CN115257724B

CN115257724B - A plug-in hybrid vehicle safety and energy-saving decision control method and system

Info

Publication number: CN115257724B
Application number: CN202210802285.5A
Authority: CN
Inventors: 赵治国; 李涛
Original assignee: Tongji University
Current assignee: Tongji University
Priority date: 2022-07-07
Filing date: 2022-07-07
Publication date: 2024-11-26
Anticipated expiration: 2042-07-07
Also published as: CN115257724A

Abstract

The present invention relates to a plug-in hybrid vehicle safety and energy-saving decision-making control method and system, the method comprising the following steps: obtaining environmental information around a target vehicle, filtering obstacles, and determining dangerous obstacles; performing trajectory prediction and collision detection on the vehicle and dangerous obstacles respectively, and using game decision theory to make decisions on the safety behavior of the vehicle; based on the results of the safety behavior decision of the vehicle, establishing a vehicle longitudinal dynamic state model under a discrete state, constructing a multi-objective coordinated cost optimization function with the minimum system energy consumption, vehicle fuel economy, safety and driving comfort, planning local driving trajectory and longitudinal safety and economic speed; according to the longitudinal safety and economic speed obtained by planning, using a model predictive control algorithm to follow the longitudinal speed, obtain the vehicle target demand driving torque, and control the target vehicle. Compared with the prior art, the present invention fully taps the energy-saving potential of plug-in hybrid vehicles under the premise of ensuring safe driving of the vehicle.

Description

Safety energy-saving decision control method and system for plug-in hybrid electric vehicle

Technical Field

The invention relates to the technical field of energy-saving control of hybrid electric vehicles, in particular to a safety energy-saving decision control method and system for a plug-in hybrid electric vehicle.

Background

Plug-in Hybrid ELECTRIC VEHICLE, PHEV is a new energy automobile with the advantages of both pure electric and Hybrid electric automobiles, and can charge a power battery by utilizing the surplus power of an external power grid and an engine, reasonably distribute the output energy of electric energy and mechanical energy in a power system in the driving process, fully exert the advantages of Plug-in Hybrid power configuration and improve the fuel economy of the automobile. The working mode of the power system is closely related to the instantaneous driving working condition, and the problems of different modes and switching coordination control thereof under different working conditions need to be considered.

At present, the intelligent safety decision in the instant traffic environment has the problems of low control dimension and poor energy coordination and optimization control energy-saving effect, and a novel plug-in hybrid electric vehicle safety energy-saving decision control method and system are needed to be designed so as to fully exploit the plug-in hybrid power energy-saving potential.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a safety energy-saving decision control method and a system for a plug-in hybrid electric vehicle, which can fully dig the energy-saving potential of the plug-in hybrid electric vehicle.

The aim of the invention can be achieved by the following technical scheme:

According to a first aspect of the invention, a safety energy-saving decision control method of a plug-in hybrid electric vehicle is provided, and the method comprises the following steps:

s1, acquiring environmental information around a target vehicle, filtering obstacles, and determining dangerous obstacles; track prediction and collision detection are respectively carried out on the self-vehicle and the dangerous obstacle, and a game decision theory is adopted to carry out self-vehicle safety behavior decision;

Step S2, based on a self-vehicle safety behavior decision result, a vehicle longitudinal dynamics state model in a discrete state is established, a multi-objective coordination cost optimization function is established according to the minimum system energy consumption vehicle fuel economy, safety and driving comfort, and a local running track and a longitudinal safety economic vehicle speed are planned;

And step S3, according to the planned longitudinal safe economic vehicle speed, adopting a model predictive control algorithm to carry out longitudinal vehicle speed following, obtaining a vehicle target required driving torque, and controlling a target vehicle.

Preferably, in the step S1, environmental information around the target vehicle is obtained, the obstacle is filtered, and the dangerous obstacle is determined, which specifically is:

1) Performing a preliminary filtering according to the presence or absence of a lateral velocity of the obstacle relative to the trajectory of the vehicle, comprising:

filtering obstacles which have transverse and longitudinal speeds smaller than a set value and are not on the track of the vehicle;

track prediction is carried out based on the positions and speeds of the vehicle and the obstacle at the current moment, and when the motion trends of the two vehicles are separated, filtering is carried out;

2) And determining a filtering area of the own vehicle according to the position, the speed boundary and the movement trend of the obstacle relative to the own vehicle, filtering the obstacle which is outside the filtering area of the own vehicle and has no transverse speed on the track again, and determining the dangerous obstacle.

Preferably, the track prediction in step S1 is specifically:

And (5) predicting a self-vehicle track: predicting a running track and a vehicle posture of a vehicle in the future for a period of time according to the position, the course angle, the driving operation characteristic and the vehicle dynamics characteristic of the target vehicle at the current moment, wherein the running track and the vehicle posture comprise track points, the time for reaching the track points and the vehicle posture;

Obstacle trajectory prediction: judging the behaviors and driving style probability of the obstacle according to the historical data of the obstacle, and predicting the obstacle track for a long time by adopting a multi-source information fusion method to obtain the contour and the transverse and longitudinal speeds of the obstacle in a future period of time.

Preferably, the collision detection in step S1 specifically includes:

Traversing the track predicted positions of all dangerous obstacles and the track predicted positions of the vehicle, judging whether collision conflict exists or not from two dimensions of time and distance, and recording obstacle conflict information including the transverse and longitudinal speeds of the obstacles and the time of reaching a conflict point and vehicle conflict information including the time of reaching the conflict point and the distance of the conflict point of the vehicle.

Preferably, in the step S1, a game decision theory is adopted to make a decision on the safety behavior of the vehicle, specifically:

1) When the vehicle runs straight, dangerous obstacles run in the front or at the left and right sides, and reach a predicted conflict point in advance of the vehicle for a certain time, the vehicle runs without acceleration;

2) When the vehicle moves straight, the dangerous obstacle runs in the front or at the left and right sides, and reaches the predicted conflict point in a certain time range simultaneously with the vehicle, and the vehicle runs at a reduced speed if collision risk exists;

3) When the vehicle runs straight, dangerous obstacles run in the front or at the left and right sides, no conflict exists between the dangerous obstacles and the vehicle, and the vehicle runs normally;

4) When the vehicle turns, the dangerous obstacle runs in front, and reaches the predicted conflict point simultaneously with the vehicle within a certain time range, and the vehicle runs at a reduced speed with collision risk.

Preferably, the step S2 specifically includes:

Virtualizing dangerous obstacle track prediction, track curvature limitation and safety behavior decision results into control targets, constructing a discrete vehicle longitudinal dynamics model, taking vehicle fuel economy, safety and driving comfort as optimization targets, constructing a multi-target coordination cost optimization function, combining a vehicle running track and an optimal control algorithm under a system constraint condition, planning longitudinal vehicle speed point by point, updating the state of a target object in real time, and dynamically updating local longitudinal safety speed planning according to actual vehicle speed;

When the self-vehicle makes a deceleration and non-acceleration safety behavior decision, planning a local longitudinal safety economic vehicle speed, and intervening in driving demand power in advance.

Preferably, the longitudinal dynamics model of the vehicle in the step S2 is expressed in mathematical expression:

y(k)＝Cx(k)

where x is a state variable, k represents the kth sampling time, Representing a system matrix, wherein y is a system output, C is an output matrix, u is a control input, the speed or acceleration of the vehicle is used as a control quantity, w is a system disturbance, and the speed or acceleration of an obstacle is used as a disturbance quantity.

Preferably, the multi-objective coordination cost optimization function in step S2 has the expression:

J＝J_F+J_s+J_c

J_s＝w_ΔdΔd²+w_ΔvΔv²

J_C＝w_aa_f ²

Wherein J _F is a fuel economy optimization performance index, w _u is a desired acceleration weight coefficient, and w _du is a weight coefficient of a desired acceleration change rate; j _s is a system safety tracking performance index, w _Δd is a vehicle spacing error weight coefficient, deltad is a vehicle spacing, w _Δv is a vehicle relative speed weight coefficient, deltav is a relative speed; j _C is a vehicle comfort performance index, w _a is a longitudinal acceleration weight coefficient, and a _f is a longitudinal acceleration.

Wherein the multi-objective coordination cost function satisfies constraints in terms of vehicle economy, safety and comfort performance as constraints of comprehensive performance.

Preferably, in the step S3, a model predictive control algorithm is adopted to perform longitudinal vehicle speed following control, specifically:

Selecting the square accumulation of the difference value between the output quantity y _p (i|k) and the target vehicle speed v (i) in a prediction time domain window T _p = [ k+1:k+p ], optimizing to obtain a control variable sequence with the minimum target function in the corresponding prediction time domain, taking the first value in the sequence as the total required torque value of the current vehicle, and repeating the process at the next moment;

u_min≤u≤u_max

Wherein the target vehicle speed v (i) is a vehicle longitudinal safe economic vehicle speed reference curve, the output quantity of y _p (i|k) obtained by real-time iteration of a vehicle dynamics model is u _min、u_max, and the current upper limit and the lower limit of the required torque are represented.

According to a second aspect of the present invention, a system based on the plug-in hybrid electric vehicle safety energy-saving decision control method is provided, the system comprises:

the environment sensing module is used for acquiring environment information around the target vehicle through the intelligent sensor, including obstacle information;

the obstacle filtering module is used for filtering obstacles around the target vehicle to obtain dangerous obstacles;

The track prediction module is used for predicting the track of the dangerous obstacle and the vehicle obtained by the obstacle filtering module;

the collision detection module is used for detecting the collision of the dangerous obstacle obtained by the obstacle filtering module and the own vehicle;

The safety behavior decision module is used for making a self-vehicle safety behavior decision based on track prediction and collision detection results;

The local longitudinal speed planning module is used for planning the vehicle speed based on the vehicle safety behavior decision result to obtain the longitudinal safety economic vehicle speed;

And the speed following control module is used for carrying out longitudinal speed following control of the bicycle.

Compared with the prior art, the invention has the following advantages:

1) According to the real-time decision control method provided by the invention, on the premise of ensuring the safety of the vehicle in the dynamic traffic environment, the vehicle economy is taken as a constraint condition in the steps of behavior decision, speed planning and vehicle speed following, so that the fuel economy of the plug-in hybrid electric vehicle can be further improved, and the problems of low intelligent safety decision control dimension and poor energy coordination optimization control energy-saving effect in the current instantaneous traffic environment are solved;

2) The collision detection times are reduced by filtering the obstacles around the target vehicle for a plurality of times;

3) When the self-vehicle makes a deceleration and non-acceleration safety decision, the driving demand power is intervened in advance by further planning the local longitudinal safety economic vehicle speed, so that the energy loss caused by rapid acceleration and rapid deceleration is avoided, the torques of the engine and the motor are reasonably distributed, and the economical efficiency of the vehicle is improved;

4) The intelligent sensing technology based on the intelligent sensor provides a way for acquiring short-distance working condition information, and the intelligent auxiliary controller improves the calculation force and provides calculation force support for multi-source information fusion and obstacle motion prediction.

Drawings

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 is a control flow diagram of the present invention;

FIG. 3 is a schematic diagram of trajectory prediction;

FIG. 4 is a schematic diagram of filtering obstacles according to movement trends;

FIG. 5 is a schematic view of a vehicle filtration zone.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

Examples

Firstly, an embodiment of the method of the invention is provided, and the method comprises the following steps:

A, filtering obstacle:

track prediction is carried out based on the positions and speeds of the vehicle and the obstacle at the current moment, and when the movement trends of the two vehicles are separated, filtering is carried out, as shown in fig. 4;

2) And determining the size of a vehicle filtering area according to the transverse offset of the vehicle track, the course angle of the vehicle, the intersection mark, the lane mark and the like by taking the current position of the vehicle as a reference, and determining the vehicle filtering area, and filtering the obstacles which are outside the vehicle filtering area and have no transverse speed on the track again as shown in fig. 5, thereby reducing the collision detection times.

B, track prediction:

C, collision detection:

D, vehicle safety behavior decision, as shown in table 1:

TABLE 1

Step S2, based on a self-vehicle safety behavior decision result, virtualizing dangerous obstacle track prediction, track curvature limitation and safety behavior decision result into control targets, constructing a discrete vehicle longitudinal dynamics model, constructing a multi-target coordination cost optimization function by taking vehicle fuel economy, safety and driving comfort as optimization targets, carrying out longitudinal vehicle speed planning point by combining a self-vehicle running track and an optimal control algorithm under a system constraint condition, updating the state of a target object in real time, and dynamically updating local longitudinal safety speed planning according to the actual vehicle speed;

The longitudinal dynamics model of the vehicle has the mathematical expression:

y(k)＝Cx(k)

The multi-objective coordination cost optimization function has the expression:

J＝J_F+J_s+J_c

J_s＝w_ΔdΔd²+w_ΔvΔv²

J_C＝w_aa_f ²

Step S3, according to the longitudinal safe economic vehicle speed obtained by planning, adopting a model predictive control algorithm to carry out longitudinal vehicle speed following to obtain a vehicle target demand driving torque, and controlling a target vehicle, wherein the method specifically comprises the following steps:

the longitudinal vehicle speed following control is carried out by adopting a model predictive control algorithm, and specifically comprises the following steps:

u_min≤u≤u_max

The method of the present embodiment will be described in detail with reference to the accompanying drawings.

As shown in fig. 2:

(1) And (5) predicting a self-vehicle track:

The running track of the vehicle is predicted according to the operation of the driver (accelerator opening, brake opening, steering lamp and steering wheel angle) and the feedback of the state of the vehicle (navigation line, speed and lane line). Based on the dynamics of the vehicle, the current speed is kept unchanged, and the vehicle posture on the future track is predicted, wherein the vehicle posture comprises the contour, arrival time and other information of the vehicle at each track point.

(2) Obstacle trajectory prediction:

Firstly, filtering the obstacle, and obtaining local road condition information such as the distance, speed and position of the obstacle around the vehicle based on an intelligent sensor, wherein the obstacle is filtered, as shown in fig. 3, v_self represents the speed of the vehicle, and v_obs represents the vector speed of the obstacle. Firstly, filtering an obstacle OBS_2, an obstacle OBS_4 and an obstacle OBS_5 which are outside an area and have no transverse speed on a track according to a static obstacle filtering mode; and predicting a running track according to the state of the vehicle according to a dynamic obstacle filtering mode, predicting the track according to the position and the speed of the obstacle at the current moment, and filtering out the obstacle OBS_3 when the two movement trends are separated.

Then, the pose of the obstacle which does not meet the obstacle filtering rule is predicted, as shown in fig. 3, which is an obs_1 obstacle, taking the state of the obstacle at time t0 as an example, the initial position of the obstacle is (x 0, y 0), the current speed of the obstacle is (vx, vy), and after the time t passes, the new obstacle position (x 1, y 1) can be expressed as the following formula, so that the coordinates and the pose of each position of the obstacle in the predicted time can be calculated.

(3) Collision detection:

And traversing all dangerous obstacle track predicted positions and vehicle track predicted positions according to the obstacle filtering result, judging whether collision conflict exists or not from two dimensions of time and distance, and recording information such as the transverse and longitudinal speeds of the obstacle, the time to reach the conflict point of the vehicle, the distance of the conflict point and the like if the collision conflict occurs.

(4) Safety decision:

The road weight and game decision theory is adopted, and the following safety decision behavior is adopted for collision information of the vehicle and the obstacle. When the vehicle moves straight, the most dangerous obstacle runs in the front or at the left and right sides, and reaches the predicted conflict point in advance of the vehicle for a certain time, the vehicle runs without acceleration.

(5) Local longitudinal speed planning:

The vehicle speed track planned by the vehicle is shown in fig. 3, the vehicle speed at the t time is P (x _t,y_t,v_t), the driving demand power is intervened in advance by planning the local economic vehicle speed, the energy loss caused by rapid acceleration and rapid deceleration is avoided, the torques of the engine and the motor are reasonably distributed, and the economical efficiency of the vehicle is improved.

(6) Track following:

According to the planned local economic speed, in order to enable the vehicle to stably, safely and accurately track the expected track, a model predictive control algorithm is adopted for follow control. And selecting the square accumulation of the difference value between the output quantity y _p (i|k) and the target track r (i) in a prediction time domain window (T _p = [ k+1:k+p ]), obtaining a control variable sequence with the minimum target function in the corresponding prediction time domain through an optimization algorithm, taking the first value as the total required torque value of the current whole vehicle, and repeating the process at the next moment.

u_min≤u≤u_max

Wherein the target speed v (i) is a reference vehicle speed curve for vehicle speed track planning, y _p (i|k) is obtained by real-time iteration of a vehicle dynamics model, and u _min、u_max represents upper and lower limits of the current required torque.

The embodiment of the system of the invention is provided next, a system based on the plug-in hybrid electric vehicle safety energy-saving decision control method, the system comprises:

While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims

1. A plug-in hybrid vehicle safety and energy-saving decision control method, characterized in that the method comprises the following steps:

Step S1, obtaining environmental information around the target vehicle, filtering obstacles, and determining dangerous obstacles; performing trajectory prediction and collision detection on the ego vehicle and dangerous obstacles respectively, and using game decision theory to make decisions on the ego vehicle's safety behavior;

Step S2: Based on the decision result of the vehicle safety behavior, a vehicle longitudinal dynamic state model under discrete state is established, a multi-objective coordinated cost optimization function is constructed with the minimum system energy consumption, vehicle fuel economy, safety and driving comfort, and the local driving trajectory and longitudinal safety and economic speed are planned;

The multi-objective coordination cost optimization function is expressed as:

J＝J _F +J _s +J _c

J _s =w _Δd Δd ² +w _Δv Δv ²

J _C = w _a a _f ²

Wherein, J _F is the fuel economy optimization performance index, w _u is the expected acceleration weight coefficient, w _du is the expected acceleration change rate weight coefficient; J _s is the system safety tracking performance index, w _Δd is the vehicle spacing error weight coefficient, Δd is the vehicle spacing, w _Δv is the vehicle relative speed weight coefficient, Δv is the relative speed; J _C is the vehicle comfort performance index, w _a is the longitudinal acceleration weight coefficient, a _f is the longitudinal acceleration;

Among them, the multi-objective coordination cost function satisfies the constraints of vehicle economy, safety and comfort performance as the constraints of comprehensive performance;

Step S3: Based on the planned longitudinal safe and economical vehicle speed, a model predictive control algorithm is used to perform longitudinal vehicle speed following, obtain the vehicle target demand driving torque, and control the target vehicle.

2. A plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the environmental information around the target vehicle is obtained in step S1, obstacles are filtered, and dangerous obstacles are determined, specifically:

1) Perform preliminary filtering based on whether the obstacle has a lateral speed relative to the vehicle's trajectory, including:

Filter obstacles whose lateral and longitudinal speeds are less than the set values and are not on the vehicle's trajectory;

Based on the current position and speed of the vehicle and the obstacle, the trajectory is predicted. If the two vehicles move in different directions, they are filtered out.

2) According to the position, speed boundary and movement trend of the obstacle relative to the ego vehicle, the ego vehicle filtering area is determined. Obstacles outside the ego vehicle filtering area and without lateral speed on the trajectory are filtered again to determine dangerous obstacles.

3. The plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the trajectory prediction in step S1 is specifically:

Ego vehicle trajectory prediction: Based on the current position, heading angle, driving operation characteristics and vehicle dynamics characteristics of the target vehicle, the driving trajectory and attitude of the ego vehicle in the future are predicted, including the trajectory point, the time to reach the trajectory point and the vehicle attitude;

Obstacle trajectory prediction: The obstacle behavior and driving style probability are judged based on the historical obstacle data, and the multi-source information fusion method is used to perform long-term obstacle trajectory prediction to obtain the obstacle contour and lateral and longitudinal speed in the future.

4. The plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the collision detection in step S1 is specifically:

Traverse the predicted trajectory positions of all dangerous obstacles and the predicted trajectory positions of the ego vehicle, and determine whether there is a collision conflict from the two dimensions of time and distance. If a conflict occurs, record the obstacle conflict information, including the obstacle's lateral and longitudinal speeds and the time it arrives at the conflict point, as well as the ego vehicle conflict information, including the time the ego vehicle arrives at the conflict point and the distance from the conflict point.

5. A plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the game decision theory is used in step S1 to make the vehicle safety behavior decision, specifically:

1) When the vehicle is moving straight ahead and a dangerous obstacle is moving in front or on the left or right side and arrives at the predicted conflict point a certain time earlier than the vehicle, the vehicle does not accelerate;

2) When the vehicle is traveling straight ahead and a dangerous obstacle is traveling in front or on the left or right side, and reaches the predicted conflict point at the same time as the vehicle within a certain time range, and there is a risk of collision, the vehicle will slow down;

3) When the vehicle is moving straight ahead, dangerous obstacles are moving in front or on the left or right sides, and there is no conflict with the vehicle, so the vehicle moves normally;

4) When the ego vehicle turns, a dangerous obstacle is moving ahead and reaches the predicted conflict point at the same time as the ego vehicle within a certain time range, and there is a risk of collision, so the ego vehicle slows down.

6. The plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the step S2 is specifically:

The prediction of dangerous obstacle trajectories, trajectory curvature restrictions, and safety behavior decision results are virtualized as control targets, and a discrete vehicle longitudinal dynamics model is constructed. With vehicle fuel economy, safety, and driving comfort as optimization targets, a multi-objective coordinated cost optimization function is constructed. Under system constraints, the vehicle's running trajectory and the optimal control algorithm are combined to perform longitudinal vehicle speed planning point by point, and the target object state is updated in real time. The local longitudinal safety speed planning is dynamically updated according to the actual vehicle speed.

When the vehicle makes a safety behavior decision of deceleration or non-acceleration, the local longitudinal safe and economical speed is planned to intervene in the driving demand power in advance.

7. A plug-in hybrid vehicle safety and energy-saving decision control method according to claim 6, characterized in that the vehicle longitudinal dynamics model in step S2 is expressed as follows:

In the formula, x is the state variable, k represents the kth sampling time, represents the system matrix, y is the system output, C is the output matrix, u is the control input, with the vehicle speed or acceleration as the control variable, and w is the system disturbance, with the obstacle speed or acceleration as the disturbance variable.

8. The plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the longitudinal vehicle speed following control is performed using a model predictive control algorithm in step S3, specifically:

Select the square accumulation of the difference between the output _yp (i|k) and the target vehicle speed v(i) in the prediction time domain window _Tp = [k+1: k+p], optimize and obtain the control variable sequence with the minimum objective function in the corresponding prediction time domain, and take the first value in the sequence as the total required torque value of the current vehicle, and repeat this process at the next moment;

u _min ≤u ≤u _max

Wherein, the target vehicle speed v(i) is the reference curve of the vehicle longitudinal safety and economic speed, _yp (i|k) is the output obtained by real-time iteration of the vehicle dynamics model, and _umin and _umax represent the upper and lower limits of the current required torque.

9. A system based on the plug-in hybrid vehicle safety and energy-saving decision control method according to claim 1, characterized in that the system comprises:

Environmental perception module, used to obtain environmental information around the target vehicle, including obstacle information, through intelligent sensors;

The obstacle filtering module is used to filter the obstacles around the target vehicle to obtain dangerous obstacles;

The trajectory prediction module is used to predict the trajectory of the dangerous obstacles and the ego vehicle obtained by the obstacle filtering module;

A collision detection module is used to perform collision detection between the dangerous obstacles obtained by the obstacle filtering module and the vehicle;

Safety behavior decision module, used to make safety behavior decisions for the ego vehicle based on trajectory prediction and collision detection results;

The local longitudinal speed planning module is used to plan the vehicle speed based on the vehicle safety behavior decision results to obtain the longitudinal safe and economical vehicle speed;

The speed following control module is used to perform longitudinal speed following control of the vehicle.