CN116691668A - Transverse control method for large intelligent vehicle - Google Patents

Transverse control method for large intelligent vehicle Download PDF

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Publication number
CN116691668A
CN116691668A CN202210615555.1A CN202210615555A CN116691668A CN 116691668 A CN116691668 A CN 116691668A CN 202210615555 A CN202210615555 A CN 202210615555A CN 116691668 A CN116691668 A CN 116691668A
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vehicle
target
deviation
aiming
course angle
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李晓亚
李兴佳
朱敏
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Yutong Bus Co Ltd
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Yutong Bus 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
    • 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
    • B60W40/114—Yaw movement
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/14—Yaw
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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

Abstract

The application relates to a transverse control method of a large intelligent vehicle, and belongs to the technical field of automatic driving. The method comprises the following steps: acquiring the current position, the current course angle and the current vehicle speed of the vehicle in real time, and planning the running track of the vehicle, wherein the running track of the vehicle comprises the position and the course angle of each track point; determining a plurality of pre-aiming points by combining the current position in the running track of the vehicle, and determining a target transverse deviation and a target course angle deviation by combining the positions and course angles of the pre-aiming points, the current position and the current course angle and a weighted average mode; inputting the current speed, the target lateral deviation and the target course angle deviation into a space state equation; solving a space state equation to obtain a front wheel corner, and performing steering control according to the front wheel corner. The transverse control method is suitable for parking control of the large-sized vehicle, and tracking precision of the large-sized intelligent vehicle in a large-curvature S-bend in a parking scene is effectively ensured.

Description

Transverse control method for large intelligent vehicle
Technical Field
The application relates to a transverse control method of a large intelligent vehicle, and belongs to the technical field of automatic driving.
Background
The intelligent vehicle path tracking control is one of key technologies for realizing automatic driving, and has the main functions of controlling the front wheel corner of the vehicle, enabling the vehicle to run along a planned running track, reducing deviation between the controlled vehicle and the running track as much as possible, and tracking precision is a main target of the path tracking control.
The traditional path tracking method is based on the principle of vehicle kinematics, based on transverse deviation, adopts a high-order controller such as LQR and MPC for transverse control to obtain the optimal steering angle of the front wheel of the vehicle, and further controls the steering wheel steering angle of the vehicle, however, the tracking control precision is lower.
In order to improve tracking accuracy, control methods using lateral deviation and heading angle deviation as state quantities have been proposed, for example: the application publication No. CN 113608530A discloses a parameter self-tuning LQR path tracking method with PID corner compensation, wherein when front wheel steering angle calculation is carried out, the transverse deviation and heading deviation of a vehicle and a reference point are calculated through the position of the center of mass of the vehicle and the position of an actual center of mass reference point of the vehicle, the front wheel corner is calculated through the transverse deviation and the heading deviation, the actual pre-aiming reference point position of the vehicle is determined, the transverse deviation of the reference point and the pre-aiming reference point is calculated, the front wheel corner is compensated through the transverse deviation calculation, and the final front wheel steering angle control quantity is determined by combining the front wheel corner and the compensated front wheel corner.
However, for a parking scene of a large intelligent bus (more than 12 meters), the parking scene is limited by space, a large curvature S-bend exists in a parking track, the track shape is changeable, the requirements on the accuracy adaptability and the robustness of a tracking algorithm are very high, the optimization and the adaptation to the parking scene of the large intelligent bus cannot be seen, and therefore the method is difficult to be applied to the parking scene of the large intelligent bus (more than 12 meters), and therefore, a technical scheme suitable for high-accuracy transverse control of the large intelligent bus needs to be provided.
Disclosure of Invention
The application aims to provide a transverse control method of a large intelligent vehicle, which is used for solving the problem of poor transverse control precision in the prior art.
In order to achieve the above purpose, the present application provides a technical scheme of a lateral control method for a large intelligent vehicle, comprising the following steps:
1) Acquiring the current position, the current course angle and the current vehicle speed of the vehicle in real time, and planning the running track of the vehicle, wherein the running track of the vehicle comprises the position and the course angle of each track point;
2) Determining a plurality of pre-aiming points by combining the current position in the running track of the vehicle, and determining a target transverse deviation and a target course angle deviation by combining the positions and course angles of the pre-aiming points, the current position and the current course angle and a weighted average mode;
3) Inputting the current speed, the target lateral deviation and the target course angle deviation into a space state equation; the space state equation is established based on a vehicle dynamics model;
4) And solving a space state equation through a linear secondary adjustment control algorithm or a model prediction control algorithm to obtain a front wheel corner, and performing steering control according to the front wheel corner.
The technical scheme of the transverse control method of the large intelligent vehicle has the beneficial effects that: according to the application, the target transverse deviation and the target course angle deviation are determined through the states and the current states of a plurality of pre-aiming points, and then the target transverse deviation and the target course angle deviation are used as the input of a space state equation, and the front wheel corner is solved through a linear secondary regulation control algorithm or a model prediction control algorithm. The track trend can be better reflected by the plurality of pre-aiming points, and disturbance of the LQR algorithm caused by unsmooth track can be effectively avoided. Therefore, the transverse control method is suitable for parking control of the large-sized vehicle, and tracking precision of the large-sized intelligent vehicle in a large-curvature S-bend in a parking scene can be effectively ensured.
Further, in order to improve the transverse control precision, a plurality of pre-aiming points are determined through pre-aiming distances, and the point closest to the pre-aiming distances is found out from the running track of the vehicle to serve as the pre-aiming point, wherein the calculation process of the pre-aiming distances is as follows:
L m ＝v x t pre_m +L base_m ，
wherein L is m The pre-aiming distance of the m pre-aiming point; v x The current vehicle speed; t is t pre_m The pre-aiming time of the mth pre-aiming point; l (L) base_m Base for mth pretightening pointThe foundation is pre-aimed at a distance.
Further, in order to more accurately determine the target lateral deviation, the calculation process of the target lateral deviation is as follows:
wherein e cg Is the target lateral deviation;the transverse deviation between the position of the ith pre-aiming point and the current position is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
Further, in order to more accurately determine the target course angle deviation, the calculation process of the target course angle deviation is as follows:
wherein θ e The target course angle deviation is;the heading angle deviation of the i-th pre-aiming point and the current heading angle is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
Further, the spatial state equation is:
wherein the method comprises the steps of，e cg Is the target lateral deviation; θ e The target course angle deviation is; delta f Is the front wheel corner;is yaw rate; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Distance from front axis to mass center; i z Is the moment of inertia; l (L) r Distance from the rear axle to the mass center; v (V) x The vehicle speed is the vehicle speed; a is acceleration; m is the mass of the whole vehicle.
Further, in order to improve the tracking accuracy of the front wheel corner, the driving track of the vehicle in step 1) further includes a curvature of each track point, in step 2), a target curvature is determined by combining a weighted average mode through a plurality of curvatures of pre-aiming points, the front wheel corner is compensated through calculation of the target curvature, the front wheel corner obtained in step 4) is compensated through compensation of the front wheel corner, steering control is performed through the compensated front wheel corner, and the calculation process of the compensation front wheel corner is as follows:
wherein delta f ' compensate for front wheel rotation angle; l is the wheelbase; r is a turning radius; ρ e Is the target curvature; a, a y Is the lateral acceleration; k (k) 3 The target course angle deviation weight coefficient is; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Distance from front axis to mass center; l (L) r Distance from the rear axle to the mass center; v (V) x The vehicle speed is the vehicle speed; m is the mass of the whole vehicle.
Further, in order to more accurately determine the target curvature, the calculation process of the target curvature is as follows:
wherein ρ is e Is the target curvature;curvature for the ith pretightening point; n is the number of pre-aiming points; k (k) window Is a weight window.
Further, in order to ensure tracking accuracy, the method further includes the step of determining the longitudinal vehicle speed:
when the target lateral deviation exceeds the lateral deviation threshold, or the target course angle deviation exceeds the course angle deviation threshold, or the target curvature exceeds the curvature threshold, the longitudinal vehicle speed does not exceed the low vehicle speed value.
Further, in order to ensure the final attitude precision of the large-sized vehicle parking space, weight coefficients of all state quantities in the linear secondary adjustment control algorithm are determined according to the transverse deviation precision and the course angle deviation precision during parking, wherein the state quantities comprise transverse deviation, course angle deviation, transverse deviation change rate and course angle deviation change rate.
Further, in order to ensure accuracy of pretightening-point determination, pretightening time is positively correlated with vehicle speed, and a basic pretightening distance is determined according to curvature.
Drawings
FIG. 1 is a flow chart of a lateral control method of a large intelligent vehicle of the present application;
FIG. 2 is a schematic illustration of the calculation of lateral and heading angle deviations of the pretightening point from the current location of the present application.
Detailed Description
Lateral control method embodiment of large intelligent vehicle:
the application mainly aims at solving the problem that the existing transverse control is not suitable for a large-sized vehicle, determining a plurality of pre-aiming points in a planned running track, determining target transverse deviation through the positions and the current positions of the plurality of pre-aiming points, determining target course angle deviation through the course angles and the current course angles of the plurality of pre-aiming points, further solving a front wheel corner through the combination of the target transverse deviation and the target course angle deviation with a space state equation, and further realizing transverse control of the large-sized intelligent vehicle.
Specifically, as shown in fig. 1, the lateral control method of the large intelligent vehicle comprises the following steps:
1) The current state of the vehicle is acquired in real time, and the running track of the vehicle is planned.
The current state of the vehicle includes a current position, a current heading angle, and a current vehicle speed, and may be acquired by various sensors provided on the vehicle.
The travel track of the vehicle includes a position, a heading angle, and a curvature of each track point.
The planning of the running track of the vehicle belongs to the prior art, and in this embodiment, the detailed running track planning method may refer to the prior art document: "study of local trajectory planning and tracking control algorithm for Intelligent vehicles" He Huiling, university of Harbin industry, 2021, month 6.
2) And (3) inputting the planned running track and the current position of the vehicle into a track pre-aiming model to determine the positions of a plurality of pre-aiming points, and determining the position, the course angle and the curvature of each pre-aiming point by combining the running track.
The track pre-aiming model is used for determining a plurality of pre-aiming points in the running track of the vehicle, and the positions of the plurality of pre-aiming points are determined as follows:
firstly, determining the number of pretightening points and pretightening distance of each pretightening point;
in general, the number of pretightening points is about 8, and the pretightening distance of each pretightening point is related to the pretightening time of each pretightening point and the basic pretightening distance of each pretightening point;
the calculation process of the pre-aiming distance comprises the following steps:
L m ＝v x t pre_m +L base_m ，
wherein L is m The pre-aiming distance of the m pre-aiming point; v x The current vehicle speed; t is t pre_m The pre-aiming time of the mth pre-aiming point; l (L) base_m And the base pretightening distance of the mth pretightening point. t is t pre_m The setting of (2) is positively correlated with the current vehicle speed, the faster the vehicle speed, the larger the pre-aiming time is, and the pre-aiming time is obtained by calibration, for example: the speed of the vehicle is from 0 to 70km/h, the step length is 10km/h, the speed values from 0 to 70km/h are calibrated for a pre-aiming time according to the corresponding speed values of the set step length, and then the pre-aiming time of each speed value is obtained by adopting a linear difference method. The determination of the basic pretightening distance is related to the curvature of a section of track and the sequence of pretightening points, the curvature can be the maximum value of the curvature of the whole track, and the basic pretightening distance is given according to the curvature section.
Next, a point closest to the pretightening distance is found in the travel locus of the vehicle as a pretightening point based on the current position of the vehicle (x 0 ，y 0 ) And a track point (x n ，y n ) Distance s between n The calculation formula is as follows:s n the track point whose value is closest to the pretightening distance is selected as the corresponding pretightening point.
Of course, the pretightening distance of each pretightening point need not be calculated by the above formula, for example: taking 6 pretightening points as an example, the pretightening distance of the 1 st pretightening point is a fixed distance, for example, 1 meter determined by calibration, the pretightening distances of the 2 nd pretightening point and the 6 th pretightening point are calculated by adopting the formula, and the L of the 2 nd pretightening point base_2 And t pre_2 The value is as follows: when the curvature of a segment of the track is greater than 0.03 (as determined by calibration), L base_2 Take the value of 1.5m, otherwise L base_2 The value is 2m, t pre_2 Is a fixed value of 0.1s; the 6 th pretightening point L base_6 And t pre_6 The values of (2) are as follows: when the curvature of a segment of the track is greater than 0.03, L base_6 =5m, otherwise L base_6 The value is 8m, t pre_6 The value is 0.3s; the pretightening distance of the 3 rd pretightening point, the 4 th pretightening point and the 5 th pretightening point is obtained by linear interpolation of the pretightening distance of the 2 nd pretightening point and the 6 th pretightening point.
In order to reduce the calculated amount and improve the control speed, the determination of the pre-aiming points does not need to be updated in real time according to the real-time current position, and only the current position of the vehicle is needed to be very close to the position of the 1 st pre-aiming point determined last time, and the pre-aiming points are re-determined by combining with the current position.
3) Determining the corresponding transverse deviation of each pretightening point according to the position and the current position of each pretightening point, determining the corresponding course angle deviation of each pretightening point according to the course angle and the current course angle of each pretightening point, carrying out weighted average on the corresponding transverse deviations of a plurality of pretightening points to obtain a target transverse deviation, carrying out weighted average on the corresponding course angle deviations of a plurality of pretightening points to obtain a target transverse deviation, and determining the target curvature according to the curvature of each pretightening point.
The calculation process of the target lateral deviation is as follows:
wherein e cg Is the target lateral deviation;the transverse deviation between the position of the ith pre-aiming point and the current position is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
The calculation process of the target course angle deviation comprises the following steps:
wherein θ e The target course angle deviation is;the heading angle deviation of the i-th pre-aiming point and the current heading angle is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
Taking a certain pretightening point as an example, the calculation process of the corresponding transverse deviation and course angle deviation of each pretightening point is described in detail:
as shown in FIG. 2, the position of the ith pretightening point is set as (x des ，y des ) The heading angle of the ith pre-aiming point isThe current position is (x, y), the current heading angle is +.>The method comprises the following steps of:
wherein d x The difference value of the x axis of the position of the ith pre-aiming point and the current position is obtained; d, d y The difference value of the y axis of the position of the ith pre-aiming point and the current position is obtained;the transverse deviation between the position of the ith pre-aiming point and the current position is obtained; e, e y Longitudinal deviation of the position of the ith pre-aiming point and the current position; />The heading angle deviation of the ith pre-aiming point and the current heading angle is obtained.
The calculation process of the target curvature comprises the following steps:
wherein ρ is e Is the target curvature;curvature for the ith pretightening point; n is the number of pre-aiming points; k (k) window Is a weight window.
4) And inputting the current vehicle speed, the target transverse deviation and the target course angle deviation into a space state equation, and solving the front wheel corner.
The space state equation is established based on the vehicle dynamics model, and the parking scene belongs to a low-speed running scene, so that the requirement can be met by selecting a two-wheel bicycle model, and the space state equation is as follows:
wherein e cg Is the target lateral deviation; θ e The target course angle deviation is; delta f Is the front wheel corner;is yaw rate; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Is in front ofAxis to centroid distance; i z Is the moment of inertia; l (L) r Distance from the rear axle to the mass center; v (V) x The vehicle speed is the vehicle speed; a is acceleration; m is the mass of the whole vehicle.
In the space state equation, letx is a state quantity matrix, let->u is a control quantity, A is a state quantity coefficient matrix, and B is a control quantity coefficient matrix.
Solving a space state equation through a linear quadratic adjustment control algorithm LQR or a model predictive control algorithm MPC to obtain a front wheel corner, wherein the LQR is adopted in the embodiment, and aims to find out a group of control quantity so as to minimize the state quantity and the control quantity of the system, and the following cost function J is defined:
wherein Q is a state quantity weight matrix; r is a control quantity weight matrix; q (Q) f The state quantity weight matrix is the final state quantity weight matrix; n is the number of control sequences that reach the final state.
The state quantity weight matrix Q is defined as follows:
k 1 is a lateral deviation weight coefficient; k (k) 2 The weight coefficient is the transverse deviation change rate; k (k) 3 The course angle deviation weight coefficient is used; k (k) 4 And the course angle deviation change rate weight coefficient is used.
The state quantity weight matrix Q is determined according to the transverse deviation precision and the course angle deviation precision when parking, the transverse deviation is about 0.12m, the course angle deviation is about 0.03 (radian) according to the control precision requirement, and the weight coefficient setting principle is adopted: the larger the weight coefficient is, the smaller the corresponding deviation is, the transverse deviation weight coefficient is smaller than the course angle deviation weight coefficient, and the ratio of the transverse deviation weight coefficient to the course angle deviation weight coefficient is 1:4. In the present embodiment, the lateral deviation change rate weight coefficient and the heading angle deviation change rate weight coefficient are set to 0.
The specific solving process of the LQR control algorithm is the prior art, and will not be described herein.
5) And (3) inputting the target curvature into a track curvature compensation model to obtain a compensated front wheel corner, compensating the front wheel corner obtained in the step (4) through the compensated front wheel corner, and further controlling steering through the compensated front wheel corner.
The track curvature compensation model is used for calculating a compensation front wheel corner, and the calculation process of the compensation front wheel corner is as follows:
wherein delta f ' compensate for front wheel rotation angle; l is the wheelbase; r is a turning radius; ρ e Is the target curvature; a, a y Is the lateral acceleration; k (k) 3 The course angle deviation weight coefficient is used; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Distance from front axis to mass center; l (L) r Distance from the rear axle to the mass center; v (V) x The vehicle speed is the vehicle speed; m is the mass of the whole vehicle.
Compensated front wheel steering angle = delta f δ+δ f 。
6) And inputting the target transverse deviation, the target course angle deviation and the target curvature into a vehicle speed control model to determine the longitudinal vehicle speed.
The vehicle speed control model is used for determining the longitudinal speed of the vehicle, when the target transverse deviation exceeds a transverse deviation threshold value, or the target course angle deviation exceeds a course angle deviation threshold value, or the target curvature exceeds a curvature threshold value, the longitudinal speed does not exceed a low vehicle speed value, the low vehicle speed value is 2km/h, more adjustment time is provided for transverse control, and tracking precision is ensured. Of course, when the longitudinal vehicle speed is not high during parking of the large vehicle, the control of the longitudinal vehicle speed may not be performed. In this embodiment, the lateral deviation threshold is set to 3 times the lateral deviation required for parking accuracy; the heading angle deviation threshold is set to be 2 times of the heading angle deviation required by parking precision.
The curvature threshold value is generally selected to be 0.07, and is related to the vehicle parameters (minimum turning radius and maximum lateral acceleration) and the vehicle response performance (response time of steering wheel angle), as a calibration quantity according to the vehicle parameters and the vehicle response performance.
In the above embodiment, the calculating process of the target lateral deviation and the target heading angle deviation includes calculating the deviation between each pre-aiming point and the current state of the vehicle, and then calculating the target lateral deviation and the target heading angle deviation according to the weighted average of the deviations.
In the above embodiment, the track curvature compensation model is used to compensate the front wheel steering angle, and the target curvature is used as the input value to calculate the compensated front wheel steering angle, and as other embodiments, the compensation may not be performed or the curvature of one of the pretightening points is directly selected as the input value to calculate the compensated front wheel steering angle under the condition of ensuring the accuracy of the front wheel steering angle.
In the above embodiment, in order to improve accuracy of the lateral control, the plurality of pre-aiming points are determined by the pre-aiming distance, and as other embodiments, a plurality of track points may be selected as the pre-aiming points directly at a set distance in front of the current position on the driving track.
The transverse control method is suitable for parking control of the large-sized vehicle, and tracking precision of the large-sized intelligent vehicle in a large-curvature S-bend in a parking scene is effectively ensured.

Claims (10)

1. The transverse control method of the large intelligent vehicle is characterized by comprising the following steps of:
1) Acquiring the current position, the current course angle and the current vehicle speed of the vehicle in real time, and planning the running track of the vehicle, wherein the running track of the vehicle comprises the position and the course angle of each track point;
2) Determining a plurality of pre-aiming points by combining the current position in the running track of the vehicle, and determining a target transverse deviation and a target course angle deviation by combining the positions and course angles of the pre-aiming points, the current position and the current course angle and a weighted average mode;
3) Inputting the current speed, the target lateral deviation and the target course angle deviation into a space state equation; the space state equation is established based on a vehicle dynamics model;
4) And solving a space state equation through a linear secondary adjustment control algorithm or a model prediction control algorithm to obtain a front wheel corner, and performing steering control according to the front wheel corner.
2. The transverse control method of a large intelligent vehicle according to claim 1, wherein a plurality of pre-aiming points are determined by pre-aiming distances, and a point closest to the pre-aiming distances is found in a running track of the vehicle as the pre-aiming point, and the calculation process of the pre-aiming distances is as follows:
L m ＝v x t pre_m +L base_m ；
wherein L is m The pre-aiming distance of the m pre-aiming point; v x The current vehicle speed; t is t pre_m The pre-aiming time of the mth pre-aiming point; l (L) base_m And the base pretightening distance of the mth pretightening point.
3. The lateral control method of a large intelligent vehicle according to claim 1, wherein the calculation process of the target lateral deviation is:
wherein e cg Is the target lateral deviation;the transverse deviation between the position of the ith pre-aiming point and the current position is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
4. The lateral control method of a large intelligent vehicle according to claim 1, wherein the calculation process of the target heading angle deviation is:
wherein θ e The target course angle deviation is;the heading angle deviation of the i-th pre-aiming point and the current heading angle is obtained; n is the number of pre-aiming points; k (k) window Is a weight window.
5. The lateral control method of a large intelligent vehicle according to claim 1, wherein the spatial state equation is:
wherein e cg Is the target lateral deviation; θ e The target course angle deviation is; delta f Is the front wheel corner;is yaw rate; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Distance from front axis to mass center; i z Is the moment of inertia; l (L) r Distance from the rear axle to the mass center; v (V) x The vehicle speed is the vehicle speed; a is acceleration; m is the mass of the whole vehicle.
6. The lateral control method of a large intelligent vehicle according to claim 1, wherein the driving track of the vehicle in step 1) further includes a curvature of each track point, in step 2), a target curvature is determined by combining a weighted average mode through a plurality of curvatures of the pretightening points, a compensated front wheel corner is calculated through the target curvature, the front wheel corner obtained in step 4) is compensated through the compensated front wheel corner, steering control is performed through the compensated front wheel corner, and the calculation process of the compensated front wheel corner is as follows:
wherein delta f ' compensate for front wheel rotation angle; l is the wheelbase; r is a turning radius; ρ e Is the target curvature; a, a y Is the lateral acceleration; k (k) 3 The target course angle deviation weight coefficient is; c (C) f The front axle tire sidewall bias stiffness; c (C) r The tire sidewall deflection rigidity of the rear axle is achieved; l (L) f Distance from front axis to mass center; r distance from the rear axle to the mass center; x the vehicle speed is the vehicle speed; m is the mass of the whole vehicle.
7. The lateral control method of a large intelligent vehicle according to claim 6, wherein the calculation process of the target curvature is:
wherein ρ is e Is the target curvature;curvature for the ith pretightening point; n is the number of pre-aiming points; k (k) window Is a weight window.
8. The lateral control method of a large intelligent vehicle according to claim 6, further comprising the step of determining a longitudinal vehicle speed:
when the target lateral deviation exceeds the lateral deviation threshold, or the target course angle deviation exceeds the course angle deviation threshold, or the target curvature exceeds the curvature threshold, the longitudinal vehicle speed does not exceed the low vehicle speed value.
9. The lateral control method of a large intelligent vehicle according to claim 1, wherein the weight coefficient of each state quantity in the linear secondary adjustment control algorithm is determined according to the lateral deviation accuracy and the heading angle deviation accuracy when parking, and the state quantity includes the lateral deviation, the heading angle deviation, the lateral deviation change rate, and the heading angle deviation change rate.
10. The lateral control method of a large intelligent vehicle according to claim 2, wherein the pre-aiming time is positively correlated with the vehicle speed, and the basic pre-aiming distance is determined according to the curvature.
CN202210615555.1A 2022-05-31 2022-05-31 Transverse control method for large intelligent vehicle Pending CN116691668A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118151661A (en) * 2024-05-07 2024-06-07 北京易控智驾科技有限公司 Unmanned vehicle lateral movement control method and device

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118151661A (en) * 2024-05-07 2024-06-07 北京易控智驾科技有限公司 Unmanned vehicle lateral movement control method and device

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