CN110825095B - Transverse control method for automatic driving vehicle - Google Patents
Transverse control method for automatic driving vehicle Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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Abstract
The invention relates to a transverse control method of an automatic driving vehicle, which comprises the following steps: 1) Acquiring a planned vehicle motion track and information of a vehicle; 2) Executing a transverse control algorithm, including control target point calculation, tracking error calculation and error compensation control; 3) And (3) converting the error compensation obtained in the step 2) into a steering wheel signal through a transmission ratio set for different vehicles in advance, and outputting the steering wheel signal to a drive-by-wire system of the vehicle, thereby realizing the control of the lateral motion of the vehicle. Different control target points are selected under different vehicle speeds, different error compensation mechanisms are arranged on tracks with different curvatures, and corresponding error compensation can be performed according to transverse steady-state errors. The method can have good adaptability in more different application scenes, such as: high speed up, ramp, etc.
Description
Technical Field
The invention belongs to the field of automatic driving of motor vehicles, and particularly relates to a transverse control method for controlling transverse and turning motions of an automatic driving vehicle.
Background
For over a century recently, the appearance of automobiles replaces the traditional transportation mode, so that the life of people is more convenient. In recent years, with the development of science and technology, especially the rapid development of intelligent computing, the research of the automatic driving automobile technology becomes a focus of all industries. The '12 leading edge technologies for determining future economy' report issued by McKensin discusses the influence degree of the 12 leading edge technologies on the future economy and society, and analyzes and estimates the respective economic and social influence of the 12 technologies in 2025, wherein the automatic driving automobile technology is ranked at the 6 th position, and the influence of the automatic driving automobile technology in 2025 is estimated as follows: economic benefits are about $ 0.2-1.9 trillion per year, and social benefits can recover 3-15 million lives per year.
Generally, an automatic automobile driving system is generally divided into three modules, namely a sensing module, which is equivalent to human eyes and collects the surrounding environment state in real time through a camera, a millimeter wave radar, a laser radar and other sensors, a decision module, which is equivalent to a human brain and calculates an optimal driving decision plan according to the environment state, and an execution module, which is equivalent to human hands and feet and is used for executing a decision command and realizing the control of transverse (steering) and longitudinal (accelerator/brake) driving operation.
In the field of automatic driving, the closer the running track of an automatic driving vehicle is to a planned track of a motion plan, the more excellent the control algorithm performance is; in the control algorithm, the longitudinal direction and the transverse direction are usually decoupled and are respectively controlled, and the main purpose of the transverse control algorithm is to have better performance on transverse position errors during tracking tracks, the transverse position errors have great influence, and if a vehicle with larger errors easily runs out of a lane or collides with a vehicle in an adjacent lane.
In the existing transverse control algorithm, the most basic algorithm is to use a PID control algorithm, the error compensation of the rotation angle is composed of three terms, wherein, the P term = error P _ gain, the D term = error differential D _ gain, and the I term = error integral I _ gain, and the P term is mainly used for compensating the increase of the tracking error; the term D is mainly used for compensating oscillation of tracking errors; the I term mainly accounts for the steady state error of the tracking error. After the parameters of P _ gain, I _ gain, and D _ gain are adjusted, the tracking performance is good, but the algorithm is susceptible to different parameters due to different speeds or curvatures of the vehicle, and a relatively complex PID lookup table needs to be adjusted to achieve better performance.
Another conventional algorithm is geometric pure tracking (pure pursuit), where the angular error compensation of the algorithm is arctan (2 Lsin (alpha (t))/kv _ x (t)), L is the wheel base of the vehicle, alpha is the steering error from the control target, k is an adjustable gain, and v _ x is the vehicle speed. In addition, kv _ x (t) can be regarded as a pre-traced control target point, and the algorithm is greatly influenced by the pre-tracing of the control target point, and different control target points are required at different vehicle speeds or curvatures.
The present invention has been made in view of the above circumstances.
Disclosure of Invention
In order to solve the above problems in the prior art, an object of the present invention is to provide a method for controlling a vehicle in an automatic driving manner, which calculates a control target point in a trajectory in a control algorithm based on the trajectory planned by a motion planning (planning) and positioning information of the vehicle, and improves an algorithm of a pure tracking method (pure tracking) so that the vehicle can follow the planned trajectory more quickly and stably.
The technical scheme of the invention is as follows: a method of lateral control of an autonomous vehicle, comprising the steps of:
1) Acquiring a planned vehicle motion track and information of a vehicle;
2) Executing a transverse control algorithm, including control target point calculation, tracking error calculation and error compensation control;
3) And (3) converting the error compensation obtained in the step 2) into a steering wheel signal through a transmission ratio set for different vehicles in advance, and outputting the steering wheel signal to a drive-by-wire system of the vehicle, thereby realizing the control of the lateral motion of the vehicle.
Further, the method for calculating the control target point in step 2) includes: setting different control target points according to different vehicle speeds, collecting N1 sampled track points within the duration of M1 when the vehicle speed is less than a preset value M, and averaging the information of the track points to serve as the control target points; when the vehicle speed is larger than or equal to a preset value M, collecting N2 sampled track points in the duration of M2, and taking the average value of the information of the track points as a control target point, wherein M2 is more than M1, and N2 is more than N1.
Further, the tracking error calculation method in step 2) is as follows: and calculating the error between the control target point and the vehicle position to obtain the transverse position error and the steering error of the vehicle.
Further, the method of the error compensation control in step 2) is: determining error compensation according to the curvature of the control target point, wherein when the curvature of the control target point is smaller than a certain preset value K, the error compensation is equal to arctan2 (-lateral _ gain transverse position error, velocity) -arctan2 (yaw _ gain _ small _ current sin, velocity); when the curvature of the control target point is greater than or equal to a certain preset value K, the error compensation is equal to-arctan 2 (yaw _ gain _ large _ current _ sin, steering error).
Further, the lateral position error is max (min (lateral position error, max _ lateral _ distance), min _ lateral _ distance), and the lateral position error is limited to min _ lateral _ distance and max _ lateral _ distance.
Further, the planned vehicle motion trajectory comprises planned position, speed, steering and curvature information; the information of the vehicle itself includes actual vehicle position, speed, and steering information.
The invention provides a relatively intelligent mechanism for controlling target points, integrates the advantages of a PID control algorithm and a pure tracking algorithm, selects different control target points under different vehicle speeds, has different error compensation mechanisms on tracks with different curvatures, and can make corresponding error compensation for transverse steady-state errors. The method can have good adaptability in more different application scenes, such as: high speed on ramps, etc.
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FIG. 1 is a flow chart of the lateral control method of the autonomous vehicle of the present invention.
Fig. 2 is a flow chart for acquiring a planned vehicle motion trajectory in the lateral control method of the autonomous vehicle according to the present invention.
Detailed Description
The following further description of the present invention, with reference to fig. 1-2, is made to a lateral control method for an autonomous vehicle, and it should be noted that the embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the invention, and should not be construed as limiting the invention.
As shown in fig. 1, which is a schematic flow chart of a lateral control method of an autonomous vehicle according to the present invention, the lateral control method of the autonomous vehicle includes the following steps:
1) And acquiring the planned vehicle motion track and the information of the vehicle. The planned vehicle motion track and the vehicle information are input from the outside. The planned vehicle motion track comprises information such as planned position, speed, steering and curvature; the information of the vehicle itself includes information such as an actual vehicle position, speed, and steering. The control system of the vehicle executes a lateral control algorithm based on these external inputs.
2) And executing a transverse control algorithm, including control target point calculation, tracking error calculation and error compensation control.
3) Converting the error compensation obtained in the step 2) into a steering wheel signal and outputting the steering wheel signal to a drive-by-wire system of the vehicle through a transmission ratio set for different vehicles in advance, thereby realizing the control of the lateral motion of the vehicle.
Specifically, the method for calculating the control target point in step 2) is as follows: setting different control target points according to different vehicle speeds, collecting N1 sampled track points within the duration of M1 when the vehicle speed is less than a preset value M, and averaging the information of the track points to serve as the control target points; when the vehicle speed is larger than or equal to a preset value M, collecting N2 sampled track points in the duration of M2, and taking the average value of the information of the track points as a control target point, wherein M2 is more than M1, and N2 is more than N1. The preset value M is different for different types of vehicles, and may be 10 km/h, 20 km/h, 60 km/h, 100 km/h or even 150 km/h, depending on the type of vehicle to be controlled, and may be set by an engineer in the field according to actual conditions.
Specifically, the tracking error calculation method in step 2) is as follows: and calculating the error between the control target point and the position of the vehicle, and obtaining the transverse position error and the steering error of the vehicle.
Specifically, the method of error compensation control in step 2) is: determining error compensation according to the curvature of the control target point, wherein when the curvature of the control target point is smaller than a certain preset value K, the error compensation is equal to arctan2 (-lateral _ gain transverse position error, velocity) -arctan2 (yaw _ gain _ small _ current sin, velocity); when the curvature of the control target point is greater than or equal to a certain preset value K, the error compensation is equal to-arctan 2 (yaw _ gain _ large _ current _ sin, steering error).
Wherein, linear _ gain is a transverse gain coefficient, yaw _ gain _ small _ current is a small-curvature steering gain coefficient, yaw _ gain _ large _ current is a large-curvature steering gain coefficient, and velocity is the current speed of the vehicle.
The curvature preset value K is different for different types of vehicles, and the value K may be a value less than 1, such as-0.001, -0.01, 0.001, 0.01, 0.05, 0.1, 0.5, and the like, which is related to the type of vehicle to be controlled, and the setting can be performed by an engineer in the field according to actual situations.
The lateral position error is max (min (lateral position error, max _ lateral _ distance), min _ lateral _ distance), and the lateral position error is limited to min _ lateral _ distance and max _ lateral _ distance.
The planned vehicle motion track comprises information such as planned position, speed, steering and curvature; the information of the vehicle itself includes information of an actual vehicle position, speed, steering, and the like.
In the invention, the planned vehicle motion track is obtained by computer simulation.
As shown in fig. 2, the planned vehicle motion trajectory simulation process of the present invention includes the following steps: a) The vehicle model based on data deep learning is utilized, so that the steering wheel information output by adopting a transverse control algorithm can have the same motion state feedback as that of an actual vehicle test, and meanwhile, a longitudinal control algorithm is used for obtaining the result of longitudinal vehicle motion.
b) Based on the motion feedback of the vehicle model, the positioning module is used for repositioning the vehicle in the high-precision map, and positioning information is output to the motion planning module; while new vehicle information is known based on the vehicle model. The vehicle information includes information on the moving speed of the vehicle.
c) Replanning the track based on the new positioning result, fitting a polynomial equation to the track, smoothing and outputting the smoothed track to a transverse control algorithm;
d) Calculating a control target point, calculating a tracking error and controlling error compensation, and outputting the error compensation control to a vehicle model to complete one-step simulation;
e) And adjusting various parameters through the simulated track tracking performance to obtain a planned vehicle motion track.
In the invention, the information of the vehicle can be replaced by the vehicle information, and the planned vehicle motion track can be understood as a preset and planned vehicle track in advance.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the claims.
Claims (4)
1. A method for lateral control of an autonomous vehicle, comprising the steps of:
1) Acquiring a planned vehicle motion track and information of a vehicle;
2) Executing a transverse control algorithm, including control target point calculation, tracking error calculation and error compensation control;
3) Converting the error compensation obtained in the step 2) into a steering wheel signal through a transmission ratio set for different vehicles in advance, and outputting the steering wheel signal to a drive-by-wire system of the vehicle, thereby realizing the control of the transverse motion of the vehicle;
the method for calculating the control target point in the step 2) comprises the following steps: setting different control target points according to different vehicle speeds, collecting N1 sampled track points within the duration of M1 when the vehicle speed is less than a preset value M, and averaging the information of the track points to serve as the control target points; when the vehicle speed is larger than or equal to a preset value M, collecting N2 sampled track points within the duration of M2, and averaging the information of the track points to serve as control target points, wherein M2 is larger than M1, and N2 is larger than N1;
the method for calculating the tracking error in the step 2) comprises the following steps: calculating the error between the control target point and the position of the vehicle to obtain the transverse position error and the steering error of the vehicle;
the method for controlling the error compensation in the step 2) comprises the following steps: determining error compensation according to the curvature of the control target point, wherein when the curvature of the control target point is smaller than a certain preset value K, the error compensation is equal to arctan2 (-lateral _ gain _ lateral position error, velocity) -arctan2 (yaw _ gain _ small _ current sin (steering error), velocity); when the curvature of the control target point is larger than or equal to a certain preset value K, the error compensation is equal to-arctan 2 (yaw _ gain _ large _ current _ sin, steering error); wherein, linear _ gain is a transverse gain coefficient, yaw _ gain _ small _ current is a small-curvature steering gain coefficient, yaw _ gain _ large _ current is a large-curvature steering gain coefficient, and velocity is the current speed of the vehicle;
the lateral position error is max (min (lateral position error, max _ lateral _ distance), min _ lateral _ distance), and the lateral position error is limited to min _ lateral _ distance and max _ lateral _ distance.
2. The autonomous-vehicle lateral control method of claim 1, wherein the planned vehicle motion profile comprises planned position, speed, steering, curvature information; the information of the vehicle itself includes actual vehicle position, speed, and steering information.
3. The autonomous-capable vehicle lateral control method of claim 1 wherein the planned vehicle motion profile is obtained from simulation.
4. The autonomous-vehicle lateral control method of claim 3, wherein the process steps of the simulation are as follows:
a) The vehicle model based on data deep learning is utilized, so that the information of a steering wheel output by a transverse control algorithm can have the same motion state feedback as that of an actual vehicle test, and meanwhile, a longitudinal control algorithm is used for obtaining the result of longitudinal vehicle motion;
b) Based on the motion feedback of the vehicle model, the positioning module is used for repositioning the vehicle in the high-precision map, and positioning information is output to the motion planning module; meanwhile, new vehicle movement speed information is obtained based on the vehicle model;
c) Replanning the track based on the new positioning result, fitting a polynomial equation to the track, smoothing and outputting the smoothed track to a transverse control algorithm;
d) Calculating a control target point, calculating a tracking error and controlling error compensation, and outputting the error compensation control to a vehicle model to complete one-step simulation;
e) And adjusting various parameters through the simulated track tracking performance to obtain a planned vehicle motion track.
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| US11518404B2 (en) * | 2020-03-23 | 2022-12-06 | Baidu Usa Llc | Static-state curvature error compensation control logic for autonomous driving vehicles |
| CN111862604B (en) * | 2020-07-20 | 2022-03-04 | 北京京东乾石科技有限公司 | Unmanned vehicle control method and device, computer storage medium and electronic equipment |
| CN114435470B (en) * | 2020-11-05 | 2022-11-25 | 长沙智能驾驶研究院有限公司 | Automatic reversing control method and device, vehicle and storage medium |
| CN113002620B (en) * | 2021-03-12 | 2023-04-25 | 重庆长安汽车股份有限公司 | Automatic steering wheel angle deviation correction method and system and vehicle |
| CN114275041B (en) * | 2022-01-10 | 2023-06-16 | 中国第一汽车股份有限公司 | Method and device for controlling transverse direction of automatic driving vehicle, vehicle and storage medium |
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| CN104571112B (en) * | 2015-01-14 | 2017-02-22 | 中国科学院合肥物质科学研究院 | Pilotless automobile lateral control method based on turning curvature estimation |
| US10427678B2 (en) * | 2017-06-13 | 2019-10-01 | Gm Global Technology Operations Llc. | System and method for low speed lateral control of a vehicle |
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