CN114919589B - Method and system for determining target course angle in automatic driving transverse control and vehicle - Google Patents
Method and system for determining target course angle in automatic driving transverse control and vehicle Download PDFInfo
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- CN114919589B CN114919589B CN202210604478.XA CN202210604478A CN114919589B CN 114919589 B CN114919589 B CN 114919589B CN 202210604478 A CN202210604478 A CN 202210604478A CN 114919589 B CN114919589 B CN 114919589B
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- 230000004913 activation Effects 0.000 claims abstract description 8
- 230000001133 acceleration Effects 0.000 claims description 30
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000004069 differentiation Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
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Classifications
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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
- 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
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
-
- 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
- B60W2520/12—Lateral speed
- B60W2520/125—Lateral acceleration
-
- 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
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/18—Steering angle
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/53—Road markings, e.g. lane marker or crosswalk
-
- 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
- B60W2720/00—Output or target parameters relating to overall vehicle dynamics
- B60W2720/14—Yaw
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a target course angle determining method, a system and a vehicle in automatic driving transverse control, wherein the method comprises the following steps: reading vehicle running information; fitting to obtain a curve equation of a target lane line based on the front road lane line information identified by the camera; and judging whether the course angle correction activation condition is satisfied. If yes, calculating a course angle correction value beta 1, and calculating the target course angle beta f by combining the observed course angle a 1; if not, the observed heading angle a 1 is taken as the target heading angle beta f. The device can eliminate the influence caused by longitudinal movement of the vehicle, road curvature change and road surface transverse height difference, and is beneficial to realizing stable and rapid automatic driving of the vehicle.
Description
Technical Field
The invention belongs to the field of automatic driving, and particularly relates to a target course angle determining method and system in automatic driving transverse control and a vehicle.
Background
The advanced automatic driving function (more than L3 level) of the automobile is a complex high-level combination system of software and hardware, and a software algorithm module of the advanced automatic driving function mainly comprises environment sensing, vehicle positioning, track prediction of the automobile and the peripheral automobile, behavior decision, motion planning, motion control and the like. Motion control is the most basic software algorithm module for automatic driving, and comprises two parts, namely transverse control and longitudinal control. The transverse control is mainly used for controlling a steering wheel of the vehicle, and the longitudinal control is mainly responsible for controlling an accelerator and a brake of the vehicle, and the transverse control and the longitudinal control work cooperatively to ensure that the automatic driving automobile safely runs according to a preset reference track. And the transverse control performs tracking control according to the information such as the path, curvature and the like output by the upper-layer motion planning so as to reduce tracking errors and ensure the running stability and comfort of the vehicle. The target heading angle has an extremely important influence on the robustness and accuracy of the whole lateral control.
The existing method for determining the target course angle in the automatic driving transverse control comprises the following steps:
Based on the front road lane line information identified by the camera, fitting to obtain a curve equation of a target lane line: y=a 0+a1x+a2x2+a3x3; where x represents a lateral coordinate, y represents a longitudinal coordinate, a 0 represents a lateral position deviation of the vehicle from the target lane line, a 1 represents an observation course angle, a 2 represents a curvature of the target lane line, and a 3 represents a curvature change rate of the target lane line.
The observed heading angle a 1 is taken as the target heading angle β f.
In some cases, using a 1 directly as the target heading angle β f (i.e., the lateral control heading angle control amount) in the automatic driving lateral control may generate a large amount of deviation, and the specific reason is as follows:
(1) Longitudinal movement of the vehicle results in a heading angle deviation: because of the delay (delay time >300 ms) of the lane line image recognition system, when the vehicle enters a curve or exits the curve at a higher speed (such as a vehicle speed >15 m/s), the initial actual control quantity is smaller than the expected ideal control quantity based on deviation control (such as PID (proportion integration differentiation), fuzzy control and the like), so that the actual deviation gradually becomes larger, and the actual control quantity greatly exceeds the expected ideal control quantity as the deviation is accumulated, and finally, oversteer and steering oscillation are caused.
(2) The non-constant curvature results in a heading angle jump: the transverse control generally adopts the curvature of the target lane line as a basic static control quantity, the pretightening time is determined by calibrating the speed, the curvature and the curvature change rate of the vehicle and the lane width (pretightening can reduce control deviation caused by delay of a control system), the curvature of the target lane line is calculated according to a curve equation of the lane line, and when the running curvature of the vehicle is consistent with the curvature of the target lane line, the vehicle can stably run without controlling the transverse position deviation and observing the course angle. However, when the curvature of a curve is not constant, the heading angle deviation fluctuates with the curvature, causing a lateral swing.
(3) The lateral height difference of the road surface causes the observed course angle to be larger: the curvature of a pre-aiming point of a vehicle on a road without a transverse height difference is an ideal curvature, but the curvature of a target lane line is larger than the actual curvature due to the inclination of an observation reference plane of a vehicle camera due to the transverse height difference of the road surface, and the observation course angle is larger than the actual course angle, so that oversteer is caused.
Disclosure of Invention
The invention aims to provide a target course angle determining method and system in automatic driving transverse control and a vehicle, so as to eliminate influences caused by longitudinal movement of the vehicle, road curvature change and road surface transverse height difference.
The invention aims to provide a target course angle determining method in automatic driving transverse control, which comprises the following steps:
And reading the vehicle running information. The vehicle running information comprises a vehicle speed V, a vehicle yaw rate yawrate, a vehicle lateral acceleration a y, a vehicle lateral acceleration sensor self-learning value offset1 and a steering wheel corner.
Based on the front road lane line information identified by the camera, fitting to obtain a curve equation of a target lane line: y=a 0+a1x+a2x2+a3x3; where x represents a lateral coordinate, y represents a longitudinal coordinate, a 0 represents a lateral position deviation of the vehicle from the target lane line, a 1 represents an observation course angle, a 2 represents a curvature of the target lane line, and a 3 represents a curvature change rate of the target lane line.
And judging whether the course angle correction activation condition is satisfied.
If yes, then:
using the formula: Calculating a course angle correction value beta 1; here, factor1 represents a vehicle lateral acceleration correction coefficient, and factor2 represents a vehicle yaw rate correction coefficient. When the vehicle is oversteered, a certain transverse acceleration exists; in addition, the course angle fluctuation caused by the road curvature fluctuation also has the lateral acceleration fluctuation. Therefore, the vehicle lateral acceleration is taken into consideration in calculating the course angle correction value β 1. The influence caused by the original error of the temperature/sensor can be eliminated by introducing the self-learning value offset1 of the vehicle lateral acceleration sensor when calculating the course angle correction value beta 1. Since the motion model of the vehicle is a non-ideal linear model; therefore, when calculating the course angle correction value β 1, the vehicle lateral acceleration and the vehicle yaw rate need to be corrected by introducing coefficients factor1 and factor2 determined by actual test data calibration.
Using the formula: beta f＝a1-β1, calculating the target heading angle beta f.
If not, the observed heading angle a 1 is taken as the target heading angle β f (i.e., β f＝a1).
Preferably, the vehicle lateral acceleration correction coefficient factor1 is obtained by:
Inquiring a preset transverse acceleration correction coefficient table according to the vehicle speed V and the steering wheel angle to obtain a vehicle transverse acceleration correction coefficient factor1; the preset transverse acceleration correction coefficient table is a corresponding relation table of the vehicle speed, the steering wheel angle and the transverse acceleration correction coefficient of the vehicle, which are obtained through a calibration mode.
Preferably, the vehicle yaw rate correction coefficient factor2 is obtained by:
Inquiring a preset yaw rate correction coefficient table according to the vehicle speed V and the steering wheel rotation angle to obtain a yaw rate correction coefficient factor2 of the vehicle; the preset yaw rate correction coefficient table is a corresponding relation table of the vehicle speed, steering wheel angle and vehicle yaw rate correction coefficient obtained through a calibration mode.
Preferably, if the conditions 1a to 1d are simultaneously satisfied and the first time is continued, it means that the course angle correction activation condition is satisfied; wherein, condition 1a is: the vehicle speed V is in a preset vehicle speed threshold range; condition 1b is: the curvature a 2 of the target lane line is within a preset curvature threshold range; condition 1c is: the transverse position deviation a 0 of the vehicle and the target lane line is in a preset deviation threshold range; condition 1d is: the steering wheel angle is within a preset angle threshold.
Preferably, the first time is obtained by inquiring a preset first time table according to the vehicle speed V, the curvature a 2 of the target lane line and the steering wheel angle; the preset first time table is a corresponding relation table of the vehicle speed, the curvature of the target lane line and the steering wheel angle and the first time, which are obtained through a calibration mode.
The target course angle determining system in automatic driving transverse control comprises a processor, wherein the processor is programmed to execute the target course angle determining method.
The vehicle comprises the target course angle determining system.
After the invention meets the course angle correction activation condition, the current course angle correction value can be calculated by adding the vehicle transverse acceleration, the vehicle yaw rate, the vehicle speed and the steering wheel corner as negative feedback, and the target course angle is finally obtained by combining the observed course angle, thereby eliminating the influence caused by the longitudinal movement of the vehicle, the road curvature change and the road surface transverse height difference, improving the precision and the real-time by more than 10 percent, and being beneficial to realizing the stable and rapid automatic driving of the vehicle.
Drawings
Fig. 1 is a flowchart of a target heading angle determination method in automatic driving lateral control of the present embodiment.
Detailed Description
As shown in fig. 1, the method for determining a target heading angle in automatic driving lateral control in this embodiment is executed by a processor, and specifically includes:
Step S101, reading vehicle travel information. The vehicle driving information includes a vehicle speed V, a vehicle yaw rate yawrate, a vehicle lateral acceleration a y, a vehicle lateral acceleration sensor self-learning value offset1 (sent by ESP, a vehicle lateral acceleration sensor self-learning mode belongs to the prior art), and a steering wheel angle.
Step S102, fitting to obtain a curve equation of a target lane line based on the front road lane line information identified by the camera: y=a 0+a1x+a2x2+a3x3. Where x represents a lateral coordinate, y represents a longitudinal coordinate, a 0 represents a lateral position deviation of the vehicle from the target lane line, a 1 represents an observation course angle, a 2 represents a curvature of the target lane line, and a 3 represents a curvature change rate of the target lane line. The camera (a monocular wide-angle CMOS sensor) arranged at the top of a vehicle windshield collects gray image signals in the running process of the vehicle, after distortion calibration and gray enhancement, the gray image signals are processed by the neural network software running in the special image processing chip (the prior art), the information of the front road lane line (namely the first lane line and the second lane line at the left side and the first lane line and the second lane line at the right side) is obtained through recognition, and the processor fits the front road lane line to obtain the curve equation of the target lane line.
Step S103, judging whether the course angle correction activation condition is met, if yes, executing step S105, otherwise executing step S104.
Wherein if the conditions 1a to 1d are satisfied at the same time and for a first time, it means that the course angle correction activation condition is satisfied.
Condition 1a is: the vehicle speed V is within a preset vehicle speed threshold range.
Condition 1b is: the curvature a 2 of the target lane line is within a preset curvature threshold range.
Condition 1c is: the lateral position deviation a 0 of the vehicle from the target lane line is within a preset deviation threshold range.
Condition 1d is: the steering wheel angle is within a preset angle threshold.
The first time is obtained by inquiring a preset first time schedule according to the vehicle speed V, the curvature a 2 of the target lane line and the steering wheel angle. The preset first time table is a corresponding relation table of the vehicle speed, the curvature of the target lane line and the steering wheel angle and the first time, which are obtained through a calibration mode.
Step S104, the observed heading angle a 1 is set as the target heading angle β f, and then ends.
Step S105, using the formula: The heading angle correction value β 1 is calculated, and then step S106 is performed. Here, factor1 represents a vehicle lateral acceleration correction coefficient, and factor2 represents a vehicle yaw rate correction coefficient. And inquiring a preset transverse acceleration correction coefficient table according to the vehicle speed V and the steering wheel angle to obtain a vehicle transverse acceleration correction coefficient factor1. And inquiring a preset yaw rate correction coefficient table according to the vehicle speed V and the steering wheel angle to obtain a vehicle yaw rate correction coefficient factor2. The preset transverse acceleration correction coefficient table is a corresponding relation table of the vehicle speed, steering wheel angle and the transverse acceleration correction coefficient of the vehicle, which are obtained through a calibration mode. The preset yaw rate correction coefficient table is a corresponding relation table of the vehicle speed, steering wheel angle and the yaw rate correction coefficient of the vehicle, which are obtained through a calibration mode.
Step S106, utilizing the formula: beta f＝a1-β1, calculating a target heading angle beta f, and then ending.
The target course angle beta f in the embodiment is used in automatic driving transverse control as transverse control course angle control quantity, so that deviation can be avoided, and stable and rapid automatic driving of the vehicle can be realized.
The present embodiment also provides a target course angle determination system in automatic driving lateral control, including a processor programmed to perform the target course angle determination method described above.
The embodiment also provides a vehicle comprising the target course angle determining system.
Claims (5)
1. A target heading angle determination method in automatic driving lateral control, characterized by comprising:
Reading vehicle running information; the vehicle running information comprises a vehicle speed V, a vehicle yaw rate yawrate, a vehicle transverse acceleration a y, a vehicle transverse acceleration sensor self-learning value offset1 and a steering wheel corner;
Based on the front road lane line information identified by the camera, fitting to obtain a curve equation of a target lane line: y=a 0+a1x+a2x2+a3x3; wherein x represents a lateral coordinate, y represents a longitudinal coordinate, a 0 represents a lateral position deviation of the vehicle from the target lane line, a 1 represents an observation course angle, a 2 represents a curvature of the target lane line, and a 3 represents a curvature change rate of the target lane line;
Judging whether a course angle correction activation condition is met;
If yes, then:
using the formula: Calculating a course angle correction value beta 1; wherein factor1 represents a vehicle lateral acceleration correction coefficient, and factor2 represents a vehicle yaw rate correction coefficient;
Using the formula: beta f＝a1-β1, calculating the target course angle beta f;
If not, taking the observed course angle a 1 as the target course angle beta f;
If the conditions 1 a-1 d are met at the same time and the first time is continued, the condition that the course angle correction activation condition is met is indicated; wherein,
Condition 1a is: the vehicle speed V is in a preset vehicle speed threshold range;
Condition 1b is: the curvature a 2 of the target lane line is within a preset curvature threshold range;
Condition 1c is: the transverse position deviation a 0 of the vehicle and the target lane line is in a preset deviation threshold range;
Condition 1d is: steering wheel angle is within a preset angle threshold range;
The first time is obtained by inquiring a preset first time schedule according to the vehicle speed V, the curvature a 2 of the target lane line and the steering wheel angle; the preset first time table is a corresponding relation table of the vehicle speed, the curvature of the target lane line and the steering wheel angle and the first time, which are obtained through a calibration mode.
2. The target heading angle determination method in automatic driving lateral control according to claim 1, characterized in that:
the vehicle lateral acceleration correction coefficient factor1 is obtained by:
Inquiring a preset transverse acceleration correction coefficient table according to the vehicle speed V and the steering wheel angle to obtain a vehicle transverse acceleration correction coefficient factor1; the preset transverse acceleration correction coefficient table is a corresponding relation table of the vehicle speed, the steering wheel angle and the transverse acceleration correction coefficient of the vehicle, which are obtained through a calibration mode.
3. The target heading angle determination method in automatic driving lateral control according to claim 1, characterized in that:
The vehicle yaw rate correction coefficient factor2 is obtained by:
Inquiring a preset yaw rate correction coefficient table according to the vehicle speed V and the steering wheel rotation angle to obtain a yaw rate correction coefficient factor2 of the vehicle; the preset yaw rate correction coefficient table is a corresponding relation table of the vehicle speed, steering wheel angle and vehicle yaw rate correction coefficient obtained through a calibration mode.
4. A target heading angle determination system in automatic driving lateral control, comprising a processor, characterized in that: the processor is programmed to perform the target heading angle determination method as defined in any one of claims 1 to 3.
5. A vehicle, characterized in that: comprising the target heading angle determination system as defined in claim 4.
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| US6789014B1 (en) * | 2003-05-09 | 2004-09-07 | Deere & Company | Direct modification of DGPS information with inertial measurement data |
| CN110745135A (en) * | 2018-07-06 | 2020-02-04 | 现代摩比斯株式会社 | Device and method for compensating course angle |
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| CN113360795A (en) * | 2021-08-11 | 2021-09-07 | 智道网联科技(北京)有限公司 | Method and device for correcting vehicle course angle, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US7957897B2 (en) * | 2007-06-29 | 2011-06-07 | GM Global Technology Operations LLC | GPS-based in-vehicle sensor calibration algorithm |
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| US6789014B1 (en) * | 2003-05-09 | 2004-09-07 | Deere & Company | Direct modification of DGPS information with inertial measurement data |
| CN110745135A (en) * | 2018-07-06 | 2020-02-04 | 现代摩比斯株式会社 | Device and method for compensating course angle |
| CN113295179A (en) * | 2021-06-04 | 2021-08-24 | 清智汽车科技(苏州)有限公司 | Vehicle course angle correction method and device based on laser sensor |
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