CN115346366B - Intelligent network coupled vehicle team control method and system considering road adhesion coefficient - Google Patents
Intelligent network coupled vehicle team control method and system considering road adhesion coefficient Download PDFInfo
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Abstract
The invention discloses an intelligent network vehicle team control method and system considering road adhesion coefficient, which is characterized in that: based on a single-point pre-aiming principle and a vehicle queue transverse error model, obtaining an ideal front wheel corner, and inputting road surface attachment coefficients of different roads into an active steering controller as constraint conditions to obtain an additional front wheel corner; the ideal front wheel angle and the additional front wheel angle constitute the front wheel angle of the control vehicle, thereby controlling the lateral error of the vehicle. The intelligent network linkage vehicle team horizontal and longitudinal movement cooperative control under different road surface working conditions is considered, so that the unmanned vehicle team can adapt to more road surfaces to stably run.
Description
Technical Field
The invention relates to an advanced auxiliary driving system (ADAS), in particular to an intelligent network transverse and longitudinal cooperative control method and system for a self-adaptive road surface.
Background
With the continuous spanning development of network communication technology, control algorithm theory and computer technology and the reduction of sensor price, intelligent networking automobile linkage gradually enters the hot research. The intelligent network-connected automobile at the present stage mainly acquires the speed, relative distance, road information, vehicle rotation angle and other information of the front automobile and the front automobile through the communication of the internet of vehicles between various sensors and the automobiles, processes the information, and realizes the following of the fleet through a proper control strategy.
Nowadays, the demands on the intelligence and the driving safety of vehicles are higher and higher, unmanned researches are also getting hot, and automobile motion control is becoming a popular research. The vehicle motion control mainly includes lateral motion control and longitudinal motion control. In the actual running process of the vehicle, the independent transverse and longitudinal movement control has poor control effect on the vehicle. When a vehicle team follows at a curve, the longitudinal following error also affects the transverse error and has a certain error superposition, and the transverse and longitudinal movements of the vehicle are controlled simultaneously, so that a good tracking effect can be achieved on the front vehicle or the expected path.
Most of the research today assumes that the vehicle is traveling on dry asphalt and has a high road adhesion coefficient, so that the road can provide a high friction force and the tires are not easy to slip. In real life, however, the working conditions of the road surfaces between different road surfaces have a large difference, so that the stability of the vehicle is affected differently, and the control accuracy and the control effect are greatly affected.
Disclosure of Invention
Aiming at the defect that the existing vehicle control scheme does not consider the road adhesion coefficient, the invention provides the intelligent network linkage vehicle team cooperative control which considers the transverse and longitudinal movements of the intelligent network linkage vehicle team under different road conditions, so that the unmanned vehicle team can adapt to more roads for stable running.
The invention adopts the following technical scheme to solve the technical problems:
an intelligent network coupled vehicle team control method considering road adhesion coefficient is characterized in that: based on a single-point pre-aiming principle and a vehicle queue transverse error model, obtaining an ideal front wheel corner, and inputting road surface attachment coefficients of different roads into an active steering controller as constraint conditions to obtain an additional front wheel corner; the ideal front wheel angle and the additional front wheel angle constitute the front wheel angle of the control vehicle, thereby controlling the lateral error of the vehicle.
In the technical scheme, the transverse error of the vehicle is controlled according to the following steps:
s1: after the vehicle obtains the expected track, carrying out projection conversion on the information of the expected track, the curvature, the rotation angle and the like, and converting the coordinates in the geodetic coordinate system into the coordinates of the frenet coordinate system;
s2: setting the longest pretightening distance, the shortest pretightening distance, the pretightening time and the minimum speed and the maximum speed of pretightening distance conversion of the single-point pretightening model so as to adopt corresponding pretightening distances at different speeds, and reducing transverse errors and tending to zero when a vehicle runs to a pretightening point;
s3: the method comprises the steps of performing projection conversion on position information of a vehicle, importing the converted position information and road information with a desired path converted into a single-point pre-aiming model to obtain a pre-aiming distance, and subtracting vehicle transverse coordinates of the pre-aiming distance under a vehicle transverse component and a frenet coordinate system to obtain a transverse error of the vehicle;
s4: after the front vehicle senses the road surface information, the road surface information is transmitted to the following vehicle through the communication of the Internet of vehicles; the road surface information, the vehicle transverse error and the position state of the vehicle are led into a vehicle queue transverse error model to obtain the derivative of the transverse error, and then the ideal front wheel corner is obtained through calculation of a transverse controller;
s5: the road adhesion coefficient, the centroid slip angle and the yaw rate of the controlled vehicle are used as inputs, and the additional front wheel corner is obtained through the active front wheel steering controller, and the additional front wheel corner and the ideal front wheel corner form the front wheel corner, so that the vehicle is ensured to run stably.
The intelligent network train control method taking road adhesion coefficient into consideration is characterized by further comprising the step of layered longitudinal control: the upper layer controller calculates the expected acceleration for the controlled vehicle to stably drive according to the current state of the controlled vehicle, the current state of the front vehicle and the information of the road surface, and then inputs the calculated expected acceleration into the lower layer controller, and the lower layer controller calculates the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration, so that the acceleration or the deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
In the technical scheme, the system further comprises direct control corresponding to layered longitudinal control, and the upper controller directly controls the longitudinal stability of the motorcade.
In the technical scheme, the layered longitudinal control specifically comprises the following steps:
m1: the position, speed and course angle of the front vehicle and the curvature and corner information of the road are obtained through the internet of vehicles and the perception of the sensor, and the position, speed and corner information of the controlled vehicle are obtained at the same time; converting the information data of the front vehicle and the controlled vehicle into coordinates in a frenet coordinate system through projection;
m2: the distance policy between vehicles adopts a fixed time interval policy, a safe distance and a time constant between vehicles are set, and the obtained coordinate information of the front vehicle and the controlled vehicle is calculated to obtain a longitudinal error between the front vehicle and the controlled vehicle under the fixed time interval policy;
m3: transmitting the information of the road, the front vehicle and the controlled vehicle into a longitudinal error model of a vehicle queue, and simultaneously, after transmitting the calculated longitudinal error, calculating to obtain a longitudinal error derivative;
m4: inputting the longitudinal error derivative obtained in M3 into a synovial controller to obtain the expected acceleration of the vehicle, optimizing the expected acceleration by taking the road adhesion coefficient obtained in M1 as a constraint condition to obtain the expected acceleration considering road information, and calculating the difference between the acceleration of the controlled vehicle and the expected acceleration at the moment to make the difference be the input of a lower controller;
m5: the optimized expected acceleration obtained by the upper layer is input into a lower layer controller, the expected acceleration is firstly judged, the driving system is judged to work or the braking system is judged to work, after the braking/driving logic is passed, the corresponding throttle opening degree or braking pressure is calculated according to the difference value between the acceleration of the controlled vehicle and the expected acceleration, and the vehicle is controlled to make the distance error with the front vehicle zero as far as possible.
The intelligent network train control method taking road adhesion coefficient into consideration is characterized by further comprising a transverse and longitudinal coupling control step, wherein in the transverse and longitudinal coupling control step, longitudinal speed is taken as a coupling point for transverse and longitudinal control of the vehicle, and transverse control and longitudinal control are respectively carried out;
and a transverse control step: based on a single-point pre-aiming principle and a vehicle queue transverse error model, obtaining an ideal front wheel corner, and inputting road surface attachment coefficients of different roads into an active steering controller as constraint conditions to obtain an additional front wheel corner; the ideal front wheel rotation angle and the additional front wheel rotation angle form the front wheel rotation angle of the control vehicle, thereby controlling the transverse error of the vehicle;
a longitudinal control step: the upper layer controller calculates the expected acceleration for the controlled vehicle to stably drive according to the current state of the controlled vehicle, the current state of the front vehicle and the information of the road surface, and then inputs the calculated expected acceleration into the lower layer controller, and the lower layer controller calculates the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration, so that the acceleration or the deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
An intelligent network coupled vehicle team control system considering road adhesion coefficient is characterized by at least comprising:
the transverse control module is used for obtaining an ideal front wheel corner based on a single-point pre-aiming principle and a vehicle queue transverse error model, inputting road surface attachment coefficients of different roads into the active steering controller as constraint conditions to obtain an additional front wheel corner, and controlling the front wheel corner of the vehicle by the ideal front wheel corner and the additional front wheel corner so as to control the transverse error of the vehicle;
and the longitudinal control module is used for: the method is used for calculating the expected acceleration for enabling the controlled vehicle to stably drive according to the existing state of the controlled vehicle, the existing state of the front vehicle and the information of the road surface through the upper-layer controller, inputting the calculated expected acceleration into the lower-layer controller, and calculating the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration by the lower-layer controller, so that the acceleration or the deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
In the technical scheme, the longitudinal control module and the transverse control module take the longitudinal speed as a coupling point for transverse and longitudinal control of the vehicle.
In the prior art, the transverse and longitudinal control research of the motorcade generally assumes that the vehicle runs on a road with good road surface condition, and compared with the prior art, the control of the motorcade is more robust and stable by considering the road surface adhesion coefficient. The invention can ensure that the motorcade can drive on roads with different road adhesion coefficients to have higher stability.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic diagram of a longitudinal control strategy of an intelligent network-connected fleet horizontal-longitudinal cooperative control method taking road adhesion coefficients into consideration.
Fig. 2 is a schematic diagram of the lateral control strategy of the present invention.
FIG. 3 is a transverse and longitudinal co-control diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 shows a schematic diagram of a longitudinal control strategy of the intelligent network vehicle team longitudinal cooperative control method taking road adhesion coefficient into consideration.
When the vehicle is at the condition that the speed of a motor vehicle is faster or easy to skid, the lateral force of the vehicle tyre is easy to saturate, the provided lateral force is also reduced, the lateral movement of the vehicle is easy to lose stability, the steering capability is insufficient, the path tracking capability of the vehicle is reduced, and the safety of the vehicle running can be influenced even more, so that traffic accidents are caused.
The invention obtains an ideal front wheel corner based on a single-point pre-aiming principle and a vehicle queue transverse error model. And (5) taking road surface attachment coefficients of different roads as constraint conditions to be input into the active steering controller, so as to obtain the additional front wheel corner. The ideal front wheel angle and the additional front wheel angle constitute the front wheel angle of the control vehicle, thereby controlling the lateral error of the vehicle.
Step one: after the vehicle obtains the expected track, projection conversion is carried out on the information of the expected track, the curvature, the rotation angle and the like of the expected track, and the coordinates in the geodetic coordinate system are converted into the coordinates of the frenet coordinate system.
Step two: setting the longest pretightening distance, the shortest pretightening distance, the pretightening time, the minimum speed and the maximum speed of pretightening distance conversion of the single-point pretightening model so as to adopt corresponding pretightening distances at different speeds. So that the lateral error is reduced and tends to zero when the vehicle is driven to the pre-aiming point.
Step three: the position information of the vehicle is subjected to projection conversion, the converted position information and the road information with the expected path converted are led into a single-point pre-aiming model to obtain a pre-aiming distance, and the vehicle transverse coordinates of the pre-aiming distance under the vehicle transverse component and the frenet coordinate system are subtracted to obtain the transverse error of the vehicle.
Step four: after the front vehicle perceives the road surface information, the road surface information is transmitted to the following vehicle through the Internet of vehicles communication. The road surface information, the vehicle transverse error and the position state of the vehicle are led into a vehicle queue transverse error model, the derivative of the transverse error can be obtained, and then the ideal front wheel corner is obtained through calculation of a transverse controller.
Step five: because the driving and steering systems of the vehicle have certain delay, the transverse kinematic model of the actual vehicle has nonlinearity, the road surface working condition of the vehicle running is complex, and if the vehicle is controlled under a feedback-free system, the control effect of the vehicle may be unsatisfactory. And the road adhesion coefficient, the centroid slip angle and the yaw rate of the controlled vehicle are used as inputs, and the additional front wheel corner is obtained through the active front wheel steering controller. The additional front wheel corner and the ideal front wheel corner form the front wheel corner, thereby ensuring the running stability of the vehicle.
The fleet longitudinal control scheme is shown in fig. 2:
the stability of the platoon and the driving stability of the individual vehicles are both important for the normal driving of the unmanned platoon, whereas the complex conditions of the road have a great influence on the stability of the platoon and the driving follow-up of the individual vehicles. Considering different road adhesion coefficients has important practical significance for queue stability and vehicle running stability.
When the vehicle runs on a road surface with a low road adhesion coefficient, the longitudinal force of the vehicle tire is very easy to saturate, the driving force and the braking force of the vehicle are seriously affected, the vehicle cannot achieve the expected effect during acceleration or deceleration, and the longitudinal movement stability of the vehicle is difficult to ensure, so that the running safety of the whole motorcade is affected. The proposal considers road surface factors when designing the longitudinal controller of the vehicle, and can lead the vehicle to stably run on the road surface with lower road surface adhesion coefficient. The longitudinal control can be divided into direct control and layered control, and the layered control can enable all systems of the vehicle not to interfere with each other, and meanwhile, the control algorithm of a certain layer can be more convenient to optimize. The upper layer controller is mainly used for calculating expected acceleration for enabling the controlled vehicle to stably drive according to the existing state of the controlled vehicle, the existing state of the front vehicle and the information of the road surface, the calculated expected acceleration is input into the lower layer controller, and the lower layer controller is used for calculating the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration, so that acceleration or deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
Step one: the position, speed and course angle of the front vehicle, curvature and corner information of the road are obtained through the internet of vehicles and the perception of the sensor, and the position, speed and corner information of the controlled vehicle are obtained. The data are converted into coordinates in a frenet coordinate system through projection, so that calculation is facilitated.
Step two: the spacing policy between vehicles adopts a fixed time interval policy. Setting a safety distance and a time constant between vehicles, and calculating the obtained coordinate information of the front vehicle and the controlled vehicle to obtain a longitudinal error between the front vehicle and the controlled vehicle under a fixed time interval strategy.
Step three: longitudinal errors are related to a number of factors. And transmitting the information of the road, the front vehicle and the controlled vehicle into a longitudinal error model of the vehicle queue, and simultaneously, after transmitting the calculated longitudinal error, calculating the derivative of the longitudinal error.
Step four: inputting the longitudinal error derivative obtained in the third step into a synovial membrane controller can obtain the expected acceleration of the vehicle. And at the moment, optimizing the expected acceleration by taking the road adhesion coefficient obtained in the step one as a constraint condition to obtain the expected acceleration considering the road information. At this time, the difference between the acceleration of the controlled vehicle and the expected acceleration is calculated and is used as the input of the lower controller.
Step five: the optimized expected acceleration obtained by the upper layer is input into the lower layer controller. The desired acceleration is first determined, whether by the drive system or by the brake system. After passing through the braking/driving logic, the difference between the acceleration of the controlled vehicle and the expected acceleration is used for calculating the corresponding throttle opening or braking pressure through a certain control algorithm and a transmission system inverse model or a braking system inverse model. The vehicle is controlled so that the distance error from the preceding vehicle is zero as much as possible.
The vehicle transverse and longitudinal integrated control is as shown in fig. 3:
the previous transverse and longitudinal control schemes are all independent transverse or longitudinal control schemes, and the transverse and longitudinal coupling control is beneficial to the control stability of the vehicle. In the lateral control scheme, the longitudinal speed is related to the predicted distance of the driver model and the lateral error model of the vehicle queue, and the change of the vehicle speed causes the change of the lateral error, thereby influencing the change of the vehicle corner. In the longitudinal control scheme, the vehicle speed is not only the basis of the following distance of the vehicle, but also the influence factor of a longitudinal error model of the vehicle, and is also the factor of final control of a longitudinal controller so as to ensure the following distance. Therefore, the longitudinal vehicle speed is taken as a coupling point for the transverse and longitudinal control of the vehicle.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (7)
1. An intelligent network coupled vehicle team control method considering road adhesion coefficient is characterized in that: based on a single-point pre-aiming principle and a vehicle queue transverse error model, obtaining an ideal front wheel corner, and inputting road surface attachment coefficients of different roads into an active steering controller as constraint conditions to obtain an additional front wheel corner; the ideal front wheel rotation angle and the additional front wheel rotation angle form the front wheel rotation angle of the control vehicle, thereby controlling the transverse error of the vehicle;
the lateral error of the vehicle is controlled as follows:
s1: after the vehicle obtains the expected track, carrying out projection conversion on the expected track and curvature and corner information thereof, and converting the coordinates in the geodetic coordinate system into the coordinates in the frenet coordinate system;
s2: setting the longest pretightening distance, the shortest pretightening distance, the pretightening time and the minimum speed and the maximum speed of pretightening distance conversion of the single-point pretightening model so as to adopt corresponding pretightening distances at different speeds, and reducing transverse errors and tending to zero when a vehicle runs to a pretightening point;
s3: the method comprises the steps of performing projection conversion on position information of a vehicle, importing the converted position information and road information with a desired path converted into a single-point pre-aiming model to obtain a pre-aiming distance, and subtracting vehicle transverse coordinates of the pre-aiming distance under a vehicle transverse component and a frenet coordinate system to obtain a transverse error of the vehicle;
s4: after the front vehicle senses the road surface information, the road surface information is transmitted to the following vehicle through the communication of the Internet of vehicles; the road surface information, the vehicle transverse error and the position state of the vehicle are led into a vehicle queue transverse error model to obtain the derivative of the transverse error, and then the ideal front wheel corner is obtained through calculation of a transverse controller;
s5: the road adhesion coefficient, the centroid slip angle and the yaw rate of the controlled vehicle are used as inputs, and the additional front wheel corner is obtained through the active front wheel steering controller, and the additional front wheel corner and the ideal front wheel corner form the front wheel corner, so that the vehicle is ensured to run stably.
2. The intelligent network fleet control method considering road adhesion coefficients as set forth in claim 1, further comprising the step of layered longitudinal control:
the upper controller calculates expected acceleration for enabling the controlled vehicle to stably drive according to the existing state of the controlled vehicle, the existing state of the front vehicle and road surface information, the calculated expected acceleration is input into the lower controller, and the lower controller calculates the throttle opening or braking pressure required by the controlled vehicle based on the expected acceleration, so that acceleration or deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
3. The intelligent network fleet control method considering road adhesion coefficients as set forth in claim 2, further comprising direct control corresponding to hierarchical longitudinal control, the upper controller directly controlling fleet longitudinal stability.
4. The intelligent network fleet control method considering road adhesion coefficients as claimed in claim 2, wherein the layered longitudinal control comprises the steps of:
m1: the position, speed and course angle of the front vehicle and the curvature and corner information of the road are obtained through the internet of vehicles and the perception of the sensor, and the position, speed and corner information of the controlled vehicle are obtained at the same time; converting the information data of the front vehicle and the controlled vehicle into coordinates in a frenet coordinate system through projection;
m2: the distance policy between vehicles adopts a fixed time interval policy, a safe distance and a time constant between vehicles are set, and the obtained coordinate information of the front vehicle and the controlled vehicle is calculated to obtain a longitudinal error between the front vehicle and the controlled vehicle under the fixed time interval policy;
m3: transmitting the information of the road, the front vehicle and the controlled vehicle into a longitudinal error model of a vehicle queue, and simultaneously, after transmitting the calculated longitudinal error, calculating to obtain a longitudinal error derivative;
m4: inputting the longitudinal error derivative obtained in the M3 into a synovial membrane controller to obtain the expected acceleration of the vehicle, optimizing the expected acceleration by taking the road adhesion coefficient as a constraint condition to obtain the expected acceleration considering road information, calculating the difference between the acceleration of the controlled vehicle and the expected acceleration at the moment, and taking the difference as the input of a lower controller;
m5: the optimized expected acceleration obtained by the upper layer is input into a lower layer controller, the expected acceleration is firstly judged, the driving system is judged to work or the braking system is judged to work, after the braking/driving logic is passed, the corresponding throttle opening degree or braking pressure is calculated according to the difference value between the acceleration of the controlled vehicle and the expected acceleration, and the vehicle is controlled to make the distance error with the front vehicle zero as far as possible.
5. The intelligent network fleet control method considering road adhesion coefficient according to claim 1, further comprising a transverse-longitudinal coupling control step, wherein in the transverse-longitudinal coupling control step, longitudinal speed is taken as a coupling point for transverse-longitudinal control of the vehicle, and transverse control and longitudinal control are respectively performed;
and a transverse control step: based on a single-point pre-aiming principle and a vehicle queue transverse error model, obtaining an ideal front wheel corner, and inputting road surface attachment coefficients of different roads into an active steering controller as constraint conditions to obtain an additional front wheel corner; the ideal front wheel rotation angle and the additional front wheel rotation angle form the front wheel rotation angle of the control vehicle, thereby controlling the transverse error of the vehicle;
a longitudinal control step: the upper controller calculates expected acceleration for enabling the controlled vehicle to stably drive according to the existing state of the controlled vehicle, the existing state of the front vehicle and the road surface information, the calculated expected acceleration is input into the lower controller, and the lower controller calculates the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration, so that acceleration or deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
6. An intelligent network fleet control system that considers road adhesion coefficients for performing the intelligent network fleet control method that considers road adhesion coefficients of any one of claims 1-5, the intelligent network fleet control system that considers road adhesion coefficients comprising at least:
the transverse control module is used for obtaining an ideal front wheel corner based on a single-point pre-aiming principle and a vehicle queue transverse error model, inputting road surface attachment coefficients of different roads into the active steering controller as constraint conditions to obtain an additional front wheel corner, and controlling the front wheel corner of the vehicle by the ideal front wheel corner and the additional front wheel corner so as to control the transverse error of the vehicle;
and the longitudinal control module is used for: the method is used for calculating expected acceleration for enabling the controlled vehicle to stably drive according to the existing state of the controlled vehicle, the existing state of the front vehicle and the information of the road surface through the upper-layer controller, inputting the calculated expected acceleration into the lower-layer controller, and calculating the throttle opening or the braking pressure required by the controlled vehicle based on the expected acceleration by the lower-layer controller, so that acceleration or deceleration of the vehicle is controlled, and the longitudinal stability of a motorcade is ensured.
7. The intelligent network fleet control system considering road adhesion coefficients according to claim 6, wherein the longitudinal control module and the transverse control module take the longitudinal speed as a coupling point for transverse and longitudinal control of the vehicle.
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