Intelligent automobile multi-target speed planning method for driving comfort and passing efficiency

Abstract

The invention belongs to the technical field of automatic driving testing, and particularly relates to an intelligent automobile multi-target speed planning method for driving comfort and passing efficiency. 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; step 2, the intelligent vehicle performs road section division and vehicle speed adjustment position generation by using global road information acquired by the Internet of Vehicles; and step 3, establishing a cost function according to a planning target and the generated vertical cost, designing a non-dominated dung beetle multi-target vehicle speed sequence generation method, and finally generating an optimal vehicle speed sequence in combination with the preprocessed look-ahead information. According to the method, the vertical performance, the longitudinal performance and the passing performance of the vehicle can be remarkably improved, and a new solution is provided for intelligent vehicle speed planning for driving comfort and passing efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic driving test, and specifically is a multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency. Background Art

[0002] Smart cars are at the forefront of future automotive development. As an important component, the suspension system plays an important role in ensuring the driving comfort of vehicles. However, for some bumpy roads, the suspension system alone cannot filter the impact of the road well. Vehicles need to reduce their speed to pass smoothly. Therefore, in order to balance the driving comfort and traffic efficiency of vehicles, it is feasible and necessary to plan the speed of vehicles according to the degree of road bumps.

[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, on the one hand, most of the current speed sequence generation methods focus on the fuel economy and safety of vehicles, but rarely pay attention to the driving comfort and traffic efficiency of vehicles. On the other hand, vehicles often need to consider multiple conflicting goals when generating speed sequences. In pursuit of traffic efficiency, vehicles often sacrifice some driving comfort, and vice versa. Traditional methods artificially assign weights to multiple goals, convert them into single-objective problems, and then solve them. However, due to the contradictions between different goals, it is difficult to find the best balance between multiple goals by adjusting the weights. Summary of the invention

[0005] To solve the above problems, the present invention provides a multi-objective speed planning method for intelligent vehicles aimed at driving comfort and traffic efficiency, which can significantly improve the vertical performance, longitudinal performance and traffic performance of the vehicle, and provides a new solution for the speed planning of intelligent vehicles aimed at driving comfort and traffic efficiency.

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

[0007] A multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency includes 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: The smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and generate the speed adjustment position;

[0010] Step 3: Establish a cost function based on the planning objectives and the generated vertical cost, and design a non-dominated dung beetle multi-objective speed sequence generation method. Finally, combine the pre-processed forward-looking information to generate the optimal speed sequence.

[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; 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 of the system, and 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 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;

[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 and flat sections;

[0025] 22) The entire road to be driven is divided into a number of speed adjustment positions; each speed adjustment position is provided with a speed adjustment interval of δV, from the lowest speed V min To the maximum speed V max Multiple candidate vehicle speeds.

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

[0027] (1) Assuming that the vehicle's speed is constant on the bumpy road section, each bumpy road section is regarded as a speed adjustment position;

[0028] (2) Set three intervals of l before and after each vehicle speed change event k The speed adjustment position is used to change the speed in time;

[0029] (3) 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.

[0030] Furthermore, the specific method of step three is as follows:

[0031] 31) Determine planning objectives;

[0032] The planning objectives include vertical performance objectives, longitudinal performance objectives and traffic performance objectives;

[0033] 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 through the discretized system state space equation (3), and the vertical performance of the corresponding bumpy road section is calculated as follows:

[0034]

[0035] In the formula, c i is the vertical performance of the i-th bumpy road section; N i is the sampling number of the suspension system in the i-th bumpy road section; 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 weight coefficient of suspension dynamic travel; σ3 is the weight coefficient of tire dynamic deflection; l i is the length of the i-th bumpy road section; V i is the speed of the vehicle passing through the i-th bumpy road section; T s Sampling time for the suspension system;

[0036] The cost function of the vertical performance objective is established as follows:

[0037]

[0038] Where V i is the speed of the vehicle passing through the i-th bumpy road section; Jver(Vi) N is the vertical cost of the vehicle passing through the entire road; b is the number of bumpy sections; c i is the vertical performance of the i-th bumpy road section;

[0039] The cost function that defines the longitudinal performance target is the sum of the absolute values ​​of the longitudinal acceleration of the vehicle on the entire road, reflecting the acceleration and deceleration of the vehicle, as shown below:

[0040]

[0041] Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J acc (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad 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;

[0042] The traffic performance target represents the total time it takes for a vehicle to pass through the entire road, and the cost function is expressed as follows:

[0043]

[0044] Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J t (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad Ls is the number of vehicle speed adjustment positions; i is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road, Le i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road;

[0045] 32) According to the established cost functions of the vertical performance target, the longitudinal performance target and the traffic performance target, a non-dominated dung beetle multi-objective vehicle speed sequence generation method is designed to generate the optimal vehicle speed sequence; first, an initial speed sequence Z is generated t ; Then, the vehicle speed sequence is iterated to generate the iterated speed sequence K t ; Then, the speed sequence Z before and after the iteration t and K t Perform Pareto non-dominated sorting and divide it into n non-dominated sets F(1)-F(n); finally, select k vehicle speed sequences with smaller cost functions in order as the initial speed sequence Z for the next iteration. t:1 .

[0046] Further, the specific method of step 32) is as follows:

[0047] 321) Generate vehicle speed sequence;

[0048] Randomly generate k initial vehicle speed sequences Zt ; Each speed sequence is a length equal to the number of speed adjustment bits N s A one-dimensional array of , where the i-th element in the array represents the speed of the vehicle at the i-th speed adjustment position;

[0049] 322) Iterate the vehicle speed sequence;

[0050] The vehicle speed sequence is divided into two parts during iteration: global exploration and local optimization;

[0051] During global exploration, the global exploration sequence is used to find the optimal speed sequence by moving away from the global worst sequence, that is, the speed sequence with the highest cost. The iteration method of the global exploration sequence is as follows:

[0052] Xsi(t+1)＝Xsi(t)+ α ×k×Xsi(t -1)+β ×ΔX(t) (10)

[0053]

[0054] ΔX(t)=|Xs i (t)-X w (t)| (12)

[0055] Where t is the current iteration number; Xs i (t) is the i-th search sequence at the t-th iteration; X w (t) is the global worst speed sequence at the tth iteration; α is the coefficient of variation; k is the inertia coefficient; β is the deflection coefficient; η is a random number between zero and one; λ is the probability of variation;

[0056] In local optimization, local search sequences, global search sequences and hybrid search sequences are used for detailed local optimization; the local optimal exploration sequence searches only in the local optimal area, while the global search sequence and hybrid exploration sequence search in the global optimal area; the local optimal area and the global optimal area change with the number of iterations:

[0057]

[0058] Where, Lb * is the lower bound of the optimal region; Ub * is the upper bound of the local optimal region; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region; t is the current number of iterations; b * (t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration; T max is the maximum number of iterations; Vmin is the minimum vehicle speed; V max is the maximum vehicle speed;

[0059] The three sequences (local search sequence, global search sequence, and hybrid search sequence) will iterate in different ways; among them, the iteration method of the local search sequence is as follows:

[0060] Xli(t+1)＝b * (t)+f1×(Xli(t) -Lb * )+f2×(Xli(t) -Ub * ) (14)

[0061] Where t is the current iteration number; Xl i (t) is the i-th local search sequence at the t-th iteration; b * (t) is the local optimal vehicle speed sequence at the tth iteration; f1 and f2 are 1×D random vectors; Lb * is the lower bound of the local optimal region; Ub * is the upper bound of the local optimal region;

[0062] The overall search sequence is iterated as follows:

[0063] Xgi(t+1)＝Xgi(t)+c×(Xgi(t) -Lb a )+d×(Xgi(t) -Ub a ) (15)

[0064] Where t is the current iteration number; Xg i (t) is the i-th overall search sequence at the t-th iteration; c is a 1×D vector that follows a normal distribution; d is a 1×D random vector; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region;

[0065] The iteration of the hybrid search sequence is as follows:

[0066] XC i (t+1)=b a (t)+g×(|Xc i (t)-b * (t)|+|Xc i (t)-b a (t)|) (16)

[0067] Where t is the current iteration number; Xc i (t) is the i-th mixed search sequence at the t-th iteration; b *(t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration, g is a 1×D random vector;

[0068] k initial vehicle speed sequences Z t After iterating through equations (10), (14), (15) and (16), k new vehicle speed sequences K are generated. t ; The 2k vehicle speed sequences before and after the iteration are recorded as X t , where the i-th vehicle speed sequence is X t (i);

[0069] 323) Perform Pareto non-dominated sorting;

[0070] From all the speed sequences before and after the iteration, k speed sequences with smaller cost functions are selected for the next iteration; therefore, all speed sequences need to be Pareto non-dominated sorted as follows:

[0071] First, the vertical performance, longitudinal performance, and traffic performance of all speed sequences are calculated through the established cost function. Then, all the sequences that are not dominated by other speed sequences are selected and stored in the non-dominated set F(1). t (a) The performance of any defined planning objective is no better than that of another speed sequence X t (b) Difference, and X t (a) Outperform X on at least one planning objective t (b), then X t (b) Being X t (a) dominates; then, in all vehicle speed sequences, i.e., X t Remove the sequences included in F(1), and select the sequences that are not dominated by other speed sequences from the remaining speed sequences and store them in the non-dominated set F(2); and so on, until all speed sequences are sorted and stored in the non-dominated sets F(1)-F(n); according to the definition and properties of domination, it can be seen that the speed sequence in the non-dominated set F(1) is the best, the speed sequence in F(2) is the second best, and the speed sequence in F(n) is the worst;

[0072] 324) Select vehicle speed sequence;

[0073] First, store F(i) into the initial velocity sequence Z for the next iteration in the order of the non-dominated set from small to large. t:1 Until F(p) is added, Z t:1The number of sequences in F(p) exceeds the number of initial speed sequences k; then, the non-dominated set F(p+1)-F(n) is eliminated, and the concept of congestion is used to sort the sequences in F(p); the congestion represents the density of the surrounding sequences of the selected speed sequence, and the sequence X is used t (n) Surrounded by X t (n) A cuboid Q that contains no other sequences. t (n) volume representation; finally, select the speed sequence with the smallest congestion for the next iteration; if the non-dominated set F(1)-F(p-1) contains m speed sequences, then select the first km speed sequence with the smallest congestion in F(p), that is, F1(p), and store it in Z t:1 Make the number of sequences contained in it reach the initial speed sequence number k, and eliminate the remaining speed sequence F2(p), and then Z t:1 The vehicle speed sequence in is used for the next iteration.

[0074] The beneficial effects of the present invention are:

[0075] 1) The present invention provides a multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency, which can comprehensively consider the vertical performance, longitudinal performance and traffic performance of the vehicle during vehicle speed planning, thereby improving the driving comfort and traffic efficiency of the vehicle;

[0076] 2) The present invention designs a forward-looking information preprocessing method based on the Internet of Vehicles, which obtains global road information through the Internet of Vehicles, divides road sections and determines vehicle speed change events, and then generates vehicle speed adjustment positions, which can provide comprehensive information for the optimal vehicle speed sequence generation part, so that the vehicle speed can be adjusted in time to adapt to roads with different degrees of bumpiness;

[0077] 3) The present invention uses the dung beetle optimizer as the basis for generating the optimal vehicle speed sequence. Using a variety of different speed sequences to generate the optimal vehicle speed sequence has higher convergence accuracy than other intelligent swarm optimization algorithms and is easier to generate the optimal vehicle speed sequence.

[0078] 4) The present invention designs a multi-objective vehicle speed sequence generation method. Based on the Pareto optimality theory, a non-dominated dung beetle multi-objective vehicle speed sequence generation method is designed, which can balance the conflicting vertical performance, longitudinal performance and traffic performance to generate an optimal vehicle speed sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] 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.

[0080] Figure 1 A schematic diagram of a multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to the present invention;

[0081] Figure 2 It is a schematic diagram of a quarter vehicle suspension model;

[0082] Figure 3 Provide a schematic diagram for road segmentation;

[0083] Figure 4 This is a schematic diagram of forward-looking information processing;

[0084] Figure 5 This is a schematic diagram of the method for generating multi-objective vehicle speed sequences for non-dominated dung beetles;

[0085] Figure 6 This is a schematic diagram of the congestion degree of the vehicle speed sequence;

[0086] Figure 7 This is a schematic diagram of vehicle speed sequence comparison. DETAILED DESCRIPTION

[0087] 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.

[0088] Embodiment 1

[0089] See also Figure 1 This embodiment provides a multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency, comprising the following steps:

[0090] 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:

[0091] 11) According to the quarter vehicle suspension model and vehicle dynamics principle, the quarter vehicle suspension dynamics equation is obtained as follows:

[0092]

[0093] 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 tis 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;

[0094] 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, and the quarter vehicle suspension dynamics equation (1) is rewritten as the system state space equation under continuous time, as shown below:

[0095]

[0096] in,

[0097]

[0098]

[0099] 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;

[0100] 13) The sensors in the suspension system are sampled at discrete time, and the state quantity obtained is discrete rather than continuous. Therefore, we need 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:

[0101]

[0102] 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.

[0103] Step 2: The smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and generate the speed adjustment position, as follows:

[0104] 21) Intelligent vehicles obtain forward-looking information, namely global road elevation information, through the Internet of Vehicles, and use the global road elevation information to divide the entire road to be driven into bumpy sections and flat sections, such as Figure 3 As shown;

[0105] 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;

[0106] 22) In order to facilitate the adjustment of vehicle speed, the entire road contains several speed adjustment positions. For each speed adjustment position, there is a δV interval, starting from the lowest speed V min To the maximum speed V max Multiple candidate vehicle speeds. Figure 4 Taking the vehicle speed adjustment position and the pre-processed forward-looking information as an example, this paper introduces the forward-looking information combined with the vehicle speed adjustment position and the pre-processed forward-looking information.

[0107] In the figure, the orange part is the bumpy road section, and the white part is the flat road section. The dots of different colors represent different types of candidate vehicle speeds. The blue dots are the normal candidate vehicle speeds, and the red dots are the maximum vehicle speed V. max , the green dot is the minimum speed V min The entire road contains N s speed adjustment bit, so the speed adjustment bit of the end point is recorded as Compared with normal driving, the vehicle will change speed more frequently when starting, on bumpy roads, and approaching the end point, so the starting point, end point, and bumpy road section are called "vehicle speed change events". In order to more clearly describe the method of determining the speed adjustment position, part of the road after the starting point and before the end point is omitted, as shown in the dotted road part in the figure. The specific method of determining the speed adjustment position is as follows.

[0108] (1) Under normal circumstances, a vehicle can only travel slowly on a bumpy road section and it is difficult to accelerate or decelerate. Therefore, the present invention assumes that the vehicle speed is constant on a bumpy road section, and each bumpy road section can be regarded as a speed adjustment position, that is, P in the figure. b , P b:1 and P b:8 ;

[0109] (2) To adapt to the frequent changes in vehicle speed when approaching a speed change event, three intervals of l are set before and after each speed change event. k The speed adjustment position is used to change the speed in time, that is, P in the figure 2～4 , P b；3～b；1 , P b:2～b:4 , P b:5～b:7 , P b:9～b:11 and

[0110] (3) When a vehicle passes through a flat road section between two speed change events, it rarely accelerates or decelerates significantly to avoid excessive speed adjustment. Therefore, it is stipulated that except for the speed adjustment position set in (2), no other speed adjustment position is set between two speed change events to avoid frequent acceleration or deceleration of the vehicle.

[0111] At this point, the forward information preprocessing is completed, and the N s Speed ​​adjustment position.

[0112] Step 3: Establish a cost function based on the planning objectives and the generated vertical cost, and design a non-dominated dung beetle multi-objective speed sequence generation method. Finally, combine the pre-processed forward-looking information to generate the optimal speed sequence, as follows:

[0113] 31) When generating a speed sequence, the planning goal 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 goals in this patent are as follows:

[0114] (1) Vertical performance objectives;

[0115] (2) vertical performance objectives;

[0116] (3) Traffic performance objectives;

[0117] 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 bumpy road section during 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) in each sampling period is obtained through the discretized system state space equation (3), and the vertical performance of the corresponding bumpy road section is calculated:

[0118]

[0119] In the formula, c i is the vertical performance of the i-th bumpy road section; N i is the sampling number of the suspension system in the i-th bumpy road section; 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 weight coefficient of suspension dynamic travel; σ3 is the weight coefficient of tire dynamic deflection; l i is the length of the i-th bumpy road section; V i is the speed of the vehicle passing through the i-th bumpy road section; T s Sampling time for the suspension system;

[0120] From this, the cost function of the vertical performance objective can be established as follows:

[0121]

[0122] Where V i is the speed of the vehicle passing through the i-th bumpy road section; Jver(Vi) N is the vertical cost of the vehicle passing through the entire road; b is the number of bumpy sections; c i is the vertical performance of the i-th bumpy road section;

[0123] 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 sum of the absolute values ​​of the longitudinal acceleration of the vehicle on the entire road, reflecting the acceleration and deceleration of the vehicle, as follows:

[0124]

[0125] Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J acc (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad 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; iis the distance from the end point of the i-th speed adjustment position to the starting point of the entire road;

[0126] The traffic performance objective represents the total time it takes for a vehicle to pass through the entire road, and its cost function is expressed as follows:

[0127]

[0128] Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J t (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad 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;

[0129] 32) According to the established cost function, the present invention designs a non-dominated dung beetle multi-objective vehicle speed sequence generation method to generate the optimal vehicle speed sequence, such as Figure 5 As shown. First, generate the initial velocity sequence Z t ; Then, the vehicle speed sequence is iterated to generate the iterated speed sequence K t ; Then, the speed sequence Z before and after the iteration t and K t Perform Pareto non-dominated sorting and divide it into n non-dominated sets F(1)-F(n); finally, select the better k speed sequences in order as the initial speed sequence Z for the next iteration. t:1 The specific steps are as follows:

[0130] 321) Vehicle speed sequence generation;

[0131] Randomly generate k initial vehicle speed sequences Z t ; Each speed sequence is a length equal to the number of speed adjustment bits N s A one-dimensional array of , where the i-th element in the array represents the speed of the vehicle at the i-th speed adjustment position;

[0132] 322) Vehicle speed sequence iteration;

[0133] The vehicle speed sequence can be divided into two parts during iteration: global exploration and local optimization.

[0134] During global exploration, the global exploration sequence is used to find the optimal speed sequence by moving away from the global worst sequence, that is, the speed sequence with the highest cost. The iteration method of the global exploration sequence is as follows:

[0135] Xs i (t+1)=Xs i(t)+α×k×Xs i (t-1)+β×ΔX(t) (10)

[0136]

[0137] ΔX(t)=|Xs i (t)-X w (t)| (12)

[0138] Where t is the current iteration number; Xs i (t) is the i-th search sequence at the t-th iteration; X w (t) is the global worst speed sequence at the tth iteration; α is the coefficient of variation; k is the inertia coefficient; β is the deflection coefficient; η is a random number between zero and one; λ is the probability of variation;

[0139] When searching for local optimization, local search sequences, global search sequences, and hybrid search sequences are used for detailed local optimization. Among them, the local optimal exploration sequence only searches in the local optimal area, while the global search sequence and hybrid exploration sequence search in the global optimal area. Among them, the local optimal area and the global optimal area will change with the number of iterations:

[0140]

[0141] Where, Lb * is the lower bound of the optimal region; Ub * is the upper bound of the local optimal region; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region; t is the current number of iterations; b * (t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration; T max is the maximum number of iterations; V min is the minimum vehicle speed; V max is the maximum vehicle speed;

[0142] The three sequences are iterated in different ways; the local search sequence is iterated as follows:

[0143] Xli(t+1)＝b * (t)+f1×(Xli(t) -Lb * )+f2×(Xli(t) -Ub * ) (14)

[0144] Where t is the current iteration number; Xl i(t) is the i-th local search sequence at the t-th iteration; b * (t) is the local optimal vehicle speed sequence at the tth iteration; f1 and f2 are 1×D random vectors; Lb * is the lower bound of the local optimal region; Ub * is the upper bound of the local optimal region;

[0145] The overall search sequence is iterated as follows:

[0146] Xgi(t+1)＝Xgi(t)+c×(Xgi(t) -Lb a )+d×(Xgi(t) -Ub a ) (15)

[0147] Where t is the current iteration number; Xg i (t) is the i-th overall search sequence at the t-th iteration; c is a 1×D vector that follows a normal distribution; d is a 1×D random vector; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region;

[0148] The iteration of the hybrid search sequence is as follows:

[0149] XC i (t+1)=b a (t)+g×(|Xc i (t)-b * (t)|+|Xc i (t)-b a (t)|) (16)

[0150] Where t is the current iteration number; Xc i (t) is the i-th mixed search sequence at the t-th iteration; b * (t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration; g is a 1×D random vector;

[0151] k initial vehicle speed sequences Z t After iterating through equations (10), (14), (15) and (16), k new vehicle speed sequences K are generated. t ; The 2k vehicle speed sequences before and after the iteration are recorded as X t , where the i-th vehicle speed sequence is X t (i);

[0152] 323) Pareto non-dominated sorting;

[0153] In order to ensure that the number of speed sequences participating in each iteration remains constant, it is necessary to select k speed sequences with smaller cost functions from all speed sequences before and after the iteration for the next iteration. Therefore, it is necessary to perform Pareto non-dominated sorting on all speed sequences, such as Figure 5 As shown in step three.

[0154] First, the vertical performance, longitudinal performance, and traffic performance of all speed sequences are calculated using the established cost function. Then, all speed sequences that are not dominated by other speed sequences are selected and stored in the non-dominated set F(1). t (a) The performance of any planning objective defined in this patent is no better than that of another speed sequence X t (b) Difference, and X t (a) Outperform X on at least one planning objective t (b), then X t (b) Being X t (a) dominates. Then, in all vehicle speed sequences, that is, X t Remove the sequences included in F(1), and select the sequences that are not dominated by other speed sequences from the remaining speed sequences, and store them in the non-dominated set F(2). This process is repeated until all speed sequences are sorted and stored in the non-dominated sets F(1)-F(n). According to the definition and properties of domination, it can be seen that the speed sequence in the non-dominated set F(1) is the best, the speed sequence in F(2) is the second best, and the speed sequence in F(n) is the worst.

[0155] 324) Vehicle speed sequence selection;

[0156] First, store F(i) into the initial velocity sequence Z for the next iteration in the order of the non-dominated set from small to large. t:1 In Figure 5 As shown in step 4. Until F(p) is added, Z t:1 The number of sequences in exceeds the initial number of vehicle speed sequences k. Then, the non-dominated set F(p+1)-F(n) is eliminated, and the concept of congestion is used to sort the sequences in F(p). The congestion represents the density of the surrounding sequences of the selected vehicle speed sequence, such as Figure 6 As shown in the green rectangle, the sequence X t (n) Surrounded by X t (n) A cuboid Q that contains no other sequences. t (n) is represented by the volume. Finally, select the speed sequence with the smallest congestion for the next iteration. If the non-dominated set F(1)-F(p-1) contains m speed sequences, then select the first km speed sequence with the smallest congestion in F(p), that is, F1(p), and store it in Z t:1Make the number of sequences contained in it reach the initial speed sequence number k, and eliminate the remaining speed sequence F2(p). Then Z t:1 The vehicle speed sequence in is used for the next iteration.

[0157] At this point, one iteration of the vehicle speed sequence is completed. After the maximum number of iterations T max After iterations, the optimal vehicle speed sequence V can be obtained. opt .

[0158] Embodiment 2

[0159] In order to verify the effectiveness and superiority of the method proposed in the present invention, the following Figure 7 The road shown in the figure is simulated and verified. The total length of the road is 1450m, of which 300m-500m, 400m-450m and 900m-1050m are bumpy sections, and the rest are flat sections. The conventional dung beetle optimizer is compared with the method proposed in the present invention, and the speed sequence comparison diagram obtained is shown in FIG. Figure 7 Shown in the blue and orange curves.

[0160] It can be seen that the speed sequence planned by the multi-objective speed planning method proposed in the present invention changes smoothly throughout the entire road, without drastic acceleration and deceleration, and most of the speed sequences are above the speed sequence planned by the traditional dung beetle optimizer. This shows that the speed planning method proposed in the present invention can significantly improve the vertical performance, longitudinal performance and traffic performance of the vehicle, and can improve the driving comfort and traffic efficiency of the vehicle.

[0161] 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 multi-objective speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency, 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: The smart car uses the global road information obtained by the Internet of Vehicles to divide the road sections and generate the speed adjustment position; Step 3: Establish a cost function based on the planning objectives and the generated vertical cost, and design a non-dominated dung beetle multi-objective speed sequence generation method. Finally, combine the pre-processed forward-looking information to generate the optimal speed sequence.

2. The multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to claim 1 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; 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, and 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. The multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to claim 1 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 and flat sections; 22) The entire road to be driven is divided into a number of speed adjustment positions; each speed adjustment position is provided with a speed adjustment interval of δV, from the lowest speed V min To the maximum speed V max Multiple candidate vehicle speeds.

4. The multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to claim 3 is characterized in that: The specific method for determining the vehicle speed adjustment position is as follows: (1) Assuming that the vehicle's speed is constant on the bumpy road section, each bumpy road section is regarded as a speed adjustment position; (2) Set three intervals of l before and after each vehicle speed change event k The speed adjustment position is used to change the speed in time; (3) 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. The multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to claim 1 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 through the discretized system state space equation (3), and the vertical performance of the corresponding bumpy road section is calculated as follows: In the formula, c i is the vertical performance of the i-th bumpy road section; N i is the sampling number of the suspension system in the i-th bumpy road section; 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 weight coefficient of suspension dynamic travel; σ3 is the weight coefficient of tire dynamic deflection; l i is the length of the i-th bumpy road section; V i is the speed of the vehicle passing through the i-th bumpy road section; T s Sampling time for the suspension system; The cost function of the vertical performance objective is established as follows: Where V i is the speed of the vehicle passing through the i-th bumpy road section; J ver (V i ) is the vertical cost of the vehicle passing through the entire road; N b is the number of bumpy sections; c i is the vertical performance of the i-th bumpy road section; The cost function that defines the longitudinal performance target is the sum of the absolute values ​​of the longitudinal acceleration of the vehicle on the entire road, reflecting the acceleration and deceleration of the vehicle, as shown below: Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J acc (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad 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; The traffic performance target represents the total time it takes for a vehicle to pass through the entire road, and the cost function is expressed as follows: Where V i is the speed of the vehicle passing through the i-th speed adjustment position; J t (V i ) is the longitudinal cost of the vehicle passing the entire road; N ad Ls is the number of vehicle speed adjustment positions; i is the distance from the starting point of the i-th speed adjustment position to the starting point of the entire road, Le i is the distance from the end point of the i-th speed adjustment position to the starting point of the entire road; 32) According to the established cost functions of the vertical performance target, the longitudinal performance target and the traffic performance target, a non-dominated dung beetle multi-objective vehicle speed sequence generation method is designed to generate the optimal vehicle speed sequence; first, an initial speed sequence Z is generated t ; Then, the vehicle speed sequence is iterated to generate the iterated speed sequence K t ; Then, the speed sequence Z before and after the iteration t and K t Perform Pareto non-dominated sorting and divide it into n non-dominated sets F(1)-F(n); finally, select k vehicle speed sequences with smaller cost functions in order as the initial speed sequence Z for the next iteration. t+1 .

6. The multi-objective vehicle speed planning method for intelligent vehicles oriented to driving comfort and traffic efficiency according to claim 5 is characterized in that: The specific method of step 32) is as follows: 321) Generate vehicle speed sequence; Randomly generate a sequence Z containing k initial vehicle speeds t ; Each speed sequence is a length equal to the number of speed adjustment bits N s A one-dimensional array of , where the i-th element in the array represents the speed of the vehicle at the i-th speed adjustment position; 322) Iterate the vehicle speed sequence; The vehicle speed sequence is divided into two parts during iteration: global exploration and local optimization; During global exploration, the global exploration sequence is used to find the optimal speed sequence by moving away from the global worst sequence, that is, the speed sequence with the highest cost. The iteration method of the global exploration sequence is as follows: Xs i (t+1)=Xs i (t)+α×k×Xs i (t-1)+β×ΔX(t) (10) ΔX(t)=|Xs i (t)-X w (t)| (12) Where t is the current iteration number; Xs i (t) is the i-th search sequence at the t-th iteration; X w (t) is the global worst speed sequence at the tth iteration; α is the coefficient of variation; k is the inertia coefficient; β is the deflection coefficient; η is a random number between zero and one; λ is the probability of variation; In local optimization, local search sequences, global search sequences and hybrid search sequences are used for detailed local optimization; the local optimal exploration sequence searches only in the local optimal area, while the global search sequence and hybrid exploration sequence search in the global optimal area; the local optimal area and the global optimal area change with the number of iterations: Where, Lb * is the lower bound of the optimal region; Ub * is the upper bound of the local optimal region; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region; t is the current number of iterations; b * (t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration; T max is the maximum number of iterations; V min is the minimum vehicle speed; V max is the maximum vehicle speed; The three sequences, local search sequence, global search sequence, and hybrid search sequence, are iterated in different ways; the iterative method of the local search sequence is as follows: Xl i (t+1)=b * (t)+f1×(Xl i (t)-Lb * )+f2×(Xl i (t)-Ub * ) (14) Where t is the current iteration number; Xl i (t) is the i-th local search sequence at the t-th iteration; b * (t) is the local optimal speed sequence at the tth iteration; f1 and f2 are 1×D random vectors; Lb * is the lower bound of the local optimal region; Ub * is the upper bound of the local optimal region; The overall search sequence is iterated as follows: Xg i (t+1)=Xg i (t)+c×(Xg i (t)-Lb a )+d×(Xg i (t)-Ub a ) (15) Where t is the current iteration number; Xg i (t) is the i-th overall search sequence at the t-th iteration; c is a 1×D vector that follows a normal distribution; d is a 1×D random vector; Lb a is the lower bound of the overall optimal region; Ub a is the upper bound of the overall optimal region; The iteration of the hybrid search sequence is as follows: Xc i (t+1)=b a (t)+g×(|Xc i (t)-b * (t)|+|Xc i (t)-b a (t)|) (16) Where t is the current iteration number; Xc i (t) is the i-th mixed search sequence at the t-th iteration; b * (t) is the local optimal vehicle speed sequence at the tth iteration; b a (t) is the global optimal vehicle speed sequence at the tth iteration, g is a 1×D random vector; k initial vehicle speed sequences Z t After iterating through equations (10), (14), (15) and (16), k new vehicle speed sequences K are generated. t ; The 2k vehicle speed sequences before and after the iteration are recorded as X t , where the i-th vehicle speed sequence is X t (i); 323) Perform Pareto non-dominated sorting; From all the speed sequences before and after the iteration, k speed sequences with smaller cost functions are selected for the next iteration; therefore, all speed sequences need to be Pareto non-dominated sorted as follows: First, the vertical performance, longitudinal performance, and traffic performance of all speed sequences are calculated through the established cost function. Then, all the sequences that are not dominated by other speed sequences are selected and stored in the non-dominated set F(1). t (a) The performance of any defined planning objective is no better than that of another speed sequence X t (b) Difference, and X t (a) Outperform X on at least one planning objective t (b), then X t (b) Being X t (a) dominates; then, in all vehicle speed sequences, i.e., X t Remove the sequences included in F(1), and select the sequences that are not dominated by other speed sequences from the remaining speed sequences and store them in the non-dominated set F(2); and so on, until all speed sequences are sorted and stored in the non-dominated sets F(1)-F(n); according to the definition and properties of domination, it can be seen that the speed sequence in the non-dominated set F(1) is the best, the speed sequence in F(2) is the second best, and the speed sequence in F(n) is the worst; 324) Select vehicle speed sequence; First, store F(i) into the initial velocity sequence Z for the next iteration in the order of the non-dominated set from small to large. t+1 Until F(p) is added, Z t+1 The number of sequences in F(p) exceeds the number of initial speed sequences k; then, the non-dominated set F(p+1)-F(n) is eliminated, and the concept of congestion is used to sort the sequences in F(p); the congestion represents the density of the surrounding sequences of the selected speed sequence, and the sequence X is used t (n) Surrounded by X t (n) A cuboid Q that contains no other sequences. t (n) volume representation; finally, select the speed sequence with the smallest congestion for the next iteration; if the non-dominated set F(1)-F(p-1) contains m speed sequences, then select the first km speed sequence with the smallest congestion in F(p), that is, F1(p), and store it in Z t+1 Make the number of sequences contained in it reach the initial speed sequence number k, and eliminate the remaining speed sequence F2(p), and then Z t+1 The vehicle speed sequence in is used for the next iteration.

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