of a vehicle, the curvature of a reference point and the pre-aiming time, a Y is a course angle deviation between the pre-aiming point and the reference point, and K i is a feedback control integral term coefficient;
s8: after the interference limiting treatment is carried out on the target steering wheel angle, a treated steering wheel angle is obtained, and the treated steering wheel angle is output to a vehicle VCU controller to control the transverse movement of the vehicle;
The interference limiting process includes: clipping processing, filtering processing, and rate of change limiting processing.
It is further characterized by:
it also comprises the following steps:
S9: judging whether to end the transverse control according to the end condition;
The end condition includes:
Condition 1: when the quality of the left lane line and the right lane line is available, the left lane line and the right lane line last for more than 3 s;
condition 2: the driver takes over the steering wheel operation manually;
when any one of the two conditions is met, judging that the transverse control is finished;
If the transverse control is judged to be finished, exiting;
otherwise, maintaining control of vehicle lateral movement using the processed steering wheel angle;
In step S2, when the following scene conditions are satisfied at the same time, it is determined that the current vehicle is in the intersection straight scene:
scene condition 1: the quality of the lane lines at the left side and the right side can be avoided;
Or the one-sided lane line quality is available and the curvature is greater than the set threshold SC curv and the curvature change rate is greater than the set threshold SC crate;
Scene condition 2: the left and right steering lamps of the vehicle are in an off state;
Scene condition 3: the vehicle yaw rate is less than the set threshold SC yaw;
Scene condition 4: the vehicle heading angle is less than the set threshold SC head;
Scene condition 5: the steering wheel angle is less than a set threshold SC strag;
Scene condition 6: the traffic light state is green light or yellow light;
scene condition 7: the lane centering function is in an open state;
Scene condition 8: the self-adaptive cruising function is in an on state;
in step S3, the method for calculating the lane center line track specifically includes the following steps:
a1: the method comprises the steps of collecting lane line data of the current vehicle in the previous second of a straight scene of an intersection, wherein the sampling period is 40ms;
a2: the x-axis coordinates of discrete points are calculated using the vehicle longitudinal speed integration method x H:
，
Wherein v log is the vehicle longitudinal speed at the i-th discrete point moment, and x Hi is the x-axis coordinate of the i-th discrete point; t is the time corresponding to the discrete point;
a3: calculating y-axis coordinates y H of the discrete points:
When the quality of the lane lines at the left side and the right side is available:
yHi=0.5×(LC0i+RC0i)；
Wherein L C0i is the y-axis coordinate of the left lane line reference point corresponding to the ith discrete point; r C0i is the y-axis coordinate of the right lane line reference point corresponding to the i-th discrete point;
When the left lane line is available, the right lane line quality is not available:
yHi=LC0i+H/2；
Wherein H is the width of the self-vehicle lane which is latched when the quality of lane lines at the left side and the right side is available;
When the right lane line is available, the left lane line quality is not available:
yHi=RC0i-H/2；
a4: fitting a cubic polynomial curve equation by using discrete points (x H,yH) to obtain a lane centerline track:
yH=dH+aH×xH+bH×x2 H+cH×x3 H;
Wherein y H is the ordinate of the track point of the center line of the fitted lane, x H is the abscissa of the track of the center line of the fitted lane, a H is the slope of the track point of the center line of the fitted lane, b H represents one half of the curvature of the track point of the center line of the fitted lane, c H represents one sixth of the curvature change rate of the track point of the center line of the fitted lane, and d H is the abscissa of the track starting point of the center line of the fitted lane;
In step S4, the method for calculating the predicted motion trail of the current vehicle includes the following steps:
b1: calculating a motion discrete point (x V,yV) of the current vehicle based on the two-degree-of-freedom vehicle steady-state kinematic model;
xVi= ( L(1+Kv2)/(Ks*S))*cos(yaw*t)；
yVi= ( L(1+Kv2)/(Ks*S))*[1-sin(yaw*t)]；
Wherein L is the vehicle wheelbase, v is the longitudinal speed of the current vehicle, K s is the steering transmission ratio, S is the steering wheel angle, yaw is the yaw rate, and t is the time of the discrete point; k is a vehicle cornering coefficient;
K =[(a/k2)-(b/k1)] *m/L2；
Wherein m is the mass of the whole vehicle, a is the distance from the mass center of the vehicle to the front wheel, b is the distance from the mass center of the vehicle to the rear wheel, k 1 is the cornering stiffness of the front wheel, and k 2 is the cornering stiffness of the rear wheel;
b2: fitting a cubic polynomial curve equation based on the motion discrete points (x V,yV) of the current vehicle to obtain a predicted motion trail equation of the current vehicle:
yV=dV+aV×xV+bV×xV 2+cV×xV 3,
Wherein y V is the vertical axis coordinate of the predicted motion track point of the fitted vehicle, x V is the horizontal axis coordinate of the predicted motion track point of the fitted vehicle, a V predicts the slope of the motion track point, b V represents one half of the curvature of the predicted motion track point, c V represents one sixth of the curvature change rate of the predicted motion track point, and d V is the horizontal axis coordinate of the starting point of the predicted motion track;
in step S5, judging whether the current vehicle has a preceding vehicle or not based on the millimeter wave radar, and detecting preceding vehicle information;
The preceding vehicle information includes: distance of the preceding vehicle from the current vehicle and speed of the preceding vehicle;
in step S5, the method for calculating the motion track of the preceding vehicle includes the following steps:
c1: based on the previous vehicle data 1s before the discrete point selection calculation moment, the sampling period is 40ms, and the discrete point (x Ei,yEi) calculation formula is as follows:
xEi= OLogi- OLogi-1，
yEi= OLati；
Wherein x Ei and y Ei represent the abscissa of the i-th discrete point; OLog i is the longitudinal distance of the front vehicle from the vehicle at the moment corresponding to the ith discrete point, OLog i-1 is the longitudinal distance of the front vehicle from the vehicle at the moment of the last discrete point; OLat i is the lateral distance of the vehicle from the vehicle before the moment corresponding to the ith discrete point;
c2: fitting a polynomial equation of degree 3 of the historical motion trail of the front vehicle based on discrete points (x Ei,yEi):
yE=dE+aE×xE+bE×x2 E+cE×x3 E;
Wherein y E is the vertical axis coordinate of the front vehicle history motion track point, x E is the horizontal axis coordinate of the front vehicle history motion track point, a E is the slope of the front vehicle history motion track point, b E is one half of the curvature of the front vehicle history motion track point, c E is one sixth of the curvature change rate of the front vehicle history motion track point, and d E is the horizontal axis coordinate of the front vehicle history motion track starting point;
In step S8, the interference limiting process specifically includes:
The limiting treatment is to limit the steering wheel angle to be within plus or minus 30 degrees;
The filtering process uses low pass filtering;
The change rate limiting process limits the steering angle of the steering wheel to within 20 degrees per second.
A vehicle lateral control structure for straight through an intersection, comprising: camera, millimeter wave radar and control module, its characterized in that: the control module includes: the system comprises a sensor information processing module, a track calculation module, a track fusion module and a transverse track tracking PID module; the camera and the millimeter microwave radar are electrically connected with the control module, and the control module is in communication connection with a VCU vehicle-mounted controller and an ADU automatic driving controller of the current vehicle;
the sensor information processing module includes: the system comprises a camera information processing module, a vehicle information processing module, a target information processing module and a scene judging module;
The camera information processing module receives the data acquired and processed by the camera, and the main operation is lane line quality judgment and lane line data low-pass filtering; the vehicle information processing module receives signals of a vehicle body controller VCU, and the main operation is data validity check and data filtering; the target information processing module receives target information after ADU (automatic dependent surveillance) camera data and millimeter wave radar data are fused, the main operation is target data filtering, and then a front vehicle target is selected according to the transverse distance and the longitudinal distance of the target; when there is no valid front truck target, the front truck target validity signal should be set to 0; the scene judging module receives the processed camera information, the processed vehicle information and the processed front vehicle target information, and mainly judges whether the current scene where the vehicle is currently located is an intersection straight-going scene or not;
the track calculation module comprises a lane center line track calculation module, a self-vehicle movement track calculation module and a front vehicle movement track calculation module, and is used for calculating a lane center line track, a self-vehicle movement track and a front vehicle movement track respectively based on the data acquired by the sensor information processing module and sending a settlement result to the track fusion module;
In the track fusion module, the track fusion module fuses the track data calculated by the track calculation module by using the extended Kalman filter to obtain a track equation of a fused vehicle running reference path corresponding to the current vehicle; the obtained track equation of the vehicle running reference path is sent to the transverse track tracking PID module;
The transverse track tracking PID module comprises: the device comprises a feedforward control module, a feedback pid module and a steering wheel angle output module, wherein the feedforward control module calculates according to the curvature of a reference track to obtain a feedforward steering wheel angle; the feedback pid control is a PI controller, the input is the transverse deviation distance and the course angle of the reference track, and the output feedback pid control steering wheel corner;
The steering wheel angle output module calculates a target steering wheel angle based on the feedforward steering wheel angle, the feedback pid control steering wheel angle and a zero offset correction angle of a pre-calibrated steering wheel installation; and then, performing interference limiting processing on the target steering wheel angle to obtain a processed steering wheel angle, and outputting the processed steering wheel angle to a vehicle VCU controller to control the transverse movement of the vehicle.
It is further characterized by:
it also includes: a method status flag bit F-LC whose state includes: an activated state and a deactivated state;
when the method state flag bit F-LC is in an activated state, starting the control module to execute a vehicle transverse control method;
And stopping the control module when the method state flag bit F-LC is in the inhibition state, and ending the vehicle transverse control method.
The vehicle transverse control method for straight-going at the intersection provided by the application obtains a more accurate vehicle running reference track by fusing the lane central line track, the self-vehicle running track and the front vehicle running track, and reduces the occurrence probability of abnormal situations of the driving auxiliary system in straight-going at the intersection. According to the method, a high-definition map is not needed, more expensive equipment is not needed to be added, the reference track of the vehicle in the straight running at the intersection can be planned in real time based on lower cost, and the accuracy of the transverse control of the vehicle is improved.
Drawings
FIG. 1 is a schematic view of straight traveling at an intersection;
FIG. 2 is a schematic diagram of a vehicle hardware configuration in the present method;
FIG. 3 is a schematic diagram of the trajectory computation in the present method;
FIG. 4 is a schematic diagram of a lateral tracking PID in the present method;
fig. 5 is a schematic diagram showing the module configuration of the control module in the vehicle lateral control structure for straight running at an intersection in the present method.
Detailed Description
The application comprises a vehicle transverse control method for straight running at an intersection, which comprises the following steps.
S1: based on the vehicle-mounted camera, monitoring the current running scene of the vehicle in real time;
The camera is installed in the place ahead of current vehicle, detects vehicle front side environmental information, and environmental information includes: lane lines, vehicles, pedestrians, obstacles.
As shown in fig. 2, the method needs to install a camera with calculation capability and a device with speed measurement capability on a vehicle. From the cost aspect, the millimeter wave radar can be used for achieving the functions required by the method. Judging whether the current vehicle has a preceding vehicle or not based on millimeter wave radar, and detecting preceding vehicle information, wherein the preceding vehicle information comprises: the distance of the preceding vehicle from the current vehicle and the speed of the preceding vehicle. Namely, the method does not need to use a high-precision map and a laser radar, and can help the L2-level vehicle driving auxiliary system to finish the straight running of the intersection of the urban road on the basis of a 1V1R (one camera and one millimeter wave radar) sensor, so that the additional cost is not increased.
In the method, the rest parameters required by calculation are based on a CAN bus, and the parameters are communicated with a VCU vehicle-mounted controller in a control system of the intelligent vehicle to complete acquisition. And finally, transmitting the calculation result to an ADU automatic driving controller and an EPS electric power steering system to complete the transverse control of the vehicle. The modules in the intelligent vehicle involved include:
VCU onboard controllers-responsible for integrating and processing data from the various sensors and implementing decisions and controls for the vehicle,
The ADU automatic driving controller is responsible for realizing an automatic driving auxiliary function and is a deployment controller of the method;
The EPS electric power steering system is responsible for steering the vehicle.
As shown in fig. 5, in order to implement a vehicle lateral control method for straight running at an intersection, a control module needs to be built on a vehicle, and the control module includes: the system comprises a sensor information processing module, a track calculation module, a track fusion module and a transverse track tracking PID (proportional-integral-DERIVATIVE CONTROL) module.
The sensor information module processing comprises four parts: the system comprises a camera information processing module, a vehicle information processing module, a target information processing module and a scene judging module.
The signal received by the camera information processing process mainly comes from the data acquired and processed by the camera, and the main operation is lane line quality judgment and lane line data low-pass filtering.
The data based on camera gathers includes: left lane line state L ava, left lane line confidence L conf, left lane line lateral distance L C0, left lane line heading angle L C1, left lane line curvature L C2, left lane line curvature change rate L C3, left lane line visual distance L dst、, right lane line state R ava, right lane line confidence R conf, right lane line lateral distance R C0, right lane line heading angle R C1, right lane line curvature R C2, right lane line curvature change rate R C3, left lane line visual distance R dst、 traffic light state.
The signals received in the vehicle information processing process mainly come from a vehicle body controller VCU, and the main operation is data validity check and data filtering;
The data collected based on the body controller VCU includes: vehicle longitudinal speed V log, vehicle longitudinal acceleration a log, vehicle lateral acceleration a lat, vehicle yaw rate YawRate, steering wheel angle R str, vehicle turn signal state S trn, brake state S brk, throttle state S acc, and vehicle Heading angle Heading.
The signal received in the target information processing process is target information after ADU fuses camera data and millimeter wave radar data; the main operation is that the target data is filtered, and then the front vehicle target is selected according to the transverse distance and the longitudinal distance of the target; when there is no valid front truck target, the front truck target validity signal should be set to 0; and sending the front vehicle target data and the information of whether the front vehicle exists or not to the track calculation module.
The received signals include: target lateral distance OLat, target longitudinal distance Olog, target longitudinal speed OlogV, target longitudinal acceleration OlogA.
Scene judgment, wherein the received signals are processed camera information, vehicle information and target information; in the step S2, whether the current scene where the vehicle is currently located is an intersection straight scene or not is judged.
S2: confirming whether the current scene is an intersection straight scene or not;
if yes, executing step S3, and entering a vehicle transverse control flow of the intersection;
otherwise, the steps S1-S2 are circularly executed.
In the method, when the following scene conditions are met at the same time, judging that the current vehicle is in an intersection straight scene:
scene condition 1: the quality of the lane lines at the left side and the right side can be avoided;
Or the one-sided lane line quality is available and the curvature is greater than the set threshold SC curv and the curvature change rate is greater than the set threshold SC crate;
Scene condition 2: the left and right steering lamps of the vehicle are in an off state;
Scene condition 3: the vehicle yaw rate is less than the set threshold SC yaw;
Scene condition 4: the vehicle heading angle is less than the set threshold SC head;
Scene condition 5: the steering wheel angle is less than a set threshold SC strag;
Scene condition 6: the traffic light state is green light or yellow light;
scene condition 7: the lane centering function is in an open state;
Scene condition 8: the adaptive cruise function is on.
And once the sensor information module judges that the current vehicle enters the straight-going scene of the intersection, the method is activated, the track calculation module is started to perform subsequent calculation, and meanwhile, a prompt signal is sent to the central gateway, and the vehicle sends out a prompt sound to remind a driver of safety.
In the running process of the vehicle, the sensor information module is started in real time and monitors the current scene of the vehicle, and once the quality of the left lane line and the right lane line is available and lasts for more than 3 seconds, the sensor information module judges that the vehicle has directly passed through the intersection, the method is marked as entering into a suppression state, and calculation is stopped. Or in the implementation of the method, if the driver takes over the steering wheel operation, the method marking bit is also put into a suppression state. In particular, the method is marked by a method state marking bit as to whether the method is in an activated state or a deactivated state.
In the track calculation module, the track calculation includes: calculating the track of the center line of a lane, calculating the motion track of a self-vehicle, calculating the motion track of a front vehicle, and correspondingly arranging three sub-modules: the system comprises a lane center line track calculation module, a self-vehicle movement track calculation module and a front vehicle movement track calculation module.
Referring to fig. 3, in the three sub-modules, the track coordinate system is all a cartesian coordinate system, the origin O is the center of the rear axle of the vehicle at the beginning of track calculation, the X-axis is consistent with the advancing direction of the vehicle, the Y-axis is consistent with the right direction of the vehicle, the track calculation methods all use a method of fitting a cubic polynomial curve equation with discrete points, and the track equation is: y=d+a×x+b×x 2+c×x3;
Wherein; d represents the horizontal axis coordinate of the track starting point, a represents the slope of the track point, b represents one half of the curvature of the track point, c represents one sixth of the curvature change rate of the track point, x represents the horizontal axis coordinate of the track point, and y is the vertical axis coordinate of the track point.
The specific calculation method implemented in the lane center line track calculation module is as follows.
S3: and collecting lane line information and calculating lane center line tracks.
The calculation method of the lane center line track specifically comprises the following steps:
a1: the method comprises the steps of collecting lane line data of the current vehicle in the previous second of a straight scene of an intersection, wherein the sampling period is 40ms;
a2: the x-axis coordinates of discrete points are calculated using the vehicle longitudinal speed integration method x H:
；
Wherein v log is the vehicle longitudinal speed at the i-th discrete point moment, and x Hi is the x-axis coordinate of the i-th discrete point; t is the time corresponding to the discrete point;
a3: calculating y-axis coordinates y H of the discrete points:
When the quality of the lane lines at the left side and the right side is available:
yHi=0.5×(LC0i+RC0i)；
Wherein L C0i is the y-axis coordinate of the left lane line reference point corresponding to the ith discrete point; r C0i is the y-axis coordinate of the right lane line reference point corresponding to the i-th discrete point;
When the left lane line is available, the right lane line quality is not available:
yHi=LC0i+H/2；
Wherein H is the width of the self-vehicle lane which is latched when the quality of lane lines at the left side and the right side is available;
When the right lane line is available, the left lane line quality is not available:
yHi=RC0i-H/2；
a4: fitting a cubic polynomial curve equation by using discrete points (x H,yH) to obtain a lane centerline track:
yH=dH+aH×xH+bH×x2 H+cH×x3 H;
Wherein y H is the ordinate of the track point of the center line of the fitted lane, x H is the abscissa of the track of the center line of the fitted lane, a H is the slope of the track point of the center line of the fitted lane, b H is one half of the curvature of the track point of the center line of the fitted lane, c H is one sixth of the curvature change rate of the track point of the center line of the fitted lane, and d H is the abscissa of the track starting point of the center line of the fitted lane.
The lane line data of the first 1 second is activated by the method to complete the construction of the lane line center track, and the lane center line track is kept unchanged in the whole activation process of the method after the construction is completed until the method jumps to the inhibition state. The method ensures that even if the information of the lane line is lost, the subsequent calculation can be completed based on the lane center line track.
The self-vehicle motion track calculation module calculates the self-vehicle motion track in real time in the process of method activation, predicts the motion track within 0.4s in the future of the self-vehicle according to the vehicle motion information, calculates the discrete point coordinates by using a two-degree-of-freedom vehicle steady-state kinematic model, and simultaneously assumes that the vehicle keeps the yaw rate unchanged within 0.4s in the future.
S4: calculating the predicted motion trail of the current vehicle according to the own vehicle motion information of the VCU;
the method for calculating the predicted motion trail of the current vehicle comprises the following steps:
b1: calculating a motion discrete point (x V,yV) of the current vehicle based on the two-degree-of-freedom vehicle steady-state kinematic model;
xVi= ( L(1+Kv2)/(Ks*S))*cos(yaw*t)；
yVi= ( L(1+Kv2)/(Ks*S))*[1-sin(yaw*t)]；
Wherein L is the vehicle wheelbase, v is the longitudinal speed of the current vehicle, K s is the steering transmission ratio, S is the steering wheel angle, yaw is the yaw rate, and t is the time of the discrete point; k is a vehicle cornering coefficient;
K =[(a/k2)-(b/k1)] *m/L2；
Wherein m is the mass of the whole vehicle, a is the distance from the mass center of the vehicle to the front wheel, b is the distance from the mass center of the vehicle to the rear wheel, k 1 is the cornering stiffness of the front wheel, and k 2 is the cornering stiffness of the rear wheel;
b2: fitting a cubic polynomial curve equation based on the motion discrete points (x V,yV) of the current vehicle to obtain a predicted motion trail equation of the current vehicle:
yV=dV+aV×xV+bV×xV 2+cV×xV 3,
Wherein y V is the vertical axis coordinate of the predicted motion track point of the fitted vehicle, x V is the horizontal axis coordinate of the predicted motion track point of the fitted vehicle, a V is the slope of the predicted motion track point, b V is one half of the curvature of the predicted motion track point, c V is one sixth of the curvature change rate of the predicted motion track point, and d V is the horizontal axis coordinate of the starting point of the predicted motion track.
The method obtains the historical motion trail of the front vehicle according to the transverse distance and the longitudinal distance of the front vehicle, and when the method is in an activated state, the front vehicle motion trail calculation module is started when the effective zone bit of the front vehicle is 1, and otherwise, the front vehicle motion trail calculation module is closed.
S5: judging whether a preceding vehicle exists in the current vehicle or not;
If yes, after calculating the historical motion trail of the front vehicle according to the historical motion information of the front vehicle, executing a step S6;
Otherwise, after the historical motion trail of the front vehicle is assigned to be empty, the step S6 is executed.
In step S5, the method for calculating and transmitting the motion track of the preceding vehicle includes the following steps:
c1: based on the previous vehicle data 1s before the discrete point selection calculation moment, the sampling period is 40ms, and the discrete point (x Ei,yEi) calculation formula is as follows:
xEi= OLogi-OLogi-1，
yEi= OLati；
Wherein x Ei and y Ei represent the abscissa of the i-th discrete point; OLog i is the longitudinal distance of the front vehicle from the vehicle at the moment corresponding to the ith discrete point, OLog i-1 is the longitudinal distance of the front vehicle from the vehicle at the moment of the last discrete point; OLat i is the lateral distance of the vehicle from the vehicle before the moment corresponding to the ith discrete point;
c2: fitting a polynomial equation of degree 3 of the historical motion trail of the front vehicle based on discrete points (x Ei,yEi):
yE=dE+aE×xE+bE×x2 E+cE×x3 E;
Wherein y E is the vertical axis coordinate of the front vehicle history motion track point, x E is the horizontal axis coordinate of the front vehicle history motion track point, a E is the slope of the front vehicle history motion track point, b E is one half of the curvature of the front vehicle history motion track point, c E is one sixth of the curvature change rate of the front vehicle history motion track point, and d E is the horizontal axis coordinate of the front vehicle history motion track starting point.
And after all the track calculation in the track calculation module is finished, sending the calculation result into the track fusion module. The track fusion module uses the extended Kalman filtering to fuse the lane center line track, the self-vehicle motion track and the front vehicle historical motion track calculated by the track calculation module.
S6: judging whether the historical motion trail of the front vehicle is empty or not;
If so, fusing the lane center line track and the predicted motion track of the vehicle by using an extended Kalman filtering method to obtain a final vehicle running reference path track;
otherwise, fusing the lane center line track, the predicted motion track and the front vehicle historical motion track by using an extended Kalman filtering method to obtain a final vehicle running reference path track.
The extended Kalman filtering mainly comprises the following steps:
step one, determining that the filtered state vector x is [ x S,yS,aS,bS,cS,dS ],
Wherein X s is the X-axis coordinate of the reference path track point, Y s is the Y-axis coordinate of the reference path track point, and a S,bS,cS,dS is the parameter of the fusion track equation.
And step two, determining a state update matrix F.
Step three, state covariance prediction, wherein the expression is P k=Fk-1Pk-1FT k-1+Qk-1;
wherein Q k-1 is a system noise matrix, P k is a covariance matrix at k time, F k-1 is a state update matrix at k-1 time, P k-1 is a covariance matrix at k-1 time, and F T k-1 is a transpose matrix of the state update matrix at k-1 time;
step four, calculating Kalman gain, wherein the expression is as follows:
Kk=PkHT k(HkPkHT k+Rk)-1;
Wherein P is covariance matrix, R is observation noise matrix, and H is observation matrix; k is the Kalman gain at the current K time of K k, which represents the K time;
Step five: calculating an error y between the observed value and the predicted value, wherein the expression is as follows: y=z k-Hkxk;
Wherein Z k is the observation value at the current moment, and is a simple fusion track obtained by linear interpolation of three tracks calculated by a track calculation module;
In a specific application, when the three track data are fused through the linear interpolation matrix to obtain Z k, the specific gravity of the three tracks in calculation can be adjusted by respectively setting the weight coefficients of the three tracks in the linear interpolation matrix. For example, in the case of no preceding vehicle, the weight coefficient of the history motion trajectory of the preceding vehicle is set to 0, i.e., the preceding vehicle data does not participate in the calculation. In practical application, if the track of the front vehicle is determined to be within the allowable error range in advance under the condition that the historical motion track of the front vehicle exists, the weight coefficient of the historical motion track of the front vehicle can be increased, and the Z k value is calculated by more depending on the track data of the front vehicle, so that the accuracy of a calculation result is improved.
Step six: calculating an optimal estimated value at the current moment according to the Kalman gain, wherein the expression is x k=xk-1+Kk y;
wherein x k-1 is the predicted value of the previous moment, K k is the Kalman gain of the current moment, and y is the error of the current moment;
step seven: updating covariance matrix, and the expression is: p k=(I-KkHk)Pk-1;
wherein, I is an identity matrix, and P k-1 is a covariance matrix at the previous time.
The extended Kalman filtering not only considers the predicted value of the last moment, but also considers the observed value and the noise of the whole system, and greatly reduces the error caused by simple linear interpolation fusion, thereby being closer to the real motion trail of the vehicle.
Then: the track equation of the fused vehicle running reference path is as follows:
ys=ds+as×xs+bs×x2 s+cs×x3 s;
The track calculation is based on a Cartesian vehicle coordinate system, an origin O is the center of a rear axle of the vehicle when the track calculation starts, an X-axis is consistent with the advancing direction of the vehicle, a Y-axis is consistent with the right direction of the vehicle, Y s is the Y-axis coordinate of a reference path track point, X s is the X-axis coordinate of the reference path track point, and a s、bs、cs and d s are coefficients of a track equation.
And the track fusion module sends the calculation result to the transverse track tracking PID module. The transverse track tracking control PID module is based on a single-point driver pre-aiming model, and referring to FIG. 4, consists of a feedforward control module, a feedback PID control module and a steering wheel angle output module.
Feedforward control is carried out according to the curvature of the reference track, and a feedforward steering wheel rotation angle calculation formula is as follows:
Φf= Kf*i*(180/π)*arctan(L*bF)；
K f is a feedforward rotation angle calibration coefficient and is determined by the speed of the vehicle and the comprehensive deviation; i is the transmission ratio of the steering system; l is the wheelbase of the vehicle, b F is the curvature of the reference point on the reference track, which is the closest track point to the pretightening point.
The feedback pid is controlled as a PI controller, the input is a reference track lateral deviation distance d S and a course angle a S, and the output feedback pid controls the steering wheel rotation angle phi pi:
；
Wherein K PY is a coefficient of a proportional term for feedback control of the lateral deviation distance, and d Y is a lateral deviation distance between the pre-aiming point and the reference point; k PH is a course angle deviation feedback control proportional term coefficient, d F is a pretightening distance, the pretightening distance is determined by the speed of a vehicle, the curvature of a reference point and pretightening time, a Y is a course angle deviation between the pretightening point and the reference point, and K i is a feedback control integral term coefficient.
The steering wheel angle output module is used for tracking and controlling the steering wheel angle finally output by the PID based on the calculated results of the feedforward control and the feedback Pid control.
S7: calculating a target steering wheel angle by using a transverse track tracking PID based on pre-aiming;
ΦST=Φf+Φpi+ΦZ；
Wherein, phi ST is the target steering wheel angle, phi f is the feed-forward steering wheel angle, phi pi is the feedback pid control steering wheel angle, and phi Z is the zero offset correction angle of the pre-calibrated steering wheel installation. Regarding the zero offset correction angle Φ Z, for hardware reasons, after different steering wheels are mounted on the vehicle, there is a certain deviation, and in order to ensure driving safety, each model of vehicle is calibrated in a laboratory before leaving the factory according to the specific characteristics of the steering wheel used. When in calibration, a given table is constructed, corresponding phi Z is set according to different speed values, and the calibration table is stored in the system. When the vehicle is running, the corresponding phi Z value in the calibration table is called based on the current vehicle speed to realize hardware deviation correction of the steering wheel angle. For example, when the speed of the car is 20KM/H, the corresponding Φ Z is 1.0 degree.
S8: in the steering wheel angle output module, interference limitation processing is further needed to be carried out on the target steering wheel angle, the processed steering wheel angle is obtained, the processed steering wheel angle is output to the vehicle VCU controller, and the transverse movement of the vehicle is controlled;
the interference limiting process includes: clipping processing, filtering processing, and rate of change limiting processing.
In particular, the interference limiting process specifically includes:
Because the steering wheel is in straight running at the intersection, limiting the steering wheel angle to be within plus or minus 30 degrees;
The filtering treatment uses low-pass filtering to prevent abrupt change caused by interference;
also according to the intersection straight-going scene, the change in the steering angle is prevented from being too fast, and the change rate limiting process limits the steering angle of the steering wheel to within 20 degrees per second.
S9: judging whether to end the transverse control according to the end condition;
The end conditions include:
Condition 1: when the quality of the left lane line and the right lane line is available, the left lane line and the right lane line last for more than 3 s;
condition 2: the driver takes over the steering wheel operation manually;
when any one of the two conditions is met, judging that the transverse control is finished;
If the transverse control is judged to be finished, setting a method state flag bit F-LC to be in a suppression state, and exiting;
Otherwise, the method state flag bit F-LC is maintained in an activated state, and the processed steering wheel angle is used to control the lateral movement of the vehicle.
In the prior art, if the lane center line information before the lane line is lost is latched and used as the reference track for the straight running of the vehicle in the prior art, the distance between the center of the vehicle and the center line of the lane line is collected in real time and used as the lane center line information for storage in the prior art, so that the actual movement direction of the vehicle is not considered if the lane center line information before the lane line is lost is always adopted, the lateral deviation and the course angle deviation of the transverse control PID are always kept unchanged, the output steering wheel angle is continuously increased, and the vehicle is further caused to be continuously deviated to one side of the lane. In the other scheme in the prior art, the transverse control gesture of the vehicle is kept unchanged, namely the steering wheel angle is unchanged, and in practical application, the vehicle also deviates to one side; both solutions have the potential for problems with vehicle misalignment.
In the method, the extended Kalman filtering method is adopted to fuse the lane central line track and the vehicle predicted motion track, so that the transverse deviation distance between the vehicle and the predicted lane central line track can be effectively and dynamically compensated, and the vehicle can be ensured to travel along the direction of the original lane central line track.
When a front vehicle exists, the track of the front vehicle is in a preset safety range, the method refers to the track of the front vehicle at the same time, adopts an extended Kalman filtering method to fuse the track of the center line of a lane, the predicted motion track and the historical motion track of the front vehicle, and can obtain a more accurate track of the vehicle running reference path by comprehensively considering information of a plurality of data sources, thereby improving the accuracy and the robustness of transverse control, providing better transverse control experience and enhancing the safety of a driving auxiliary system. When the method is specifically applied, a special judging module is arranged on the front vehicle track, whether the front vehicle track is in an allowable deviation range or not is judged, and if the front vehicle track is out of the allowable deviation range, the front vehicle is considered to be absent. The allowable deviation range is set differently according to the type of the intersection. For example, when the number of lanes before and after the intersection is unchanged and changed, the allowable deviation range may be set with different data. The method is ensured to be suitable for various road conditions.
After the technical scheme of the application is used, the manual takeover rate of the L2-level vehicle driving assistance system in the straight-through scene of the intersection is reduced, and the convenience of the driving assistance system is improved.
Claims (10)
1. A vehicle transverse control method for straight running at an intersection is characterized by comprising the following steps:
S1: based on the vehicle-mounted camera, monitoring the current running scene of the vehicle in real time;
The camera is arranged in front of the current vehicle and used for detecting the environmental information in front of the vehicle; the environment information includes: lane lines, vehicles, pedestrians, obstacles;
S2: confirming whether the current scene is an intersection straight scene or not;
if yes, executing step S3, and entering a vehicle transverse control flow of the intersection;
Otherwise, circularly executing the steps S1-S2;
s3: collecting lane line information and calculating lane center line tracks;
s4: calculating the predicted motion trail of the current vehicle according to the own vehicle motion information of the VCU;
S5: judging whether a preceding vehicle exists in the current vehicle or not;
If yes, after calculating the historical motion trail of the front vehicle according to the historical motion information of the front vehicle, executing a step S6;
Otherwise, after the historical motion trail of the front vehicle is assigned to be empty, executing the step S6;
S6: judging whether the historical motion trail of the front vehicle is empty or not;
If so, fusing the lane center line track and the predicted motion track by using an extended Kalman filtering method to obtain a final vehicle running reference path track;
otherwise, fusing the lane center line track, the predicted motion track and the front vehicle historical motion track by using an extended Kalman filtering method to obtain a final vehicle running reference path track;
the track equation of the fused vehicle running reference path is as follows:
ys=ds+as×xs+bs×x2 s+cs×x3 s;
the track calculation is based on a Cartesian vehicle coordinate system, an origin O is the center of a rear axle of the vehicle when the track calculation starts, an X-axis is consistent with the advancing direction of the vehicle, a Y-axis is consistent with the right direction of the vehicle, Y s is the Y-axis coordinate of a reference path track point, X s is the X-axis coordinate of the reference path track point, and a s、bs、cs and d s are coefficients of a track equation;
S7: calculating a target steering wheel angle by using a transverse track tracking PID based on pre-aiming;
ΦST=Φf+Φpi+ΦZ；
Wherein, phi ST is the target steering wheel angle, phi f is the feed-forward steering wheel angle, phi pi is the feedback PID control steering wheel angle, and phi Z is the zero offset correction angle of the pre-calibrated steering wheel installation;
Φf= Kf*i*(180/π)*arctan(L*bF)；
K f is a feedforward rotation angle calibration coefficient and is determined by the speed of the vehicle and the comprehensive deviation; i is the transmission ratio of the steering system; l is the wheelbase of the vehicle, b F is the curvature of a reference point on a reference track, and the reference point is the track point closest to the pre-aiming point on the reference track;
；
Wherein K PY is a coefficient of a proportional term for feedback control of the lateral deviation distance, and d Y is a lateral deviation distance between the pre-aiming point and the reference point; k PH is a course angle deviation feedback control proportional term coefficient, d F is a pre-aiming distance, and is determined by the speed of a vehicle, the curvature of a reference point and the pre-aiming time, a Y is a course angle deviation between the pre-aiming point and the reference point, and K i is a feedback control integral term coefficient;
s8: after the interference limiting treatment is carried out on the target steering wheel angle, a treated steering wheel angle is obtained, and the treated steering wheel angle is output to a vehicle VCU controller to control the transverse movement of the vehicle;
The interference limiting process includes: clipping processing, filtering processing, and rate of change limiting processing.
2. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: it also comprises the following steps:
S9: judging whether to end the transverse control according to the end condition;
The end condition includes:
Condition 1: when the quality of the left lane line and the right lane line is available, the left lane line and the right lane line last for more than 3 s;
condition 2: the driver takes over the steering wheel operation manually;
when any one of the two conditions is met, judging that the transverse control is finished;
If the transverse control is judged to be finished, exiting;
Otherwise, the processed steering wheel angle is kept used to control the lateral movement of the vehicle.
3. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S2, when the following scene conditions are satisfied at the same time, it is determined that the current vehicle is in the intersection straight scene:
scene condition 1: the quality of the lane lines at the left side and the right side can be avoided;
Or the one-sided lane line quality is available and the curvature is greater than the set threshold SC curv and the curvature change rate is greater than the set threshold SC crate;
Scene condition 2: the left and right steering lamps of the vehicle are in an off state;
Scene condition 3: the vehicle yaw rate is less than the set threshold SC yaw;
Scene condition 4: the vehicle heading angle is less than the set threshold SC head;
Scene condition 5: the steering wheel angle is less than a set threshold SC strag;
Scene condition 6: the traffic light state is green light or yellow light;
scene condition 7: the lane centering function is in an open state;
Scene condition 8: the adaptive cruise function is on.
4. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S3, the method for calculating the lane center line track specifically includes the following steps:
a1: the method comprises the steps of collecting lane line data of the current vehicle in the previous second of a straight scene of an intersection, wherein the sampling period is 40ms;
a2: the x-axis coordinates of discrete points are calculated using the vehicle longitudinal speed integration method x H:
；
Wherein v log is the vehicle longitudinal speed at the i-th discrete point moment, and x Hi is the x-axis coordinate of the i-th discrete point; t is the time corresponding to the discrete point;
a3: calculating y-axis coordinates y H of the discrete points:
When the quality of the lane lines at the left side and the right side is available:
yHi=0.5×(L C0i+R C0i)；
Wherein L C0i is the y-axis coordinate of the left lane line reference point corresponding to the ith discrete point; r C0i is the y-axis coordinate of the right lane line reference point corresponding to the i-th discrete point;
When the left lane line is available, the right lane line quality is not available:
yHi=L C0i+H/2；
Wherein H is the width of the self-vehicle lane which is latched when the quality of lane lines at the left side and the right side is available;
When the right lane line is available, the left lane line quality is not available:
yHi=R C0i -H/2；
a4: fitting a cubic polynomial curve equation by using discrete points (x H,yH) to obtain a lane centerline track:
yH=dH+aH×xH+bH×x2 H+cH×x3 H;
Wherein y H is the ordinate of the track point of the center line of the fitted lane, x H is the abscissa of the track of the center line of the fitted lane, a H is the slope of the track point of the center line of the fitted lane, b H is one half of the curvature of the track point of the center line of the fitted lane, c H is one sixth of the curvature change rate of the track point of the center line of the fitted lane, and d H is the abscissa of the track starting point of the center line of the fitted lane.
5. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S4, the method for calculating the predicted motion trail of the current vehicle includes the following steps:
b1: calculating a motion discrete point (x Vi,yVi) of the current vehicle based on the two-degree-of-freedom vehicle steady-state kinematic model;
xVi= ( L(1+Kv2)/(Ks*S))*cos(yaw*t)；
yVi = ( L(1+Kv2)/(Ks*S))*[1-sin(yaw*t)]；
Wherein L is the vehicle wheelbase, v is the longitudinal speed of the current vehicle, K s is the steering transmission ratio, S is the steering wheel angle, yaw is the yaw rate, and t is the time of the discrete point; k is a vehicle cornering coefficient; i is a discrete point sequence number;
K =[(a/k2)-(b/k1)] *m/L2；
Wherein m is the mass of the whole vehicle, a is the distance from the mass center of the vehicle to the front wheel, b is the distance from the mass center of the vehicle to the rear wheel, k 1 is the cornering stiffness of the front wheel, and k 2 is the cornering stiffness of the rear wheel;
b2: fitting a cubic polynomial curve equation based on the motion discrete points (x Vi,yVi) of the current vehicle to obtain a predicted motion trail equation of the current vehicle:
yV=dV+aV×xV+bV×xV 2+cV×xV 3,
Wherein y V is the vertical axis coordinate of the predicted motion track point of the fitted vehicle, x V is the horizontal axis coordinate of the predicted motion track point of the fitted vehicle, a V is the slope of the predicted motion track point, b V is one half of the curvature of the predicted motion track point, c V is one sixth of the curvature change rate of the predicted motion track point, and d V is the horizontal axis coordinate of the starting point of the predicted motion track.
6. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S5, judging whether the current vehicle has a preceding vehicle or not based on the millimeter wave radar, and detecting preceding vehicle information;
The preceding vehicle information includes: the distance of the preceding vehicle from the current vehicle and the speed of the preceding vehicle.
7. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S5, the method for calculating the historical motion trail of the preceding vehicle includes the following steps:
c1: based on the previous vehicle data 1s before the discrete point selection calculation moment, the sampling period is 40ms, and the discrete point (x Ei,yEi) calculation formula is as follows:
xEi= OLogi-OLogi-1，
yEi= OLati；
Wherein x Ei and y Ei represent the abscissa of the i-th discrete point; OLog i is the longitudinal distance of the front vehicle from the vehicle at the moment corresponding to the ith discrete point, OLog i-1 is the longitudinal distance of the front vehicle from the vehicle at the moment of the last discrete point; OLat i is the lateral distance of the vehicle from the vehicle before the moment corresponding to the ith discrete point;
c2: fitting a polynomial equation of degree 3 of the historical motion trail of the front vehicle based on discrete points (x Ei,yEi):
yE=dE+aE×xE+bE×x2 E+cE×x3 E;
Wherein y E is the vertical axis coordinate of the front vehicle history motion track point, x E is the horizontal axis coordinate of the front vehicle history motion track point, a E is the slope of the front vehicle history motion track point, b E is one half of the curvature of the front vehicle history motion track point, c E is one sixth of the curvature change rate of the front vehicle history motion track point, and d E is the horizontal axis coordinate of the front vehicle history motion track starting point.
8. The method for controlling the lateral direction of a vehicle traveling straight through an intersection according to claim 1, wherein: in step S8, the interference limiting process specifically includes:
The limiting treatment is to limit the steering wheel angle to be within plus or minus 30 degrees;
The filtering process uses low pass filtering;
The change rate limiting process limits the steering angle of the steering wheel to within 20 degrees per second.
9. A vehicle lateral control structure for straight through an intersection, comprising: camera, millimeter wave radar and control module, its characterized in that: the control module includes: the system comprises a sensor information processing module, a track calculation module, a track fusion module and a transverse track tracking PID module; the camera and the millimeter wave radar are electrically connected with the control module, and the control module is in communication connection with a VCU vehicle-mounted controller and an ADU automatic driving controller of the current vehicle;
the sensor information processing module includes: the system comprises a camera information processing module, a vehicle information processing module, a target information processing module and a scene judging module;
The camera information processing module receives the data acquired and processed by the camera and judges the quality of the lane line and carries out low-pass filtering on the lane line data; the vehicle information processing module receives signals of a vehicle body controller VCU and performs data validity check and data filtering; the target information processing module receives target information obtained by fusing the ADU with the camera data and the millimeter wave radar data, performs target data filtering, and then screens out a front vehicle target according to the target transverse distance and the target longitudinal distance; when there is no valid front truck target, the front truck target validity signal should be set to 0; the scene judging module receives the processed camera information, the processed vehicle information and the processed front vehicle target information, and judges whether the current scene where the vehicle is currently located is an intersection straight scene or not;
the track calculation module comprises a lane center line track calculation module, a self-vehicle movement track calculation module and a front vehicle movement track calculation module, and is used for calculating a lane center line track, a self-vehicle movement track and a front vehicle movement track respectively based on the data acquired by the sensor information processing module and sending a settlement result to the track fusion module;
In the track fusion module, the track fusion module fuses the track data calculated by the track calculation module by using the extended Kalman filter to obtain a track equation of a fused vehicle running reference path corresponding to the current vehicle; the obtained track equation of the vehicle running reference path is sent to the transverse track tracking PID module;
The transverse track tracking PID module comprises: the device comprises a feedforward control module, a feedback PID module and a steering wheel angle output module, wherein the feedforward control module calculates according to the curvature of a reference track to obtain a feedforward steering wheel angle; the feedback PID control is a PI controller, the input is the transverse deviation distance and the course angle of the reference track, and the feedback PID control steering wheel corner is output;
The steering wheel angle output module calculates a target steering wheel angle based on the feedforward steering wheel angle, the feedback PID control steering wheel angle and a zero deviation correction angle of a pre-calibrated steering wheel installation; and then, performing interference limiting processing on the target steering wheel angle to obtain a processed steering wheel angle, and outputting the processed steering wheel angle to a vehicle VCU controller to control the transverse movement of the vehicle.
10. The cross-vehicle control structure for straight through-traffic at an intersection as recited in claim 9, wherein: it also includes: a method status flag bit F-LC whose state includes: an activated state and a deactivated state;
when the method state flag bit F-LC is in an activated state, starting the control module to execute a vehicle transverse control method;
And stopping the control module when the method state flag bit F-LC is in the inhibition state, and ending the vehicle transverse control method.
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| CN115123294A (en) * | 2022-06-16 | 2022-09-30 | 中国第一汽车股份有限公司 | Vehicle control method, vehicle, storage medium, and electronic device |
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| CN117227714A (en) * | 2023-11-15 | 2023-12-15 | 成都西谌科技有限公司 | Control method and system for turning avoidance of automatic driving vehicle |
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| CN113428218A (en) * | 2021-07-29 | 2021-09-24 | 中汽创智科技有限公司 | Vehicle steering control method, device, equipment and storage medium |
| CN116080754A (en) * | 2023-04-07 | 2023-05-09 | 常州星宇车灯股份有限公司 | Transverse control method for autonomous driving of vehicle |
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