CN115366876A - Lateral control method and device for autonomous vehicle, vehicle and storage medium - Google Patents

Lateral control method and device for autonomous vehicle, vehicle and storage medium Download PDF

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Publication number
CN115366876A
CN115366876A CN202210969583.3A CN202210969583A CN115366876A CN 115366876 A CN115366876 A CN 115366876A CN 202210969583 A CN202210969583 A CN 202210969583A CN 115366876 A CN115366876 A CN 115366876A
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vehicle
curvature
preview
aiming
actual
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王良
雍文亮
胡旺
杨成成
周琼
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Chongqing Changan Automobile Co Ltd
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Chongqing Changan Automobile Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)

Abstract

The application relates to a lateral control method, a device, a vehicle and a storage medium of an automatic driving vehicle, wherein the method comprises the following steps: acquiring the actual speed of an automatic driving vehicle and lane line information of a current driving lane; determining a preview time and a minimum preview distance according to the actual speed and lane line information, and calculating the preview distance and the curvature at a preview point according to the preview time and the minimum preview distance; and calculating a steering angle of the autonomous vehicle based on the curvature and a preset coefficient, and calculating a desired turning angle in combination with the actual yaw rate, and controlling the autonomous vehicle to perform a lateral action according to the desired turning angle. The embodiment of the application can realize the output of the preview point based on the vehicle speed and the lane line information, can give consideration to the vehicle speed and the road condition, has high universality and better calculation power compatibility, thereby ensuring the stability of the transverse action of the automatic driving vehicle.

Description

Lateral control method and device for autonomous vehicle, vehicle and storage medium
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a method and an apparatus for controlling a lateral direction of an automatic driving vehicle, a vehicle, and a storage medium.
Background
The automobile industry develops at a high speed, the intelligent driving function is more mature, the mainstream driver model control method in the automatic driving field is to calculate an expected vehicle running track, then an angle is output through a control module to follow the expected track, and a qualified driver model needs to ensure that the error between the real track and the expected track is less than a value allowed by safe driving.
Under the condition that the calculated expected track is reasonable, the smaller the error and the greater the stability of the automobile following the expected track, the better the selected control system is, and therefore, the selection of the preview point is significant for following the expected track. Generally, the experienced driver increases the pre-aiming distance when the automobile is running straight, and shortens the pre-aiming distance when the automobile passes through a curve or the condition of the automobile is complex, because the closer the pre-aiming is, the faster the driver can react, but the stability of the automobile body is correspondingly reduced, and the farther the pre-aiming is, the more gentle the driver reacts, and the more stable the automobile body is. Therefore, for the driver model automatic control system, if the preview point selection is close, the automobile can frequently respond to the parameters at the close position, the automobile body shakes, and if the preview point selection is far, the automobile can not respond to the change of the parameters, and the following effect is poor.
In the related art, the output of the preview point can be realized by adopting a mixed Gaussian model and a hidden Markov model, however, the training of the model has higher requirement on calculation power, a large amount of real data is required, especially, the more road conditions are, the longer the machine learning time is, and once the road conditions not involved in the model appear, the output preview point is very easy to adapt to the current road conditions, so that traffic accidents are caused and need to be improved.
Disclosure of Invention
The application provides a transverse control method and device for an automatic driving vehicle, the vehicle and a storage medium, and aims to solve the technical problems that in the related art, when a preview point selection model is established for ensuring the transverse control stability of the vehicle, a large amount of real data is required for supporting, the consumed time is long, and a large amount of calculation power is required.
An embodiment of a first aspect of the application provides a lateral control method of an automatic driving vehicle, which comprises the following steps: acquiring the actual speed of an automatic driving vehicle and lane line information of a current driving lane; determining a pre-aiming time and a minimum pre-aiming distance according to the actual vehicle speed and the lane line information, and calculating the pre-aiming distance and the curvature at a pre-aiming point according to the pre-aiming time and the minimum pre-aiming distance; and calculating a steering angle of the autonomous vehicle based on the curvature and a preset coefficient, calculating a desired turning angle in combination with an actual yaw rate, and controlling the autonomous vehicle to perform a lateral action according to the desired turning angle.
According to the technical means, the preview point can be obtained based on the actual speed of the automatic driving vehicle and the current driving lane line information, so that the transverse control of the automatic driving vehicle is realized, the speed and road conditions can be considered, the universality is wide, the calculation capacity compatibility is good, the stability of transverse action of the automatic driving vehicle can be effectively improved, and the driving safety of the automatic driving vehicle is improved.
Optionally, in an embodiment of the present application, after calculating the desired turning angle in combination with the actual yaw rate, the method further includes: carrying out interpolation and filtering processing on the expected corner to obtain a processed expected corner; and generating a final expected corner based on the processed expected corner by using a preset function safety limit strategy, and outputting the final expected corner to a Controller Area Network (CAN) Network of the vehicle.
According to the technical means, the expected corner can be processed, so that the output result can effectively control the transverse action of the automatic driving vehicle, and the transverse control stability of the automatic driving vehicle is guaranteed.
Optionally, in an embodiment of the present application, the calculation formula of the desired rotation angle is:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * Is the desired angle of rotation, p * The curvature at the predicted point of the desired trajectory, K is a stability coefficient determined from the vehicle speed, v is the vehicle speed, i is the steering gear ratio of the vehicle, L is the wheel base of the vehicle, δ (EE) is the median offset of the steering wheel angle, and δ (ω) is a feedback value calculated from the yaw rate of the vehicle.
According to the technical means, the calculation of the expected turning angle can be achieved based on the parameters of the automatic driving vehicle, the current state, the expected track and the like.
Optionally, in an embodiment of the present application, the determining a preview time and a minimum preview distance according to the actual vehicle speed and the lane information includes: detecting an actual curve state of the autonomous vehicle according to the lane line information; and matching corresponding weights based on the actual curve state, and calculating the curvature of a pre-aiming point and the curvature of an upper period of the target track and the weighted curvature of the weights.
According to the technical means, the embodiment of the application can correct corresponding parameters aiming at the curve, so that the stability of the curve running of the automatic driving vehicle is ensured.
Optionally, in an embodiment of the present application, the determining a preview time and a minimum preview distance according to the actual vehicle speed and the lane information further includes: and inquiring a preset relation table according to the curvature of the preview point of the target track, the curvature of the upper period and the weighted curvature of the weight to obtain the preview time and the minimum preview distance.
According to the technical means, the preview time and the minimum preview distance can be obtained based on the preset relation table, so that the preview point can be conveniently determined.
An embodiment of a second aspect of the present application provides a lateral control device of an autonomous vehicle, comprising: the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring the actual speed of an automatic driving vehicle and the lane line information of a current driving lane; the calculation module is used for determining the pre-aiming time and the minimum pre-aiming distance according to the actual vehicle speed and the lane line information, and calculating the pre-aiming distance and the curvature at a pre-aiming point according to the pre-aiming time and the minimum pre-aiming distance; and a control module for calculating a steering angle of the autonomous vehicle based on the curvature and a preset coefficient, calculating a desired steering angle in combination with an actual yaw rate, and controlling the autonomous vehicle to perform a lateral maneuver according to the desired steering angle.
Optionally, in an embodiment of the present application, the method further includes: the processing module is used for carrying out interpolation and filtering processing on the expected corner to obtain a processed expected corner; and the generating module is used for generating a final expected corner based on the processed expected corner by using a preset functional safety limit strategy and outputting the final expected corner to a vehicle CAN network.
Optionally, in an embodiment of the present application, the calculation formula of the desired rotation angle is:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * Is the desired angle of rotation, p * The curvature at the predicted point of the expected track is K, the stability coefficient confirmed according to the vehicle speed is V, the vehicle speed is I, the steering transmission ratio of the vehicle is I, the wheel base of the vehicle is L, the middle deviation value of the steering wheel angle is delta (EE), and the feedback value calculated according to the yaw velocity of the vehicle is delta (omega).
Optionally, in an embodiment of the present application, the calculation module includes: a detection unit configured to detect an actual curve state of the autonomous vehicle according to the lane line information; and the calculating unit is used for matching the corresponding weight based on the actual curve state, and calculating the curvature of the pre-aiming point, the curvature of the upper period and the weighted curvature of the weight of the target track.
Optionally, in an embodiment of the present application, the calculation module further includes: and the query unit is used for querying a preset relation table according to the curvature of the preview point of the target track, the curvature of the upper period and the weighted curvature of the weight to obtain the preview time and the minimum preview distance.
An embodiment of a third aspect of the present application provides a vehicle, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the lateral control method of an autonomous vehicle as described in the above embodiments.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the lateral control method of an autonomous vehicle as above.
The beneficial effects of the embodiment of the application are as follows:
(1) The method and the device can realize the output of the preview point based on the vehicle speed and the lane line information, can give consideration to the vehicle speed and the road condition, have high universality and better calculation power compatibility, thereby ensuring the stability of the transverse action of the automatic driving vehicle;
(2) According to the embodiment of the application, data processing and correction can be carried out on the curve, and then the stability of the transverse action of the curve of the automatic driving vehicle is ensured, so that the driving safety of the automatic driving vehicle is improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a lateral control method for an autonomous vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a lateral control method of an autonomous vehicle according to one embodiment of the present application;
FIG. 3 is a flow chart of a lateral control method of an autonomous vehicle according to one embodiment of the present application;
FIG. 4 is a schematic structural diagram of a lateral control device of an autonomous vehicle according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
10-a lateral control device of the autonomous vehicle; 100-acquisition module, 200-calculation module and 300-control module.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
A lateral control method, a device, a vehicle, and a storage medium of an autonomous vehicle according to embodiments of the present application are described below with reference to the drawings. In order to solve the technical problems that a large amount of real data is needed for supporting, time consumption is long, and a large amount of calculation power needs to be consumed when a preview point selection model is established in order to ensure the stability of the transverse control of a vehicle in the related technology mentioned in the background technology center, the method for transversely controlling the automatic driving vehicle can determine preview time and the minimum preview distance based on the actual speed of the automatic driving vehicle and the lane line information of the current driving lane, further calculate and obtain the preview distance and the curvature of the preview point, obtain the steering angle of the automatic driving vehicle, calculate an expected turning angle by combining the actual yaw rate, control the automatic driving vehicle to execute transverse motion, take the speed and the road condition into consideration, is high in universality and good in calculation power compatibility, further ensure the stability of the transverse motion of the automatic driving vehicle, ensure the driving safety of the automatic driving vehicle, and further improve the driving experience of a user. Therefore, the technical problems that in the related art, in order to ensure the stability of the transverse control of the vehicle, when a preview point selection model is established, a large amount of real data is needed for supporting, the consumed time is long, and a large amount of calculation power is needed are solved.
Specifically, fig. 1 is a schematic flowchart of a lateral control method of an autonomous vehicle according to an embodiment of the present disclosure.
As shown in fig. 1, the lateral control method of the autonomous vehicle includes the steps of:
in step S101, the actual vehicle speed of the autonomous vehicle and the lane line information of the current traveling lane are acquired.
In the actual implementation process, the embodiment of the application can acquire lane line information, information such as a vehicle speed, a yaw rate, a steering wheel turn center deviation, a vehicle global coordinate and a course, wherein the lane line information can include information such as a curvature, a lateral deviation, a yaw rate and a course of an expected track, and can be a target track expected to be followed, and the track needs to be converted under a vehicle coordinate system, namely, the front side of the vehicle can be the positive direction of an x axis, and the left side can be the positive direction of a y axis.
In step S102, the preview time and the minimum preview distance are determined according to the actual vehicle speed and the lane line information, and the preview distance and the curvature at the preview point are calculated according to the preview time and the minimum preview distance.
As a possible implementation manner, the embodiment of the application may determine the preview time and the minimum preview distance according to the actual vehicle speed information and the lane line information, and further may adjust the sampling frequency according to the vehicle speed signal, and further calculate the preview distance and the curvature at the preview point.
Optionally, in an embodiment of the present application, determining the preview time and the minimum preview distance according to the actual vehicle speed and the lane information includes: detecting an actual curve state of the autonomous vehicle according to the lane line information; and matching the corresponding weight based on the actual curve state, and calculating the pre-aiming point curvature and the upper period curvature of the target track and the weighted curvature of the weight.
It is understood that the technical means of the driver model control of the automatic driving can be to calculate a desired vehicle movement track and make the error between the real track and the desired track smaller than the value allowed by safe driving by following the desired track.
In some embodiments, it may be detected whether a pre-pointing point of a target trajectory of the autonomous vehicle is in a curve based on curvature information in the lane line information, and then a curvature of the pre-pointing point of the target trajectory and a weighted curvature of the upper period curvature and the weight are calculated according to an actual curve state matching the corresponding weight, thereby obtaining a pre-pointing time and a minimum pre-pointing distance of the target trajectory in the curve state.
Optionally, in an embodiment of the present application, the determining a preview time and a minimum preview distance according to the actual vehicle speed and the lane information further includes: and inquiring a preset relation table according to the curvature of the pre-aiming point of the target track and the weighted curvatures of the upper period curvature and the weight to obtain pre-aiming time and the minimum pre-aiming distance.
Specifically, according to the embodiment of the application, based on the preset relation table, the curvature of the preview point of the target track, the curvature of the last period and the weighted curvature of the weight are obtained, and then preview time and the minimum preview distance are obtained, wherein the preview distance may be greater than or equal to the minimum preview distance and smaller than the maximum preview distance.
It should be noted that the preset relationship table may be configured by querying through big data or by a person skilled in the art according to actual situations, and is not limited herein.
In step S103, the steering angle of the autonomous vehicle is calculated based on the curvature and a preset coefficient, and a desired turning angle is calculated in conjunction with the actual yaw rate, and the autonomous vehicle is controlled to perform a lateral maneuver according to the desired turning angle.
In the actual implementation process, the embodiment of the application can obtain the steering angle based on the pre-aiming curvature multiplied by the coefficient, and in addition, the feedback angle calculated according to the yaw rate is obtained to obtain the expected corner, so that the automatic driving vehicle is controlled to execute the transverse action according to the expected corner, the vehicle speed and the road condition can be considered, the universality is high, the compatibility of calculation power is better, the stability of the transverse action of the automatic driving vehicle is ensured, the driving safety of the automatic driving vehicle is ensured, and the driving experience of a user is improved.
Optionally, in an embodiment of the present application, after calculating the desired turning angle in combination with the actual yaw rate, the method further includes: carrying out interpolation and filtering processing on the expected rotation angle to obtain a processed expected rotation angle; and generating a final expected corner based on the processed expected corner by using a preset function safety limit strategy, and outputting the final expected corner to a vehicle CAN network.
As a possible implementation manner, the embodiment of the application may perform interpolation and filtering processing on the expected corner, perform functional safety limitation on the corner and the rotation speed by using a preset functional safety limitation strategy to generate a final expected corner, and finally output the final expected corner to the CAN bus, thereby ensuring that the output result CAN effectively control the lateral motion of the autonomous vehicle and ensure the lateral control stability of the autonomous vehicle.
Optionally, in an embodiment of the present application, the calculation formula of the desired rotation angle is:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * To desired angle of rotation, p * The curvature at the predicted point of the desired trajectory is determined, K is the stability factor determined from the vehicle speed, v is the vehicle speed, i is the steering gear ratio of the vehicle, L is the wheel base of the vehicle, δ (EE) is the median deviation value of the steering wheel angle, and δ (ω) is the feedback value calculated from the yaw rate of the vehicle.
It can be understood that the track from the current position to the pre-aiming point in a uniform circular motion can be called as an expected track, the time for the vehicle to reach the pre-aiming point is short, and the process of the motion can be simplified and decomposed into a uniform acceleration motion with a uniform motion in the X-axis direction, an initial velocity of 0 and an acceleration of ay in the Y-axis direction.
Further according to:
ey＝Y(d)，
Figure BDA0003796024590000061
the following were obtained:
Figure BDA0003796024590000062
then according to the following steps:
Figure BDA0003796024590000063
Figure BDA0003796024590000064
the following can be found:
Figure BDA0003796024590000065
wherein ey is a deviation value between a preview point and a y direction in front of the autonomous vehicle at d, d is a preview distance, v is a vehicle speed in an x direction of the vehicle, ay is a lateral acceleration of the autonomous vehicle, R is a radius of an expected track, and rho * Is the curvature of the desired trajectory, wherein the curvature of the desired trajectory and the curvature at the home-point are considered equal.
And then according to the formula:
δ＝ρ * (1+Kv 2 )iL，
delta, the steering wheel angle that the vehicle needs to turn to reach the pre-aim point, can be calculated.
Where δ is the feed forward rotation angle, ρ * The curvature at the predicted point of the desired trajectory, K is the stability factor identified from the vehicle speed, v is the vehicle speed, i is the steering gear ratio of the autonomous vehicle, and L is the wheelbase of the autonomous vehicle.
In addition, as the autonomous vehicle runs, there is a deviation value in the steering gear, and an angle, namely δ (EE), needs to be compensated, and during the vehicle moving process, the feedback angle δ (ω) needs to be calculated according to the yaw rate ω, so that the desired steering wheel angle needed by the autonomous vehicle from the starting point to the pre-aiming point can be:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)。
the method for controlling the lateral direction of the autonomous vehicle according to the embodiment of the present application will be described in detail with reference to fig. 2 and 3.
As shown in fig. 2, where 1 is a point where the preview distance d corresponds to the target track, i.e., the preview point, 2 is the target track, 3 is the preview distance d,4 is the desired track, 5 is the autonomous vehicle, and 6 is the preview point and the y-direction offset value at the front of the autonomous vehicle, d.
In the actual implementation process, the embodiment of the application can acquire the lane line information, the vehicle speed, the yaw rate, the steering wheel turn angle median deviation, the global coordinate and the course of the automatic driving vehicle and other information, convert the lane line into the coordinate system of the automatic driving vehicle, take the rear axle center of the automatic driving vehicle as the origin, take the right front as the positive direction of the X axis, take the left side as the positive direction of the Y axis, and take the equation of the target track as follows:
Y(X)＝a 0 +a 1 X+a 2 X 2 +a 3 X 3 。
furthermore, the method and the device can determine the preview time and the minimum preview distance according to the lane line information and the vehicle speed information, and determine the preview distance d according to the preview time and the vehicle speed.
The method for confirming the curvature of the preview point can be as shown in fig. 3:
s301: the embodiment of the application can acquire information such as lane lines, vehicle speed, maximum pre-aiming distance and the like.
S302: according to the embodiment of the application, the curvature of the pre-aiming point and the curvature of the current point of the target track can be solved according to the lane line, and whether the vehicle enters the pre-aiming point and enters the curve entering state or not is judged.
S303 and S304: the embodiment of the application can calculate the weighted curvature of the pre-aiming point of the target track, for example, when the vehicle enters a curve, the curvature weight of the pre-aiming point at the current period can be 0.2, the curvature weight of the upper period is 0.8, when the vehicle exits the curve, the curvature weight of the pre-aiming point at the current period is 0.05, and the curvature weight of the upper period is 0.95.
S305: according to the embodiment of the application, the preview time T and the minimum preview distance Mindis can be obtained by looking up a table according to the calculated target track preview point weighting curvature.
S306: the embodiment of the application may calculate the preview distance d1= V × T + constminis, wherein constminis may be set to 10-15m.
S307: the embodiment of the application can calculate the pre-aiming distance d2, wherein d2 is a larger value of V × T and Mindis.
S308: according to the embodiment of the application, the minimum value of d1, d2 and Maxdis can be selected as the pre-aiming distance d, wherein the value of Maxdis can be 100m, so that operation errors caused by overlarge pre-aiming distance can be prevented. Under the low-speed condition, the pre-aiming distance is usually equal to d1, constMindis's effect lies in guaranteeing that the pre-aiming point is in the locomotive the place ahead of autopilot, and when the autopilot is about to get into under the condition of curvature bend, the pre-aiming distance is usually equal to minimum pre-aiming distance Mindis according to the curvature look-up table, and the near pre-aiming distance can let autopilot have better effect of passing through to curvature bend.
S309: according to the embodiment of the application, the expected preview curvature rho can be calculated according to the preview distance d.
Because the time of the vehicle reaching the pre-aiming point is short, the motion process can be simplified and decomposed into uniform acceleration motion with uniform motion in the X-axis direction, initial velocity of 0 in the Y-axis direction and acceleration of ay.
Further according to:
ey＝Y(d)，
Figure BDA0003796024590000081
the following were obtained:
Figure BDA0003796024590000082
and then according to:
Figure BDA0003796024590000083
Figure BDA0003796024590000084
the following can be found:
Figure BDA0003796024590000085
wherein ey is a deviation value between the preview point and the front of the autonomous vehicle in the y direction d, d is a preview distance, v is the vehicle speed in the x direction of the vehicle, ay is the lateral acceleration of the autonomous vehicle, R is the radius of the expected track, and ρ is the radius of the expected track * Is the curvature of the desired trajectory, wherein the curvature of the desired trajectory and the curvature at the home point are considered equal.
And then according to the formula:
δ＝ρ * (1+Kv 2 )iL，
delta, the steering wheel angle that the vehicle needs to turn to reach the pre-aim point, can be calculated.
Where δ is the feed forward rotation angle, ρ * The curvature at the predicted point of the desired trajectory, K is the stability factor identified from the vehicle speed, v is the vehicle speed, i is the steering gear ratio of the autonomous vehicle, and L is the wheelbase of the autonomous vehicle.
In addition, as the autonomous vehicle runs, a deviation value exists in the steering gear, an angle needs to be compensated, namely δ (EE), during the moving process of the vehicle, a feedback angle δ (ω) needs to be calculated according to a yaw rate ω, and therefore, the expected steering wheel rotation angle needed by the autonomous vehicle from a starting point to a pre-aiming point can be as follows:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)。
according to the transverse control method of the automatic driving vehicle, the pre-aiming time and the minimum pre-aiming distance can be determined based on the actual speed of the automatic driving vehicle and the lane line information of the current driving lane, the pre-aiming distance and the curvature of the pre-aiming point are calculated and obtained, the steering angle of the automatic driving vehicle is obtained, the expected turning angle is calculated by combining the actual yaw rate, the automatic driving vehicle is controlled to execute transverse motion, the speed and the road condition can be considered, the universality is high, the calculation capacity compatibility is good, the transverse motion stability of the automatic driving vehicle is guaranteed, the driving safety of the automatic driving vehicle is guaranteed, and the driving experience of a user is improved. Therefore, the technical problems that in the related art, in order to ensure the stability of the transverse control of the vehicle, when a preview point selection model is established, a large amount of real data is needed for supporting, the consumed time is long, and a large amount of calculation power is needed are solved.
Next, a lateral control device of an autonomous vehicle according to an embodiment of the present application will be described with reference to the drawings.
Fig. 4 is a block schematic diagram of a lateral control device of an autonomous vehicle according to an embodiment of the present application.
As shown in fig. 4, the lateral control device 10 of the autonomous vehicle includes: an acquisition module 100, a calculation module 200 and a control module 300.
Specifically, the acquiring module 100 is configured to acquire an actual vehicle speed of the autonomous vehicle and lane line information of a current driving lane.
And the calculation module 200 is configured to determine a preview time and a minimum preview distance according to the actual vehicle speed and the lane line information, and calculate a preview distance and a curvature at a preview point according to the preview time and the minimum preview distance.
A control module 300 for calculating a steering angle of the autonomous vehicle based on the curvature and a preset coefficient, and calculating a desired turning angle in combination with the actual yaw rate, and controlling the autonomous vehicle to perform a lateral maneuver according to the desired turning angle.
Optionally, in an embodiment of the present application, the lateral control device 10 of the autonomous vehicle further includes: the device comprises a processing module and a generating module.
The processing module is used for carrying out interpolation and filtering processing on the expected rotation angle to obtain the processed expected rotation angle.
And the generating module is used for generating a final expected corner based on the processed expected corner by using a preset functional safety limit strategy and outputting the final expected corner to the vehicle CAN network.
Optionally, in an embodiment of the present application, the calculation formula of the desired rotation angle is:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * To desired rotation angle, p * The curvature at the predicted point of the desired trajectory, K the stability factor determined from the vehicle speed, v the vehicle speed,i is the steering gear ratio of the vehicle, L is the wheel base of the vehicle, δ (EE) is the median deviation value of the steering wheel angle, and δ (ω) is the feedback value calculated from the yaw rate of the vehicle.
Optionally, in an embodiment of the present application, the computing module 200 includes: a detection unit and a calculation unit.
The detection unit is used for detecting the actual curve state of the automatic driving vehicle according to the lane line information.
And the calculating unit is used for matching the corresponding weight based on the actual curve state and calculating the curvature of the pre-aiming point of the target track, the curvature of the upper period and the weighted curvature of the weight.
Optionally, in an embodiment of the present application, the computing module 200 further includes: and querying the unit.
The query unit is used for querying a preset relation table according to the curvature of the preview point of the target track, the curvature of the upper period and the weighted curvature of the weight to obtain preview time and the minimum preview distance.
It should be noted that the foregoing explanation of the embodiment of the lateral control method of the autonomous vehicle is also applicable to the lateral control device of the autonomous vehicle of this embodiment, and will not be described again here.
According to the transverse control device of the automatic driving vehicle, the pre-aiming time and the minimum pre-aiming distance can be determined based on the actual speed of the automatic driving vehicle and the lane line information of the current driving lane, the curvature of the pre-aiming distance and the pre-aiming point can be calculated and obtained, the steering angle of the automatic driving vehicle can be obtained, the expected turning angle can be calculated by combining the actual yaw rate, the automatic driving vehicle is controlled to execute transverse motion, the speed and the road condition can be considered, the universality is high, the calculation capacity compatibility is good, the transverse motion stability of the automatic driving vehicle is guaranteed, the driving safety of the automatic driving vehicle is guaranteed, and the driving experience of a user is improved. Therefore, the technical problems that in the related art, in order to ensure the stability of the transverse control of the vehicle, when a preview point selection model is established, a large amount of real data is needed for supporting, the consumed time is long, and a large amount of calculation power is needed to be consumed are solved.
Fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
memory 501, processor 502, and computer programs stored on memory 501 and executable on processor 502.
The processor 502, when executing the program, implements the lateral control method of the autonomous vehicle provided in the above-described embodiments.
Further, the vehicle further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
A memory 501 for storing computer programs that can be run on the processor 502.
The memory 501 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Alternatively, in practical implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 may complete communication with each other through an internal interface.
The processor 502 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the lateral control method of an autonomous vehicle as above.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A lateral control method of an autonomous vehicle, characterized by comprising the steps of:
acquiring the actual speed of an automatic driving vehicle and lane line information of a current driving lane;
determining a pre-aiming time and a minimum pre-aiming distance according to the actual vehicle speed and the lane line information, and calculating the pre-aiming distance and the curvature at a pre-aiming point according to the pre-aiming time and the minimum pre-aiming distance; and
calculating a steering angle of the autonomous vehicle based on the curvature and a preset coefficient, calculating a desired turning angle in combination with an actual yaw rate, and controlling the autonomous vehicle to perform a lateral maneuver according to the desired turning angle.
2. The method of claim 1, further comprising, after calculating the desired turn angle in conjunction with the actual yaw rate:
carrying out interpolation and filtering processing on the expected rotation angle to obtain a processed expected rotation angle;
and generating a final expected corner based on the processed expected corner by using a preset function safety limiting strategy, and outputting the final expected corner to a vehicle CAN network.
3. The method of claim 1, wherein the desired rotation angle is calculated by the formula:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * Is the desired angle of rotation, p * The curvature at the predicted point of the desired trajectory, K is a stability coefficient determined from the vehicle speed, v is the vehicle speed, i is the steering gear ratio of the vehicle, L is the wheel base of the vehicle, δ (EE) is the median offset of the steering wheel angle, and δ (ω) is a feedback value calculated from the yaw rate of the vehicle.
4. The method of claim 1, wherein determining a preview time and a minimum preview distance based on the actual vehicle speed and the lane information comprises:
detecting an actual curve state of the autonomous vehicle according to the lane line information;
and matching corresponding weights based on the actual curve state, and calculating the curvature of a pre-aiming point and the curvature of an upper period of the target track and the weighted curvature of the weights.
5. The method of claim 4, wherein determining a look-ahead time and a minimum look-ahead distance based on the actual vehicle speed and the lane information further comprises:
and inquiring a preset relation table according to the pre-aiming point curvature and the upper period curvature of the target track and the weighted curvature of the weight to obtain the pre-aiming time and the minimum pre-aiming distance.
6. A lateral control apparatus for an autonomous vehicle, comprising:
the acquisition module is used for acquiring the actual speed of the automatic driving vehicle and the lane line information of the current driving lane;
the calculation module is used for determining the preview time and the minimum preview distance according to the actual speed and the lane line information, and calculating the preview distance and the curvature at a preview point according to the preview time and the minimum preview distance; and
and the control module is used for calculating the steering angle of the automatic driving vehicle based on the curvature and a preset coefficient, calculating a desired corner by combining the actual yaw rate, and controlling the automatic driving vehicle to execute a transverse action according to the desired corner.
7. The apparatus of claim 6, further comprising:
the processing module is used for carrying out interpolation and filtering processing on the expected corner to obtain a processed expected corner;
and the generating module is used for generating a final expected corner based on the processed expected corner by using a preset functional safety limit strategy and outputting the final expected corner to a vehicle CAN network.
8. The apparatus of claim 6, wherein the desired rotation angle is calculated by:
δ * ＝ρ * (1+Kv 2 )iL+δ(EE)+δ(ω)，
wherein, delta * Is the desired angle of rotation, p * The curvature at the predicted point of the expected track is K, the stability coefficient confirmed according to the vehicle speed is V, the vehicle speed is I, the steering transmission ratio of the vehicle is I, the wheel base of the vehicle is L, the middle deviation value of the steering wheel angle is delta (EE), and the feedback value calculated according to the yaw velocity of the vehicle is delta (omega).
9. A vehicle, characterized by comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the lateral control method of an autonomous vehicle as claimed in any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing a lateral control method of an autonomous vehicle as claimed in any of claims 1-5.
CN202210969583.3A 2022-08-12 2022-08-12 Lateral control method and device for autonomous vehicle, vehicle and storage medium Pending CN115366876A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115593439A (en) * 2022-11-25 2023-01-13 小米汽车科技有限公司（Cn） Vehicle control method, vehicle control device, vehicle and storage medium
CN116039640A (en) * 2023-01-28 2023-05-02 广汽埃安新能源汽车股份有限公司 Vehicle over-bending deceleration control method and device, electronic equipment and storage medium
CN116572972A (en) * 2023-07-03 2023-08-11 中国第一汽车股份有限公司 Transverse control method and device of vehicle, electronic equipment and storage medium
CN117549897A (en) * 2023-12-28 2024-02-13 上海保隆汽车科技股份有限公司 Vehicle over-bending control method, system, storage medium and electronic equipment

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115593439A (en) * 2022-11-25 2023-01-13 小米汽车科技有限公司（Cn） Vehicle control method, vehicle control device, vehicle and storage medium
CN116039640A (en) * 2023-01-28 2023-05-02 广汽埃安新能源汽车股份有限公司 Vehicle over-bending deceleration control method and device, electronic equipment and storage medium
CN116572972A (en) * 2023-07-03 2023-08-11 中国第一汽车股份有限公司 Transverse control method and device of vehicle, electronic equipment and storage medium
CN116572972B (en) * 2023-07-03 2024-06-14 中国第一汽车股份有限公司 Transverse control method and device of vehicle, electronic equipment and storage medium
CN117549897A (en) * 2023-12-28 2024-02-13 上海保隆汽车科技股份有限公司 Vehicle over-bending control method, system, storage medium and electronic equipment
CN117549897B (en) * 2023-12-28 2024-05-10 上海保隆汽车科技股份有限公司 Vehicle over-bending control method, system, storage medium and electronic equipment

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