Intelligent automobile low-computing-power speed planning method based on road diffusion risk area

Abstract

The invention belongs to the technical field of automatic driving testing, and particularly relates to an intelligent automobile low-computing-power speed planning method based on a road diffusion risk area. Comprising the following steps: step 1, establishing a 1 / 4 vehicle suspension kinetic equation and a continuous time state-space equation, and discretizing the state-space equation; 2, preprocessing the look-ahead information acquired by the intelligent automobile; the intelligent vehicle performs road section division and vehicle speed change event determination by using global road information acquired by the Internet of Vehicles so as to establish a road diffusion risk area and generate a vehicle speed adjustment position; 3, generating an optimal vehicle speed sequence; a cost function is established according to a planning target and the generated vertical cost, a dynamic planning vehicle speed sequence generation method is designed, and a vehicle speed sequence is generated in combination with preprocessed look-ahead information. According to the method, the intelligent automobile is helped to plan the stable speed with low computing power, and a new solution is provided for speed planning of the intelligent automobile.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving testing, and specifically is a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones. Background Art

[0002] Smart cars are at the forefront of future car development. Speed ​​planning, as a necessary process in the operation of smart cars, can not only ensure the traffic efficiency of vehicles, but also improve the driving comfort of vehicles by considering the bumps of the road. For some bumpy roads, the suspension system of the vehicle alone cannot filter the impact of the road well. The vehicle needs to reduce the speed to pass smoothly. Therefore, it is feasible and necessary to plan the speed of the vehicle according to the bumpiness of the road.

[0003] Smart car speed planning can usually be divided into two parts: forward-looking information preprocessing and speed sequence generation. The rapidly developing Internet of Vehicles technology can provide smart cars with more comprehensive forward-looking information, such as traffic light signals, vehicle positions, and road elevation information, and preprocess the forward-looking information according to the planning goals. By using the preprocessed forward-looking information, smart cars can generate speed sequences for the entire road.

[0004] However, current research still has some limitations. On the one hand, the current forward-looking information preprocessing method simply distinguishes events that affect the planning target from ordinary roads, and cannot predict the approach or distance of these events. This will directly lead to the planned vehicle speed to easily change when approaching these events, affecting the longitudinal performance of smart cars. On the other hand, the current vehicle speed sequence generation method is generally based on the idea of ​​optimization, in which the iterative optimization process requires a lot of computing resources, resulting in high computing power requirements and long computing time. Summary of the invention

[0005] To solve the above problems, the present invention provides a low-computing-power speed planning method for smart cars based on road diffusion risk zones, which helps smart cars plan a stable speed with lower computing power, providing a new solution for smart car speed planning.

[0006] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0007] A low-computing speed planning method for an intelligent vehicle based on a road diffusion risk zone comprises the following steps:

[0008] Step 1: Establish the quarter vehicle suspension dynamics equation and continuous time state space equation, and discretize the state space equation;

[0009] Step 2: Preprocess the forward-looking information obtained by the smart car; the smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and determine the speed change events, and then establish the road diffusion risk area and generate the speed adjustment position;

[0010] Step 3: Generate the optimal vehicle speed sequence; establish a cost function based on the planning objectives and the generated vertical cost, design a dynamic planning vehicle speed sequence generation method, and generate a vehicle speed sequence in combination with the preprocessed forward-looking information.

[0011] Furthermore, the specific method of step one is as follows:

[0012] 11) According to the quarter vehicle suspension model and vehicle dynamics principle, the quarter vehicle suspension dynamics equation is obtained as follows:

[0013]

[0014] In the formula, m s is the sprung mass, m u is the unsprung mass, k s is the spring stiffness, c s is the suspension damping coefficient, c t is the vertical equivalent damping of the tire, k t is the vertical equivalent stiffness of the tire, z s is the vertical displacement of the sprung mass, z u is the vertical displacement of the unsprung mass, z r is the road surface height;

[0015] 12) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity As the state variable; choose sprung mass acceleration Suspension travel z u (t)-z s (t), tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the road height z r and its rate of change is the disturbance quantity of the system; then the quarter vehicle suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below:

[0016]

[0017] in,

[0018]

[0019] Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; ω(t) is the disturbance variable; A is the suspension system matrix; C is the suspension output matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the displacement of the sprung mass; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; z r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire;

[0020] 13) The continuous time matrix in equation (2) is discretized using the zero-order hold discretization method, and the discretized system state space equation is obtained as follows:

[0021]

[0022] Where A is the suspension system matrix; E is the state interference matrix; C is the suspension output matrix; L is the output interference matrix; T s is the sampling period of the suspension system; is the discrete suspension system matrix; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; ω(k) is the interference variable of the kth period.

[0023] Furthermore, the specific method of step 2 is as follows:

[0024] 21) The intelligent vehicle obtains forward-looking information, namely global road elevation information, through the Internet of Vehicles, and uses the global road elevation information to divide the entire road to be driven into bumpy sections B. i and flat road section S i ;

[0025] 22) Divide the entire road to be driven into N s Vehicle speed adjustment position P i Each speed adjustment position includes a speed change event speed adjustment position T i and normal speed adjustment position A i .

[0026] Furthermore, the specific method for determining the vehicle speed adjustment position is as follows:

[0027] (1) The starting point, the end point and the bumpy road section are called the speed change event speed adjustment position;

[0028] (2) Three common speed adjustment positions, A1, A2 and A3, are set before and after each speed change event to change the speed in time;

[0029] (3) The area between A3 in the normal speed adjustment position and the adjacent speed change event speed adjustment position is called the road diffusion risk area, which includes the normal speed adjustment positions A1, A2 and A3; the distance between the normal speed adjustment position and the speed change event speed adjustment position is set as follows:

[0030] l i =5i 2 , i=1,2,3 (5)

[0031] Where i is the normal speed adjustment position A i Serial number; l i Adjust position A for normal speed i The distance to the speed adjustment position of the speed change event;

[0032] (4) When a vehicle is traveling on a flat road section between two speed change events, it is stipulated that, except for the speed adjustment position set in (2), no other speed adjustment position shall be set between the two speed change events to avoid frequent acceleration and deceleration of the vehicle;

[0033] (5) For each speed adjustment position, there is a δV interval, starting from the lowest speed V min To the maximum speed V max N v Candidate speeds.

[0034] Furthermore, the specific method of step three is as follows:

[0035] 31) Determine planning objectives;

[0036] The planning objectives include vertical performance objectives, longitudinal performance objectives and traffic performance objectives;

[0037] The vertical performance includes the sprung mass acceleration, tire dynamic deflection and suspension dynamic travel. The global road elevation information obtained by the Internet of Vehicles is injected into the passive suspension model, that is, the quarter vehicle suspension model is established. The output variable y(k) in each sampling period is obtained by the discretized system state space equation (3), and the vertical performance of the corresponding vehicle speed adjustment position is calculated as follows:

[0038]

[0039] In the formula, is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N i is the sampling number of the suspension system at the i-th vehicle speed adjustment position; y1(k) is the sprung mass acceleration of the k-th sampling period; y2(k) is the suspension dynamic travel of the k-th sampling period; y3(k) is the tire dynamic deflection of the k-th sampling period; σ1 is the weight coefficient of sprung mass acceleration; σ2 is the suspension dynamic travel weight coefficient; σ3 is the tire dynamic deflection weight coefficient; l i is the length of the i-th speed adjustment position, V i is the speed of the vehicle passing through the i-th speed adjustment position, T s Sampling time for the suspension system;

[0040] Set the speed adjustment position P for bumpy road sections k The vertical performance of the vehicle speed adjustment position in the front and rear road diffusion risk areas is as follows:

[0041]

[0042] In the formula, is the vertical performance of the vehicle passing the kth speed adjustment position representing the bumpy road section at the pth candidate speed; is the vertical performance of passing the k-1th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-2th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-3th speed adjustment position with the pth candidate speed;

[0043] The cost function of the vertical performance objective is established as follows:

[0044]

[0045] In the formula, is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N s The number of bits for vehicle speed adjustment;

[0046] The speed of the vehicle between adjacent speed adjustment positions changes linearly, that is, the acceleration is a constant; therefore, the cost function for defining the longitudinal performance target is the absolute value of the longitudinal acceleration of the vehicle between two adjacent speed adjustment positions, reflecting the acceleration and deceleration of the vehicle, as follows:

[0047]

[0048] In the formula, V is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; Ls i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road; N s The number of bits for vehicle speed adjustment;

[0049] The traffic performance target represents the time between two adjacent speed adjustment positions of the vehicle, and its cost function is expressed as follows:

[0050]

[0051] In the formula, V is the cost of the vehicle traveling from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; N s Ls is the number of vehicle speed adjustment positions; i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road;

[0052] A total cost function including vertical performance, longitudinal performance and traffic performance is established; the specific form of the total cost function is shown in the following formula:

[0053]

[0054] In the formula, J pk (i) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the travel cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; α is the vertical cost weight coefficient; β is the longitudinal cost weight coefficient; γ is the travel cost weight coefficient; is the maximum vertical cost among all vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum travel cost among all adjacent speed adjustment positions; c i V is the vertical performance of the vehicle passing through the i-th speed adjustment position; max is the maximum speed of the vehicle; V min is the minimum speed of the vehicle; L max is the maximum distance among all adjacent speed adjustment positions on the road; l max is the maximum length of all bumpy sections on the road;

[0055] 32) Generate the vehicle speed sequence according to the established cost functions of vertical performance target, longitudinal performance target and traffic performance target; the entire road contains N s speed adjustment positions, each of which contains N v candidate speeds; the dynamic programming method calculates the cost of each speed adjustment position from back to front and performs speed planning.

[0056] Further, the specific method of step 32) is as follows:

[0057] 321) Build an N v ×N s The cost matrix Q is used to record the cost of all candidate speeds in the speed adjustment position. At the same time, an N v ×N s The speed matrix F is used to record the vehicle speed sequence; the element Q(p,k) in the kth column and pth row of the cost matrix Q represents the pth candidate speed V of the vehicle from the kth speed adjustment position p The minimum cost to the end of the road, the element F(p,k) in the kth column and the pth row of the speed matrix F represents the V to the kth speed adjustment position p The speed of the k+1th speed adjustment position with the minimum cost;

[0058] 322) Nth s Speed ​​adjustment position As the end of the road, set the speed adjustment position The cost of is zero, that is, the Nth s The elements of the column are all recorded as zero;

[0059] 323) For the Nth s -1 speed adjustment position The pth candidate speed is calculated according to formula (12) s Speed ​​adjustment position All candidate vehicle speeds are converted to their process costs, and the minimum process cost is selected and recorded in the cost matrix Q. At the same time, the Nth process cost corresponding to the minimum cost is recorded in the cost matrix Q. s The candidate vehicle speeds for the vehicle speed adjustment positions are recorded in the speed matrix F as follows:

[0060] Q(p,N s -1)=J(p,N s -1)=minJ p,k (N s ),p=1,2,...,N v ,k=1,2,...,N v (16)

[0061] Where Q is the cost matrix; N s N is the number of speed adjustment bits included in the entire road; v The number of candidate vehicle speeds for each vehicle speed adjustment position; J(p,N s -1) is the distance from the end of the road to the Nth s -1 The minimum total cost of the pth candidate speed for the speed adjustment position; J p,k (N s ) is the vehicle from the Nth s The kth candidate vehicle speed of the vehicle speed adjustment position is the Nth candidate vehicle speed. s -1 process cost between the pth candidate vehicle speeds of the vehicle speed adjustment position;

[0062] From the Nth s The speed adjustment position of the p-th candidate speed to the N-th candidate speed s The process cost of the j-th candidate speed for the -1 speed adjustment position is J j,p (N s );Select the minimum cost J j,j+1 (N s ) is denoted as J(j,N s -1) and stored in the jth row and Nth row of the cost matrix Q s -1 column, and at the same time, the j+1th candidate speed V corresponding to the minimum cost j+1 Stored in the jth row, Nth s -1 column, represents the Nth s The speed of the vehicle speed adjustment position should be V j+1 ;

[0063] 324) For the remaining speed adjustment positions, namely the Nth s -2 speed adjustment positions to the first speed adjustment position, calculate the adjacent speed adjustment position P according to step (3) i and P i+1 The process cost between the process cost and the vehicle speed adjustment position P i+1 The costs of the corresponding candidate speeds are added together, and the minimum cost is selected and recorded in the cost matrix, as shown below:

[0064] Q(p,i)=J(p,i)=min(J p,k(i+1)+J(p,i+1)),p=1,2,...,N v ,k=1,2,...,N v (17)

[0065] Where Q is the cost matrix; N v is the number of candidate vehicle speeds for each speed adjustment position; J(p,i+1) is the minimum total cost of the vehicle from the end of the road to the pth candidate vehicle speed at the i+1th speed adjustment position; J p,k (i+1) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position;

[0066] The process cost from the pth candidate speed at the n+1th speed adjustment position to the second candidate speed at the nth speed adjustment position is J 2,p (i+1); add the cost J(p,n+1) stored in the pth row of the n+1th column of the original cost matrix Q, select the minimum value and record it as J(2,n) and store it in the 2nd row and nth column of the cost matrix Q; at the same time, add the Nth row corresponding to the minimum cost v -1 candidate speed Stored in the 2nd row and nth column of the speed matrix F, the speed representing the n+1th speed adjustment position should be

[0067] Thus, the element Q(m,l) in the cost matrix Q is the minimum cost from the mth candidate speed at the lth speed adjustment position to the end point, and the element F(m,l) in the speed matrix F is the V to the l-1th speed adjustment position. m The speed of the lth speed adjustment position with the smallest cost;

[0068] According to the elements stored in the speed matrix F, a vehicle speed sequence obtained by a dynamic programming method is obtained;

[0069] When the speed of the vehicle at the first speed adjustment position is V1, first, the speed stored in the first row and first column of the speed matrix F is V2, which means the speed of the vehicle at the next speed adjustment position is V2. Then, the speed stored in the second row and second column of the speed matrix F is V2, which means the speed of the vehicle at the next speed adjustment position is V2; finally, the same process is repeated until the speed matrix F is found. s The velocity stored in column -1 is V j+1 , represents the vehicle at the Nth s The speed of the vehicle speed adjustment position is V j+1 ; So far, the vehicle speed sequence V of the entire road is obtained through the dynamic programming method.

[0070] The beneficial effects of the present invention are:

[0071] 1) The present invention provides a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones. The vehicle's vertical performance, longitudinal performance and traffic performance can be comprehensively considered during speed planning to improve the vehicle's driving comfort and traffic efficiency;

[0072] 2) The present invention designs a forward-looking information preprocessing method based on the Internet of Vehicles. The Internet of Vehicles obtains global road information and divides the road sections and determines the vehicle speed change events, and then establishes the road diffusion risk area and generates the vehicle speed adjustment position, which can provide comprehensive information for the vehicle speed sequence generation part and predict the approach and distance of the vehicle speed change event, so that the vehicle speed can be adjusted in time to adapt to the road surface with different degrees of bumpiness;

[0073] 3) The present invention designs a dynamic planning vehicle speed sequence generation method, which uses the dynamic planning method to generate the vehicle speed sequence, and can consider vertical performance, longitudinal performance and traffic performance during speed planning. While ensuring the relevant performance of the vehicle, the calculation power of the vehicle speed sequence generation method is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0075] Figure 1 It is a schematic diagram of a low-computing speed planning method for an intelligent vehicle based on a road diffusion risk zone according to the present invention;

[0076] Figure 2 It is a schematic diagram of a quarter vehicle suspension model;

[0077] Figure 3 This is a schematic diagram of forward-looking information processing;

[0078] Figure 4 This is a schematic diagram of the dynamic planning speed sequence;

[0079] Figure 5 This is a schematic diagram of vehicle speed sequence comparison. DETAILED DESCRIPTION

[0080] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0081] Embodiment 1

[0082] See also Figure 1 This embodiment provides a low-computing speed planning method for an intelligent vehicle based on a road diffusion risk zone, comprising the following steps:

[0083] Step 1: Read Figure 2 , establish the quarter vehicle suspension dynamics equation and continuous time state space equation, and discretize the state space equation, as follows:

[0084] 11) According to the quarter vehicle suspension model and vehicle dynamics principle, the quarter vehicle suspension dynamics equation is obtained as follows:

[0085]

[0086] In the formula, m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; s is the vertical displacement of the sprung mass; z u is the vertical displacement of the unsprung mass; z r is the road surface height;

[0087] 12) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity As the state variable; choose sprung mass acceleration Suspension travel z u (t)-z s (t), tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the road height z r and its rate of change is the disturbance quantity of the system; then the quarter vehicle suspension dynamics equation (1) can be rewritten as the system state space equation under continuous time, as shown below:

[0088]

[0089] in,

[0090]

[0091] Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; ω(t) is the disturbance variable; A is the suspension system matrix; C is the suspension output matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the displacement of the sprung mass; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; z r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire;

[0092] 13) The sensors in the suspension system are sampled at discrete time, and the state quantity obtained is discrete rather than continuous. Therefore, it is necessary to discretize the system state space equation under continuous time. The continuous time matrix in equation (2) is discretized using the zero-order hold discretization method, and the discretized system state space equation is obtained as follows:

[0093]

[0094] Where A is the suspension system matrix; E is the state interference matrix; C is the suspension output matrix; L is the output interference matrix; T s is the sampling period of the suspension system; is the discrete suspension system matrix; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; ω(k) is the interference variable of the kth period.

[0095] Step 2: Preprocess the forward-looking information obtained by the smart car; the smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and determine the speed change events, and then establish the road diffusion risk area and generate the speed adjustment position, as follows:

[0096] 21) The intelligent vehicle obtains forward-looking information, namely global road elevation information, through the Internet of Vehicles, and uses the global road elevation information to divide the entire road to be driven into bumpy sections B. i and flat road section S i ,like Figure 3 As shown;

[0097] The forward-looking information includes traffic light information, intersection information, global road elevation information, etc., but the present invention only uses the global road elevation information;

[0098] 22) In order to facilitate the adjustment of vehicle speed, the entire road contains N s Vehicle speed adjustment position P i , corresponding to each vertical line in the figure, which contains the speed change event speed adjustment position T i and normal speed adjustment position A i .

[0099] by Figure 4 Taking as an example, the specific method for determining the vehicle speed adjustment position is as follows.

[0100] (1) Compared with normal driving, the vehicle will change its speed more frequently when starting, on bumpy roads, and approaching the end point. Therefore, the starting point, end point, and bumpy road section are called speed change events and speed adjustment positions, which are represented by orange solid lines, i.e. Figure 4 T 1～5 ;

[0101] (2) In order to adapt to the frequent changes in vehicle speed when approaching the speed adjustment position of the "vehicle speed change event", three ordinary speed adjustment positions are set before and after each "vehicle speed change event" speed adjustment position for timely speed change, which are represented by blue solid lines, i.e. Figure 4 A in 1～3 ;

[0102] (3) The area between A3 in the normal speed adjustment position and the speed adjustment position of the adjacent speed change event is called the road diffusion risk area, which includes the normal speed adjustment position A 1～3 Considering that the vehicle speed will be adjusted more frequently as the distance approaches the speed adjustment position of the speed change event, the distance between the normal speed adjustment position and the speed change event speed adjustment position is set as follows:

[0103] l i =5i 2 , i=1,2,3 (5)

[0104] Where i is the normal speed adjustment position A i The serial number, l i Adjust position A for normal speed i The distance to the speed adjustment position of the "speed change event";

[0105] (4) When a vehicle passes through a flat road section between two speed change event speed adjustment positions, it rarely accelerates or decelerates significantly to avoid too frequent speed adjustments. Therefore, it is stipulated that except for the ordinary speed adjustment position set in (2), no other speed adjustment position shall be set between the two speed change event speed adjustment positions to avoid frequent acceleration or deceleration of the vehicle;

[0106] (5) For each speed adjustment position, there is a δV interval, starting from the lowest speed V min To the maximum speed V max N v Candidate speeds. Figure 3 The dots of different colors represent different types of candidate vehicle speeds. The blue dots are common candidate vehicle speeds, and the red dots are the highest vehicle speed V max , the green dot is the minimum speed V min ;

[0107] At this point, the forward information preprocessing is completed, and the N s Speed ​​adjustment position.

[0108] Step 3: Generate the optimal speed sequence. A cost function is established based on the planning goal and the generated vertical cost, and a dynamic planning speed sequence generation method is designed. The speed sequence is generated by combining the preprocessed forward-looking information, as follows:

[0109] 31) When generating a speed sequence, the planning target should be determined first, and then the corresponding cost function should be established to generate the optimal speed sequence; in order to ensure the driving comfort and traffic efficiency of the vehicle, the planning target in the present invention is mainly provided to include:

[0110] (1) Vertical performance objectives;

[0111] (2) vertical performance objectives;

[0112] (3) Traffic performance objectives;

[0113] The vertical performance includes the sprung mass acceleration, tire dynamic deflection, and suspension dynamic travel. In order to obtain the vertical performance of the vehicle passing through the speed adjustment position in the speed planning stage, the global road elevation information obtained by the Internet of Vehicles is injected into the passive suspension model. The output variable y(k) at each sampling period is obtained through the discretized system state space equation (3), and the vertical performance of the corresponding speed adjustment position is calculated:

[0114]

[0115] In the formula, is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N i is the sampling number of the suspension system at the i-th vehicle speed adjustment position; y1(k) is the sprung mass acceleration of the k-th sampling period; y2(k) is the suspension dynamic travel of the k-th sampling period; y3(k) is the tire dynamic deflection of the k-th sampling period; σ1 is the weight coefficient of sprung mass acceleration; σ2 is the suspension dynamic travel weight coefficient; σ3 is the tire dynamic deflection weight coefficient; l iis the length of the i-th vehicle speed adjustment position; V i is the speed of the vehicle passing through the i-th speed adjustment position; T s Sampling time for the suspension system;

[0116] In order to predict the approach and departure of the bumpy road section, the speed adjustment position P representing the bumpy road section is set. k The vertical performance of the vehicle speed adjustment position in the front and rear road diffusion risk areas is as follows:

[0117]

[0118] In the formula, is the vertical performance of the vehicle passing the kth speed adjustment position representing the bumpy road section at the pth candidate speed; is the vertical performance of passing the k-1th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-2th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-3th speed adjustment position with the pth candidate speed;

[0119] From this, the cost function of the vertical performance target can be established as shown in the following formula:

[0120]

[0121] In the formula, is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N s The number of bits for vehicle speed adjustment;

[0122] The speed of the vehicle between adjacent speed adjustment positions changes linearly, that is, the acceleration is a constant. Therefore, the cost function of the longitudinal performance target is defined as the absolute value of the longitudinal acceleration of the vehicle between two adjacent speed adjustment positions, reflecting the acceleration and deceleration of the vehicle, as follows:

[0123]

[0124] In the formula, V is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; Ls i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road; N s The number of bits for vehicle speed adjustment;

[0125] The traffic performance target represents the time between two adjacent speed adjustment positions of the vehicle, and its cost function can be expressed as follows:

[0126]

[0127] In the formula, V is the cost of the vehicle traveling from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; N s Ls is the number of vehicle speed adjustment positions; i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road;

[0128] Next, a total cost function including vertical performance, longitudinal performance and traffic performance is established. Since the dimensions of the three performances are different, normalization is required. The specific form of the total cost function is shown in the following formula:

[0129]

[0130] In the formula, J pk (i) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the travel cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; α is the vertical cost weight coefficient; β is the longitudinal cost weight coefficient; γ is the travel cost weight coefficient; is the maximum vertical cost among all vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum travel cost among all adjacent speed adjustment positions; c i V is the vertical performance of the vehicle passing through the i-th speed adjustment position; max is the maximum speed of the vehicle; V min is the minimum speed of the vehicle; L max is the maximum distance among all adjacent speed adjustment positions on the road; l max is the maximum length of all bumpy sections on the road;

[0131] 32) Based on the established cost function, this paper uses the dynamic programming method to generate the vehicle speed sequence. Figure 4 As shown, the entire road contains N s speed adjustment positions, each of which contains N v The dynamic programming method calculates the cost of each speed adjustment position from back to front and performs speed planning. The specific steps are as follows:

[0132] 321) Build an N v ×N s The cost matrix Q is used to record the cost of all candidate speeds in the speed adjustment position. At the same time, an N v ×N s The speed matrix F is used to record the vehicle speed sequence. The element Q(p,k) in the kth column and the pth row of the cost matrix Q represents the pth candidate speed V of the vehicle from the kth speed adjustment position p The minimum cost to the end of the road, the element F(p,k) in the kth column and the pth row of the speed matrix F represents the V to the kth speed adjustment position p The speed of the k+1th speed adjustment position with the minimum cost;

[0133] 322) Nth s Speed ​​adjustment position As the end of the road, set the speed adjustment position The cost of is zero, that is, the Nth s The elements of the column are all recorded as zero;

[0134] 323) For the Nth s -1 speed adjustment position The pth candidate speed is calculated according to formula (12) s Speed ​​adjustment position All candidate vehicle speeds are converted to their process costs, and the minimum process cost is selected and recorded in the cost matrix Q. At the same time, the Nth process cost corresponding to the minimum cost is recorded in the cost matrix Q. s The candidate vehicle speeds for the vehicle speed adjustment positions are recorded in the speed matrix F:

[0135] Q(p,N s -1)=J(p,N s -1)=minJ p,k (Ns),p=1,2,...,N v ,k=1,2,...,N v (16)

[0136] Where Q is the cost matrix; N s N is the number of speed adjustment bits included in the entire road; vThe number of candidate vehicle speeds for each vehicle speed adjustment position; J(p,N s -1) is the distance from the end of the road to the Nth s -1 The minimum total cost of the pth candidate speed for the speed adjustment position; J p,k (N s ) is the vehicle from the Nth s The kth candidate vehicle speed of the vehicle speed adjustment position is the Nth candidate vehicle speed. s -1 process cost between the pth candidate vehicle speeds of the vehicle speed adjustment position;

[0137] by Figure 4 For example, from the Nth s The speed adjustment position of the p-th candidate speed to the N-th candidate speed s The process cost of the j-th candidate speed for the -1 speed adjustment position is J j,p (N s );Select the minimum cost J j,j+1 (N s ) is denoted as J(j,N s -1) and stored in the jth row and Nth row of the cost matrix Q s -1 column, and at the same time, the j+1th candidate speed V corresponding to the minimum cost j+1 Stored in the jth row, Nth s -1 column, represents the Nth s The speed of the vehicle speed adjustment position should be V j+1 ;

[0138] 324) For the remaining speed adjustment positions, namely the Nth s -2 speed adjustment positions to the first speed adjustment position, calculate the adjacent speed adjustment position P according to step (3) i and P i+1 The process cost between the process cost and the vehicle speed adjustment position P i+1 The costs of the corresponding candidate speeds are added together, and the minimum cost is selected and recorded in the cost matrix:

[0139] Q(p,i)=J(p,i)=min(J p,k (i+1)+J(p,i+1)),p=1,2,...,N v ,k=1,2,...,N v (17)

[0140] Where Q is the cost matrix; N v is the number of candidate vehicle speeds for each speed adjustment position; J(p,i+1) is the minimum total cost of the vehicle from the end of the road to the pth candidate vehicle speed at the i+1th speed adjustment position; J p,k(i+1) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position;

[0141] by Figure 4 For example, the process cost from the pth candidate speed at the n+1th speed adjustment position to the second candidate speed at the nth speed adjustment position is J 2,p (i+1); add it to the cost J(p,n+1) stored in the pth row of the n+1th column of the original cost matrix Q, select the minimum value and record it as J(2,n) and store it in the 2nd row and nth column of the cost matrix Q; at the same time, v -1 candidate speed Stored in the 2nd row and nth column of the speed matrix F, the speed representing the n+1th speed adjustment position should be

[0142] Thus, the element Q(m,l) in the cost matrix Q is the minimum cost from the mth candidate speed at the lth speed adjustment position to the end point, and the element F(m,l) in the speed matrix F is the V to the l-1th speed adjustment position. m The speed of the lth vehicle speed adjustment position with the smallest cost.

[0143] According to the elements stored in the speed matrix F, the vehicle speed sequence obtained by the dynamic programming method can be obtained. Figure 4 As shown in the figure, when the speed of the vehicle at the first speed adjustment position is V1, first, the speed stored in the first row and first column of the speed matrix F is V2, which means that the speed of the vehicle at the next speed adjustment position is V2. Then, the speed stored in the second row and second column of the speed matrix F is V2, which means that the speed of the vehicle at the next speed adjustment position is V2; finally, the same process is repeated until the speed matrix F is found. s The velocity stored in column -1 is V j+1 , represents the vehicle at the Nth s The speed of the vehicle speed adjustment position is V j+1 At this point, the vehicle speed sequence V of the entire road is obtained through the dynamic programming method.

[0144] Embodiment 2

[0145] In order to verify the effectiveness and superiority of the method proposed in this patent, the following Figure 5 The road shown in the figure is simulated and verified, where the orange part is the bumpy road section and the white part is the flat road section. The traditional dynamic programming method is compared with the dynamic programming method based on the road diffusion risk zone proposed in this patent, and the speed sequence comparison diagram is as follows Figure 5 shown.

[0146] It can be seen that the speed sequence planned by the dynamic planning method based on the road diffusion risk zone proposed in the present invention can slow down in advance when approaching the bumpy road section and the end point, and the speed changes smoothly. This shows that the method proposed in the present invention can predict the approach and distance of speed change events, and can help smart cars plan better speed sequences.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-computing speed planning method for intelligent vehicles based on road diffusion risk zones, characterized in that: The following steps are involved: Step 1: Establish the quarter vehicle suspension dynamics equation and continuous time state space equation, and discretize the state space equation; Step 2: Preprocess the forward-looking information obtained by the smart car; the smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and determine the speed change events, and then establish the road diffusion risk area and generate the speed adjustment position; Step 3: Generate the optimal vehicle speed sequence; establish a cost function based on the planning objectives and the generated vertical cost, design a dynamic planning vehicle speed sequence generation method, and generate a vehicle speed sequence in combination with the preprocessed forward-looking information.

2. According to claim 1, a low-computing speed planning method for intelligent vehicles based on road diffusion risk areas is characterized in that: The specific method of step one is as follows: 11) According to the quarter vehicle suspension model and vehicle dynamics principle, the quarter vehicle suspension dynamics equation is obtained as follows: In the formula, m s is the sprung mass, m u is the unsprung mass, k s is the spring stiffness, c s is the suspension damping coefficient, c t is the vertical equivalent damping of the tire, k t is the vertical equivalent stiffness of the tire, z s is the vertical displacement of the sprung mass, z u is the vertical displacement of the unsprung mass, z r is the road surface height; 12) Select the sprung mass displacement z s (t), unsprung mass displacement z u (t), sprung mass displacement velocity Unsprung mass displacement velocity As the state variable; choose sprung mass acceleration Suspension travel z u (t)-z s (t), tire dynamic deflection z r (t)-z u (t) is the output variable of the system; then select the road height z r and its rate of change is the disturbance of the system; then the quarter vehicle suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below: in, Where x(t) is the state variable; is the derivative of the state variable; y(t) is the output variable; ω(t) is the disturbance variable; A is the suspension system matrix; C is the suspension output matrix; E is the state disturbance matrix; L is the output disturbance matrix; z s (t) is the sprung mass displacement; z u (t) is the unsprung mass displacement; is the sprung mass displacement velocity; is the displacement velocity of the unsprung mass; z r is the road surface height; m s is the sprung mass; m u is the unsprung mass; k s is the spring stiffness; c s is the suspension damping coefficient; c t is the vertical equivalent damping of the tire; k t is the vertical equivalent stiffness of the tire; 13) The continuous time matrix in equation (2) is discretized using the zero-order hold discretization method, and the discretized system state space equation is obtained as follows: Where A is the suspension system matrix; E is the state interference matrix; C is the suspension output matrix; L is the output interference matrix; T s is the sampling period of the suspension system; is the discrete suspension system matrix; is the discrete state interference matrix; x(k) is the state variable of the kth period; y(k) is the output variable of the kth period; ω(k) is the interference variable of the kth period.

3. According to claim 1, a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones is characterized in that: The specific method of step 2 is as follows: 21) The intelligent vehicle obtains forward-looking information, namely global road elevation information, through the Internet of Vehicles, and uses the global road elevation information to divide the entire road to be driven into bumpy sections B. i and flat road section S i ; 22) Divide the entire road to be driven into N s Speed ​​adjustment position P i Each speed adjustment position includes a speed change event speed adjustment position T i and normal speed adjustment position A i .

4. According to claim 3, a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones is characterized in that: The specific method for determining the vehicle speed adjustment position is as follows: (1) The starting point, the end point and the bumpy road section are called the speed change event speed adjustment position; (2) Three common speed adjustment positions, A1, A2 and A3, are set before and after each speed change event to change the speed in time; (3) The area between A3 in the normal speed adjustment position and the adjacent speed change event speed adjustment position is called the road diffusion risk area, which includes the normal speed adjustment positions A1, A2 and A3; the distance between the normal speed adjustment position and the speed change event speed adjustment position is set as follows: l i =5i 2 ,i=1,2,3 (5) Where i is the normal speed adjustment position A i Serial number; l i Adjust position A for normal speed i The distance to the speed adjustment position of the speed change event; (4) When a vehicle is traveling on a flat road section between two speed change events, it is stipulated that, except for the speed adjustment position set in (2), no other speed adjustment position shall be set between the two speed change events to avoid frequent acceleration and deceleration of the vehicle; (5) For each speed adjustment position, there is a δV interval, starting from the lowest speed V min To the maximum speed V max N v Candidate speeds.

5. According to claim 1, a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones is characterized in that: The specific method of step three is as follows: 31) Determine planning objectives; The planning objectives include vertical performance objectives, longitudinal performance objectives and traffic performance objectives; The vertical performance includes the sprung mass acceleration, tire dynamic deflection and suspension dynamic travel. The global road elevation information obtained by the Internet of Vehicles is injected into the passive suspension model, that is, the quarter vehicle suspension model is established. The output variable y(k) in each sampling period is obtained by the discretized system state space equation (3), and the vertical performance of the corresponding vehicle speed adjustment position is calculated as follows: In the formula, is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N i is the sampling number of the suspension system at the i-th vehicle speed adjustment position; y1(k) is the sprung mass acceleration of the k-th sampling period; y2(k) is the suspension dynamic travel of the k-th sampling period; y3(k) is the tire dynamic deflection of the k-th sampling period; σ1 is the weight coefficient of sprung mass acceleration; σ2 is the suspension dynamic travel weight coefficient; σ3 is the tire dynamic deflection weight coefficient; l i is the length of the i-th speed adjustment position, V i is the speed of the vehicle passing through the i-th speed adjustment position, T s Sampling time for the suspension system; Set the speed adjustment position P for bumpy road sections k The vertical performance of the vehicle speed adjustment position in the front and rear road diffusion risk areas is as follows: In the formula, is the vertical performance of the vehicle passing the kth speed adjustment position representing the bumpy road section at the pth candidate speed; is the vertical performance of passing the k-1th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-2th speed adjustment position with the pth candidate speed; is the vertical performance of passing the k-3th speed adjustment position with the pth candidate speed; The cost function of the vertical performance objective is established as follows: In the formula, is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the vertical performance of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; N s The number of bits for vehicle speed adjustment; The speed of the vehicle between adjacent speed adjustment positions changes linearly, that is, the acceleration is a constant; therefore, the cost function for defining the longitudinal performance target is the absolute value of the longitudinal acceleration of the vehicle between two adjacent speed adjustment positions, reflecting the acceleration and deceleration of the vehicle, as follows: In the formula, V is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; Ls i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road; N s The number of bits for vehicle speed adjustment; The traffic performance target represents the time between two adjacent speed adjustment positions of the vehicle, and its cost function is expressed as follows: In the formula, V is the cost of the vehicle traveling from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; p is the pth candidate vehicle speed; N s Ls is the number of vehicle speed adjustment positions; i Le is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road; i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road; A total cost function including vertical performance, longitudinal performance and traffic performance is established; the specific form of the total cost function is shown in the following formula: In the formula, J pk (i) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the vertical cost of the vehicle passing through the i-th speed adjustment position at the p-th candidate speed; is the longitudinal cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; is the travel cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; α is the vertical cost weight coefficient; β is the longitudinal cost weight coefficient; γ is the travel cost weight coefficient; is the maximum vertical cost among all vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum longitudinal cost among all adjacent vehicle speed adjustment positions; is the maximum travel cost among all adjacent speed adjustment positions; c i V is the vertical performance of the vehicle passing through the i-th speed adjustment position; max is the maximum speed of the vehicle; V min is the minimum speed of the vehicle; L max is the maximum distance among all adjacent speed adjustment positions on the road; l max is the maximum length of all bumpy sections on the road; 32) Generate the vehicle speed sequence according to the established cost functions of vertical performance target, longitudinal performance target and traffic performance target; the entire road contains N s speed adjustment positions, each of which contains N v candidate speeds; the dynamic programming method calculates the cost of each speed adjustment position from back to front and performs speed planning.

6. According to claim 5, a low-computing speed planning method for intelligent vehicles based on road diffusion risk zones is characterized in that: The specific method of step 32) is as follows: 321) Build an N v ×N s The cost matrix Q is used to record the cost of all candidate speeds in the speed adjustment position. At the same time, an N v ×N s The speed matrix F is used to record the vehicle speed sequence; the element Q(p,k) in the kth column and pth row of the cost matrix Q represents the pth candidate speed V of the vehicle from the kth speed adjustment position p The minimum cost to the end of the road, the element F(p,k) in the kth column and the pth row of the speed matrix F represents the V to the kth speed adjustment position p The speed of the k+1th speed adjustment position with the minimum cost; 322) Nth s Speed ​​adjustment position As the end of the road, set the speed adjustment position The cost of is zero, that is, the Nth s The elements of the column are all recorded as zero; 323) For the Nth s -1 speed adjustment position The pth candidate speed is calculated according to formula (12) s Speed ​​adjustment position All candidate vehicle speeds are converted to their process costs, and the minimum process cost is selected and recorded in the cost matrix Q. At the same time, the Nth process cost corresponding to the minimum cost is recorded in the cost matrix Q. s The candidate vehicle speeds for the vehicle speed adjustment positions are recorded in the speed matrix F as follows: Q(p,N s −1)=J(p,N s -1)=minJ p,k (N s ),p=1,2,…,N v ,k=1,2,...,N v (16) Where Q is the cost matrix; N s N is the number of speed adjustment bits included in the entire road; v The number of candidate vehicle speeds for each vehicle speed adjustment position; J(p,N s -1) is the distance from the end of the road to the Nth s -1 The minimum total cost of the pth candidate speed for the speed adjustment position; J p,k (N s ) is the vehicle from the Nth s The kth candidate vehicle speed of the vehicle speed adjustment position is the Nth candidate vehicle speed. s -1 process cost between the pth candidate vehicle speeds of the vehicle speed adjustment position; From the Nth s The speed adjustment position of the p-th candidate speed to the N-th candidate speed s The process cost of the j-th candidate speed for the -1 speed adjustment position is J j,p (N s );Select the minimum cost J j,j+1 (N s ) is denoted as J(j,N s -1) and stored in the jth row and Nth row of the cost matrix Q s -1 column, and at the same time, the j+1th candidate speed V corresponding to the minimum cost j+1 Stored in the jth row, Nth s -1 column, represents the Nth s The speed of the vehicle speed adjustment position should be V j+1 ; 324) For the remaining speed adjustment positions, namely the Nth s -2 speed adjustment positions to the first speed adjustment position, calculate the adjacent speed adjustment position P according to step (3) i and P i+1 The process cost between Adjust the process cost and vehicle speed to P i+1 The costs of the corresponding candidate speeds are added together, and the minimum cost is selected and recorded in the cost matrix, as shown below: Q(p,i)=J(p,i)=min(J p,k (i+1)+J(p,i+1)),p=1,2,...,N v ,k=1,2,...,N v (17) Where Q is the cost matrix; N v is the number of candidate vehicle speeds for each speed adjustment position; J(p,i+1) is the minimum total cost of the vehicle from the end of the road to the pth candidate vehicle speed at the i+1th speed adjustment position; J p,k (i+1) is the process cost of the vehicle from the kth candidate speed at the i+1th speed adjustment position to the pth candidate speed at the ith speed adjustment position; The process cost from the pth candidate speed at the n+1th speed adjustment position to the second candidate speed at the nth speed adjustment position is J 2,p (i+1); add the cost J(p,n+1) stored in the pth row of the n+1th column of the original cost matrix Q, select the minimum value and record it as J(2,n) and store it in the 2nd row and nth column of the cost matrix Q; at the same time, add the Nth row corresponding to the minimum cost v -1 candidate speed Stored in the 2nd row and nth column of the speed matrix F, the speed representing the n+1th speed adjustment position should be Thus, the element Q(m,l) in the cost matrix Q is the minimum cost from the mth candidate speed at the lth speed adjustment position to the end point, and the element F(m,l) in the speed matrix F is the V to the l-1th speed adjustment position. m The speed of the lth speed adjustment position with the smallest cost; According to the elements stored in the speed matrix F, a vehicle speed sequence obtained by a dynamic programming method is obtained; When the speed of the vehicle at the first speed adjustment position is V1, first, the speed stored in the first row and first column of the speed matrix F is V2, which means the speed of the vehicle at the next speed adjustment position is V2. Then, the speed stored in the second row and second column of the speed matrix F is V2, which means the speed of the vehicle at the next speed adjustment position is V2; finally, the same process is repeated until the speed matrix F is found. s The velocity stored in column -1 is V j+1 , represents the vehicle at the Nth s The speed of the vehicle speed adjustment position is V j+1 ; So far, the vehicle speed sequence V of the entire road is obtained through the dynamic programming method.

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