Hybrid traffic system for suppressing traffic random disturbance and control method and equipment thereof

Abstract

The invention relates to the technical field of intelligent traffic control, in particular to a hybrid traffic system for suppressing traffic random disturbance and a control method and equipment thereof. Constructing a manual driving vehicle following model; constructing a hybrid traffic system control model comprising an automatic driving vehicle pair according to a hybrid traffic scene formed by the manual driving vehicle and the automatic driving vehicle; according to the full speed difference model and the hybrid traffic control model, constructing a two-way automatic driving vehicle following model based on the automatic driving vehicle pair; and in the hybrid traffic system control model, according to the following model of the automatic driving vehicle and the following model of the manual driving vehicle, controlling the driving of the automatic driving vehicle based on the constraint of the control obstacle function on the automatic driving vehicle pair. According to the method, the influence of disturbance on the current traffic system is avoided, and the safety is improved.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent traffic control, and in particular to a hybrid traffic system for suppressing random traffic disturbances and a control method and equipment thereof. Background Art

[0002] With the acceleration of urbanization and the diversification of transportation, modern transportation systems have become more and more complex. At the same time, there are more and more traffic problems such as safety accidents and congestion. Intelligent and automated transportation systems can perceive traffic conditions in real time, automatically adjust traffic signals, and optimize traffic flow, thereby improving traffic efficiency, reducing traffic congestion and traffic accidents. In the early days, the main research was on adaptive cruise control (ACC), but this control method has strong dependence and poor environmental adaptability. In addition, long-term reliance on the ACC system may change the driver's driving habits, resulting in a decrease in the driver's driving skills and reaction ability when the system fails to work (such as system failure or old models not equipped with ACC). Therefore, it is not very applicable. Connected and Automated Vehicle (CAV) avoids accidents by changing its own movement to maintain the best safe distance from the vehicle in front, thus providing new opportunities for the intelligentization and automation of transportation systems. For multiple CAVs, cooperative adaptive cruise control (CACC) can be used to enhance the connection between each CAV. This method forms a group of CAVs into a queue and uses cooperative control strategies to reduce spacing, improve fuel efficiency, and improve vehicle traffic capacity to ease traffic pressure. In the critical period of smart transportation construction, how to make full use of the real-time information collected by CAVs and their collaborative capabilities is an important prerequisite for realizing smart travel.

[0003] However, due to the immaturity and imperfection of standards and supporting regulations, human-driven vehicles (HDV) still play an important role in the use of road traffic systems and consumer market demand. Therefore, in the long term, CAVs will participate in the traffic system together with HDVs to form a new type of mixed traffic flow. At present, many scholars' research on this mixed traffic system mostly considers a single CAV as an intelligent agent to guide a mixed traffic system consisting of a CAV and multiple HDVs, thus ignoring the interaction between CAVs; at the same time, due to the random disturbance of urban traffic caused by the uncertain driving behavior of HDVs, the safety of the mixed traffic system faces huge challenges. Summary of the invention

[0004] The present invention provides a hybrid traffic system for suppressing random traffic disturbances and a control method and device thereof, aiming to solve the problem that insufficient consideration of CAV and HDV in the current hybrid traffic system leads to greater traffic interference and affects vehicle driving safety.

[0005] The objective of the present invention is achieved by the following technical solutions:

[0006] A driving control method for a mixed traffic system for suppressing random traffic disturbances, comprising:

[0007] Construct a car-following model for manually driven vehicles based on the full speed difference model;

[0008] According to a mixed traffic scenario consisting of manually driven vehicles and autonomous driving vehicles, a mixed traffic system control model including an autonomous driving vehicle pair is constructed, wherein the autonomous driving vehicle pair refers to two autonomous driving vehicles in the mixed traffic scenario that are paired within a communication range for collaborative control;

[0009] A two-way autonomous driving vehicle following model based on autonomous driving vehicle pairs is constructed according to the full speed difference model and the mixed traffic control model.

[0010] In the mixed traffic system control model, according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, the driving of the autonomous driving vehicle is controlled based on the constraints of the control obstacle function on the autonomous driving vehicle pair.

[0011] As a further improvement of the present invention, the method of constructing a car-following model for a manually driven vehicle based on a full speed difference model includes: the full speed difference model is:

[0012]

[0013] In the formula, v n (t) is the speed of the nth vehicle at time t, s n is the distance between the front vehicle and the following vehicle. is the speed difference between the leading vehicle and the following vehicle, α is the driver sensitivity coefficient, is the expected speed related to the distance between the front and rear vehicles and the speed difference, and β is the speed difference influence coefficient.

[0014] As a further improvement of the present invention, the expected speed related to the distance between the front and rear vehicles and the speed difference is:

[0015]

[0016] In the formula, is the expected speed, s i is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, s go is the maximum spacing, v max is the maximum speed of the vehicle, σ is the weight factor, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

[0017] As a further improvement of the present invention, the construction of a hybrid traffic system control model including an autonomous driving vehicle pair includes: respectively determining an equilibrium state, a system state, a system input, and a state space in the hybrid traffic system control model, wherein the hybrid traffic system control model takes a desired state as a control target, is adjusted based on an input signal and a system state in a control input, and is controlled through a state space;

[0018] The equilibrium state is:

[0019] F(s * ,0,v * )=0

[0020] Among them, v * is the expected speed, s * To expect a constant vehicle distance;

[0021] The system status is:

[0022]

[0023] Among them, x(t) is the state of the mixed traffic system, x(t)∈R 2N , R is the number field, They represent the spacing error and speed error of the i-th vehicle respectively;

[0024] The control input is:

[0025]

[0026] Among them, u i (t) is the control input of the autonomous vehicle indexed as i, consisting of the acceleration signal of the autonomous vehicle pair;

[0027] The state space is:

[0028]

[0029] Where A is the system state coefficient matrix, which is obtained according to the driver sensitivity coefficient, B is the control input coefficient matrix, and H is the vehicle speed coefficient matrix. is the speed error of the leading vehicle.

[0030] As a further improvement of the present invention, the bidirectional autonomous driving vehicle following model is constructed according to the full speed difference model and the hybrid traffic control model, including: obtaining the reverse expected speed and the forward expected speed of the autonomous driving vehicle according to the full speed difference model, and obtaining the bidirectional autonomous driving vehicle following model according to the forward expected speed and the reverse expected speed;

[0031] The reverse expected speed is:

[0032]

[0033] in, is the expected speed function of the reverse vehicle, s is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, s go is the maximum spacing, v max is the maximum speed of the vehicle, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

[0034] As a further improvement of the present invention, after constructing the bidirectional autonomous driving vehicle following model according to the full speed difference model and the mixed traffic control model, the method further includes controlling the autonomous driving vehicle based on the bidirectional autonomous driving vehicle following model, including:

[0035]

[0036] in, are the distance control gains between the autonomous driving vehicle pair and the manually driven vehicle, are the speed control gains of the autonomous driving vehicle pair relative to the manually driven vehicle, i1 is the first vehicle in the autonomous driving vehicle pair, i2 is the first vehicle in the autonomous driving vehicle pair, j is the manually driven vehicle, is the relative distance between the manual driving vehicle and the automatic driving vehicle, is the speed difference between the manual vehicle and the automatic vehicle, is the expected speed function of the autonomous vehicle.

[0037] As a further improvement of the present invention, the vehicle expected speed function adopts different expected speed functions according to different following directions, that is:

[0038]

[0039] Among them, i is the number of the current autonomous driving vehicle, and j is the number of the following vehicle.

[0040] As a further improvement of the present invention, the constraint on the autonomous driving vehicle pair based on the control obstacle function, controlling the driving of the autonomous driving vehicle, includes: splitting the autonomous driving vehicle pair into two subsystems, and the two subsystems respectively satisfy the constraint conditions based on the control obstacle function; the control obstacle function is:

[0041]

[0042] in, is the control obstacle function, τ is the vehicle reaction time, s i is the relative distance between the i-1th vehicle and the i-th vehicle, is the relative distance between the i-1th vehicle and the i-th autonomous driving vehicle, is the speed of the i-th autonomous driving vehicle, v i is the speed of the i-th vehicle;

[0043] The constraint condition based on the control barrier function is:

[0044]

[0045] In the formula, is the function h i About vector f i The Lie derivative of satisfies u′ is the control input of the autonomous driving vehicle, h i The gradient of are coefficient matrices respectively.

[0046] In a second aspect, the present invention further provides a hybrid traffic system for suppressing random traffic disturbances, comprising:

[0047] A manually driven vehicle following module, including a manually driven vehicle following model, which is constructed based on a full speed difference model;

[0048] A hybrid traffic system control model building module, which builds a hybrid traffic system control model including a pair of autonomous driving vehicles according to a hybrid traffic scenario consisting of manually driven vehicles and autonomous driving vehicles. The autonomous driving vehicle pair refers to two autonomous driving vehicles in the hybrid traffic scenario that are paired within a communication range for collaborative control;

[0049] An autonomous driving vehicle following model building module is used to build a two-way autonomous driving vehicle following model based on an autonomous driving vehicle pair according to a full speed difference model and a mixed traffic control model;

[0050] The vehicle control module is used to control the driving of the autonomous driving vehicle in the mixed traffic system control model based on the constraints of the autonomous driving vehicle pair imposed by the control obstacle function according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle.

[0051] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the driving control method for a hybrid traffic system that suppresses random traffic disturbances as described above.

[0052] The beneficial effects of the present invention are as follows: a driving control method for a hybrid traffic system that suppresses random traffic disturbances in the present invention constructs a following model for manually driven vehicles based on a full speed difference model through the following characteristics of the vehicle, so that the description of manually driven vehicles is closer to the actual situation. The mixed traffic scene composed of manually driven vehicles and autonomous driving vehicles is considered to construct a control model for a hybrid traffic system including autonomous driving vehicle pairs, which effectively simplifies the description of vehicle dynamic information. At the same time, after pairing, two autonomous driving vehicles can exchange information within the communication range, thereby obtaining more accurate information about manually driven vehicles. A two-way autonomous driving vehicle following model is constructed through the full speed difference model and the following behavior under the hybrid traffic system. At the same time, when controlling the autonomous driving vehicle, constraints based on the control obstacle function are introduced for safety constraints to ensure that collisions caused by sudden acceleration or deceleration of the following vehicle are avoided, thereby improving the overall safety of the hybrid traffic system.

[0053] Furthermore, the full speed model proposed in the present invention is improved based on the existing full speed model, and the speed difference term is added to the expected speed expression. The influence of speed difference and headway on the vehicle movement speed under different weights is considered according to the scene requirements, and the real-time information obtained is fully utilized to more accurately describe the dynamic changes of HDV during following.

[0054] Furthermore, quadratic programming is used to incorporate the safety of manually driven vehicles and autonomous driving vehicles into the controller design. That is, the control obstacle function is used to safely screen the nominal control input of the autonomous driving vehicle, thereby ensuring the non-negative distance between vehicles and guaranteeing safety issues during the following process. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1is a mixed traffic scene diagram containing a CAV pair in an embodiment of the present invention;

[0057] Figure 2 is a general mixed traffic system diagram based on CAV pairs in an embodiment of the present invention;

[0058] Figure 3 is a flow chart of a method in an embodiment of the present invention;

[0059] Figure 4 It is a diagram of the expected speed function of the improved car-following model in an embodiment of the present invention;

[0060] Figure 5 = driver sensitivity coefficient α-head distance Δx in the embodiment of the present invention e Stability curve diagram in space (σ=0.4);

[0061] Figure 6 = driver sensitivity coefficient α-head distance Δx in the embodiment of the present invention e Stability curve diagram in space (β = 0.05);

[0062] Figure 7 It is a diagram of the expected speed function of the reverse improved car-following model in an embodiment of the present invention;

[0063] FIG8 is a diagram showing the motion trajectories of vehicles under two controllers when the leading vehicle suddenly brakes in an embodiment of the present invention; wherein (a) is the motion trajectory of vehicles in a mixed traffic system under a nominal controller, and (b) is the motion trajectory of vehicles in a mixed traffic system under a CBF-based controller;

[0064] Figure 9 shows the vehicle motion trajectories of the two controllers when the rear vehicle suddenly accelerates; (a) is the vehicle motion trajectory of the mixed traffic system under the nominal controller, and (b) is the vehicle motion trajectory of the mixed traffic system under the CBF-based controller;

[0065] Fig.10 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] Terminology explanation:

[0068] HDV: Human-driven vehicle;

[0069] CAV: Autonomous driving vehicle;

[0070] FVD: Full velocity difference model;

[0071] CBF: Control Barrier Function (CBF).

[0072] The idea of ​​the present invention is to provide a hybrid traffic system and a control method and device thereof for suppressing random traffic disturbances. The method of the present invention mainly includes: Firstly, a car-following model of manually driven vehicles is constructed based on the full speed difference model.

[0074] Firstly, a car-following model of manually driven vehicles is constructed based on the full speed difference model.

[0075] Among them, the full speed difference model is:

[0076]

[0077] In the formula, v n (t) is the speed of the nth vehicle at time t, s n is the distance between the front vehicle and the following vehicle. is the speed difference between the leading vehicle and the following vehicle, α is the driver sensitivity coefficient, is the expected speed related to the distance between the front and rear vehicles and the speed difference, and β is the speed difference influence coefficient.

[0078] The expected speed in the formula is:

[0079]

[0080] In the formula, is the expected speed, s i is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, s go is the maximum spacing, v max is the maximum speed of the vehicle, σ is the weight factor, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

[0081] Secondly, according to the mixed traffic scenario consisting of manually driven vehicles and autonomous driving vehicles, a mixed traffic system control model including an autonomous driving vehicle pair is constructed. The autonomous driving vehicle pair refers to two autonomous driving vehicles in the mixed traffic scenario that are paired within the communication range for collaborative control.

[0082] Specifically, the framework of the hybrid traffic system control model includes equilibrium state, system state, system input and state space, so the equilibrium state, system state, system input and state space in the hybrid traffic system control model are determined respectively. The hybrid traffic system control model takes the desired state as the control target, adjusts based on the input signal and system state in the control input, and controls through the state space.

[0083] The equilibrium state is:

[0084] F(s * ,0,v * )=0

[0085] Among them, v * is the expected speed, s * To expect a constant vehicle distance;

[0086] The system status is:

[0087]

[0088] Among them, x(t) is the state of the mixed traffic system, x(t)∈R 2N , R is the number field, They represent the spacing error and speed error of the i-th vehicle respectively;

[0089] The control input is:

[0090]

[0091] Among them, u i (t) is the control input of the autonomous vehicle indexed as i, consisting of the acceleration signal of the autonomous vehicle pair;

[0092] The state space is:

[0093]

[0094] Where A is the system state coefficient matrix, which is obtained according to the driver sensitivity coefficient, B is the control input coefficient matrix, and H is the vehicle speed coefficient matrix. is the speed error of the leading vehicle.

[0095] Then, a two-way autonomous driving vehicle following model based on autonomous driving vehicle pairs is constructed according to the full speed difference model and the mixed traffic control model.

[0096] The two-way autonomous driving vehicle following model includes the reverse expected speed and the forward expected speed, namely:

[0097]

[0098] Among them, i is the number of the current autonomous driving vehicle, and j is the number of the following vehicle.

[0099] The forward expected speed is obtained by the above formula, and the reverse expected speed is:

[0100]

[0101] in, is the expected speed function of the reverse vehicle, s is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, s go is the maximum spacing, v max is the maximum speed of the vehicle, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

[0102] After building a two-way autonomous driving vehicle following model according to the full speed difference model and the mixed traffic control model, the method further includes controlling the autonomous driving vehicle based on the two-way autonomous driving vehicle following model, including:

[0103]

[0104] in, are the distance control gains between the autonomous driving vehicle pair and the manually driven vehicle, are the speed control gains of the autonomous driving vehicle pair relative to the manually driven vehicle, i1 is the first vehicle in the autonomous driving vehicle pair, i2 is the first vehicle in the autonomous driving vehicle pair, j is the manually driven vehicle, is the relative distance between the manual driving vehicle and the automatic driving vehicle, is the speed difference between the manual vehicle and the automatic vehicle, is the expected speed function of the autonomous vehicle.

[0105] Finally, in the mixed traffic system control model, according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, the driving of the autonomous driving vehicle is controlled based on the constraints of the control obstacle function on the autonomous driving vehicle pair.

[0106] Based on the constraint of the control obstacle function on the autonomous driving vehicle pair, the autonomous driving vehicle is controlled to travel, including: splitting the autonomous driving vehicle pair into two subsystems:

[0107]

[0108] The two subsystems satisfy the constraints based on the control barrier function respectively; the control barrier function is:

[0109]

[0110] Among them, h i (x) is the control obstacle function, τ is the vehicle reaction time, s i is the relative distance between the i-1th vehicle and the i-th vehicle, is the relative distance between the i-1th vehicle and the i-th autonomous driving vehicle, is the speed of the i-th autonomous driving vehicle, v i is the speed of the i-th vehicle;

[0111] The constraint condition based on the control barrier function is:

[0112]

[0113] In the formula, is the function h i About vector f i The Lie derivative of satisfies is the control input of the autonomous vehicle, h i The gradient of are coefficient matrices respectively.

[0114] Based on the constraint of the control obstacle function, the control obstacle function is embedded into the control of the autonomous driving vehicle through quadratic programming:

[0115] Subsystem i1:

[0116]

[0117] Subsystem i2:

[0118]

[0119] CBF can be viewed as a “filter” that can filter out control inputs that violate safety. * It is the closest safety control input after being screened by CBF.

[0120] The driving control method for a mixed traffic system for suppressing random traffic disturbances disclosed in this method significantly enhances the ability to suppress traffic fluctuations, while effectively improving the anti-interference ability of the mixed traffic system and ensuring the safety of the mixed traffic system.

[0121] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments, wherein the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0122] Example 1

[0123] like Figure 1-Figure 3 A driving control method for a mixed traffic system with random traffic disturbances shown in the figure mainly comprises the following steps:

[0124] S1. Construct a car-following model for manually driven vehicles based on the full speed difference model.

[0125] Assume that the position, velocity and acceleration of the nth vehicle at time t are denoted by p n (t), v n (t), a n (t), the driver’s response is usually expressed in terms of acceleration, and the distance between the acceleration vehicles is s. n (t), driver speed v n (t) and the speed difference The full speed difference model is:

[0126]

[0127] In the formula, v n (t) is the speed of the nth vehicle at time t, s n =p n-1 -p n is the distance between the front vehicle and the following vehicle. is the speed difference between the leading vehicle and the following vehicle, α is the driver sensitivity coefficient, is the expected speed related to the distance between the front and rear vehicles and the speed difference, and β is the speed difference influence coefficient.

[0128] The expected speed is related to the distance between the front and rear vehicles and the speed difference is defined as:

[0129]

[0130] The expected speed It is composed of two terms: the distance dominant term and the speed difference dominant term. s is the speed term related to the distance between the vehicles ahead. When the distance s is less than the minimum distance s st becomes 0 when the spacing s is greater than the maximum spacing s go The maximum value v max , when the spacing s is between the minimum spacing and the maximum spacing, V s is a monotonically increasing function. is the speed difference between the front and rear vehicles, and σ is the weight factor.

[0131] For HDV, this method uses an improved full speed difference model to describe the HDV's car-following behavior. The nonlinear dynamic model is expressed as follows:

[0132]

[0133] In the formula, α h ,β h are the response gains of the HDV driver to the expected speed error and the speed error of the preceding vehicle, respectively. Indicates the expected speed related to the distance between the front and rear vehicles and the speed difference.

[0134] like Figure 4 As shown, the expected speed function of the vehicle in the improved car-following model is It is adjusted by the distance between the front and rear vehicles and the speed difference. When the distance increases, the expected speed approaches the maximum speed of the vehicle; when the distance decreases, the expected speed will be reduced to maintain a safe distance from the front vehicle. At the same time, if the speed of the rear vehicle is greater than that of the front vehicle, the expected speed should be increased to avoid collision; if the speed of the rear vehicle is too low, the expected speed of the current vehicle will be reduced to maintain the cohesion of the following system.

[0135] When considering the vehicle following model, its stability should also be considered, and the stability of the vehicle following model refers to the ability of the vehicle queue to respond to small disturbances in a relatively stable or balanced state. This embodiment analyzes the stability of the vehicle following model of the manually driven vehicle by adding a disturbance factor, specifically including:

[0136] In the equilibrium state, the vehicles in the mixed vehicle queue have the same vehicle spacing s. e and speed v e ,Right now

[0137] f(s e ,v e ,0)=0

[0138] In this embodiment, the source of the small disturbance is defined as the spacing change y in the equilibrium state. n (t) is as follows:

[0139] y n (t) = s n (t)-s e

[0140] Substituting the disturbance into the car-following model of a manually driven vehicle, the linearization can be obtained:

[0141] y”(t)=α{V'(s e )(y n+1 (t)-y n (t))+V'(0)(y' n+1 (t)-y' n (t))-y' n (t)}+β(y' n+1 (t)-y' n (t))

[0142] y n (t) = e ikn+zt Substituting into the linearized model, the periodic function form of the disturbance can be obtained as follows:

[0143] z 2 e ikn+zt =α{V'(Δx e )(e ik(n+1)+zt -e ikn+zt )+V'(0)(ze ik(n+1)+zt -ze ikn+zt )-ze ikn+zt}+β(ze ik(n+1)+zt -ze ikn+zt )

[0144] Simplify to get the following formula:

[0145] z 2 =α{V'(s e )(e ik -1)+V'(0)(ze ik -z)-z}+β(ze ik -z)

[0146] Substitute z = z1ik + z2(ik) 2 +..., in order to solve the imaginary and real coefficients of the differential equation with perturbations:

[0147] z1=V'(s e )

[0148]

[0149] When the imaginary coefficient is less than 0, that is, z2<0, a critically stable state can be obtained:

[0150]

[0151] In order to further explore the influence of the coefficient on the stability of the car-following model, we take v max =30m / s,s st =5m,s go =35m. Figure 5 is the driver sensitivity coefficient α and the headway distance s in the following model under different speed difference influence coefficients β when the weight factor σ is constant e From the stability curve in space, it can be seen that when σ is constant, the stable area of ​​the car-following model increases as the speed difference influence coefficient β increases.

[0152] Figure 6 When β is constant, under different weight factors σ, the driver sensitivity coefficient α and the headway distance s in the following model aree From the stability curve in space, we can see that when the speed difference influence coefficient β is constant, the stability region of the car-following model decreases as the weight factor σ increases, that is, the speed difference has an influence on the expected speed. The greater the influence of , the more stable the model.

[0153] S2. Constructing a hybrid traffic system control model including a pair of autonomous driving vehicles according to a hybrid traffic scenario consisting of manually driven vehicles and autonomous driving vehicles. The pair of autonomous driving vehicles refers to two autonomous driving vehicles in the hybrid traffic scenario that are paired within a communication range for collaborative control.

[0154] Aiming at the limitation of a single CAV in a mixed traffic system in suppressing traffic waves, this method proposes a control strategy for a mixed traffic system based on CAV pairs. Two CAVs within the communication range pair up and guide the mixed traffic system. The CAV at the front of the system guides the vehicle speed of the entire mixed traffic system based on the local traffic situation and demand; the CAV at the rear of the queue perceives the dynamics of the preceding vehicle in advance based on the feedback information of the preceding vehicle and the preceding CAV and adjusts the vehicle speed in time, thereby suppressing the spread of traffic waves caused by HDVs.

[0155] Mixed traffic scenarios based on CAV pairs such as Figure 1 As shown in Figure 2, two adjacent CAVs form a cooperatively controlled CAV pair within their communication range to guide the movement of the mixed traffic system. Figure 1 When the CAV with the lightest color in the figure is out of the communication range with other CAVs, the coupling constraint between the CAV pairs is released, and the unmatched CAVs implement the single-vehicle following strategy. Figure 2 As shown in the figure, the system consists of N vehicles forming the basic unit of the mixed traffic system, including m+n+l HDVs and a CAV pair, where the CAV pair is To represent the set of CAVs, A set of indices representing all vehicles.

[0156] The control objective of the mixed traffic system control model is to make all vehicles maintain the same desired speed and constant vehicle spacing. * ,v * ) as the system control target, where the equilibrium state can be expressed as follows:

[0157] F(s * ,0,v * )=0

[0158] Among them, v * is the expected speed, s * To achieve constant vehicle distance

[0159] The state expression calculation method of the mixed traffic system control model is:

[0160] First, define the error between the actual state and the equilibrium state:

[0161]

[0162] in, Denote the spacing error and speed error of the i-th vehicle respectively. The error states of all vehicles are combined into the mixed traffic system state x(t)∈R 2(m+n+l+2) , R is a set of real numbers, given by:

[0163]

[0164] In the hybrid traffic system control model, the CAV following behavior is controllable, so the acceleration of the CAV can be set as the control input of the hybrid traffic system control model, that is, The acceleration signal of the CAV pair is composed into the control input set u(t)∈R 2 , which can be expressed as follows:

[0165]

[0166] Among them, u i (t) is the CAV control input with index i.

[0167] The state space model of the hybrid traffic system control model is:

[0168]

[0169] in, is the speed error of the leading vehicle, A is the system state coefficient matrix, which is obtained according to the driver sensitivity coefficient, B is the control input coefficient matrix, H is the vehicle speed coefficient matrix, and the matrix A∈R 2N×2N ,B∈R 2N×2 ,H∈R 2N×1 , where R is a set of real numbers. Matrices A, B, and H all satisfy:

[0170]

[0171] In the formula, is the vector that constitutes the system input coefficient matrix B, are the vectors that make up the matrix H.

[0172] The state space model of the hybrid traffic system control model is obtained based on the linearized model of HDV. The specific operation method is:

[0173] Obtain the linearized model of HDV, combine the error and perform Taylor expansion on the HDV longitudinal dynamics equation, and obtain the following linearized model expression:

[0174]

[0175] in, When using the FVD model, the coefficients in the full speed calculation formula are transformed into

[0176] For CAV, consider a second-order model:

[0177]

[0178] Based on the above content, the linearized state space model of the mixed traffic system can be derived.

[0179] S3. A bidirectional autonomous driving vehicle following model based on an autonomous driving vehicle pair is constructed based on the full speed difference model and the hybrid traffic control model. A CAV pair controller of a bidirectional multi-vehicle following topology is considered based on the bidirectional CAV following model. The bidirectional autonomous driving vehicle following model has different following directions, and is divided into a forward following model and a reverse following model. The expected speed of the forward following model is:

[0180]

[0181] Based on the expected speed of the forward car-following model, the expected speed of the reverse car-following model is first determined, namely:

[0182]

[0183] In the formula, Continuous and monotonically decreasing, that is, the CAV follows the opposite strategy to the front and rear vehicles. That is, when the distance between the CAV and the rear vehicle is small, the vehicle maintains a larger expected speed for safety reasons. When the distance between the CAV and the rear vehicle gradually increases, considering the cohesive performance of the mixed traffic system, the CAV will gradually slow down or even stop to wait for the rear vehicle to catch up, to avoid being unable to follow under random disturbances. Improved inverse FVD model expected speed function V s_ Image as Figure 7 shown.

[0184] Compared with the limited system scale and optimization effect of a mixed traffic system under the guidance of a single CAV, a CAV pair has a broader traffic vision and more levels of control capabilities. Therefore, this embodiment designs a bidirectional multi-vehicle following topology controller for CAV pairs, which is expressed as:

[0185]

[0186] in, are the distance control gains between the autonomous driving vehicle pair and the manually driven vehicle, are the speed control gains of the autonomous driving vehicle pair relative to the manually driven vehicle, i1 is the first vehicle in the autonomous driving vehicle pair, i2 is the first vehicle in the autonomous driving vehicle pair, j is the manually driven vehicle, is the relative distance between the manual driving vehicle and the automatic driving vehicle, is the speed difference between the manual vehicle and the automatic vehicle, is the expected speed function of the autonomous vehicle.

[0187] Then, according to different following directions, the expected speed function of the autonomous driving vehicle is divided into the following two cases:

[0188]

[0189] Among them, i is the number of the current autonomous driving vehicle, and j is the number of the following vehicle.

[0190] This application controls the mixed traffic system based on the information transmission and mutual coordination between CAV pairs, so it only controls the HDVs in the system based on the coordination between CAVs. For example, when one of the vehicles suddenly brakes, the CAVs nearby will sense the speed change of the HDV in time and make corresponding deceleration actions to avoid large-scale speed fluctuations and collisions caused by deceleration.

[0191] S4. In the mixed traffic system control model, according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, the driving of the autonomous driving vehicle is controlled based on the constraints imposed on the autonomous driving vehicle by the control obstacle function.

[0192] Among them, the control input u of the hybrid traffic system control model is optimized by the controller to obtain the CAV controller control result u * , the obtained u * It will be used as the control input u of the hybrid traffic system control model at the next moment, and the controller will continue to perform a new round of optimization control.

[0193] For mixed traffic systems with CAV pairs, CBF ensures the safety of the system through a set of forward-invariant safe following distances between the leading and trailing vehicles. Specifically, in order to ensure that all vehicles maintain non-negative distances during the following process, a CBF control strategy based on vehicle spacing needs to be designed. The CBF constraint design of HDV is subject to the CAV control signal of the system, which has two control inputs, so the CAV pair system is divided into two subsystems. HDV before CAVi2 is subject to Control system control, HDV after CAVi2 is controlled by CAVi2 control system. The two subsystems are as follows:

[0194]

[0195] Among them, the status of each subsystem is:

[0196]

[0197] In the subsystem dominated by the first CAV vehicle i1, the vehicle index i∈[1,i1+n]. To ensure that the platoon always maintains a safe distance with the preceding vehicle, the CBF is set as follows:

[0198]

[0199] in, is the control obstacle function, τ is the vehicle reaction time, s i is the relative distance between the i-1th vehicle and the i-th vehicle, is the relative distance between the i-1th vehicle and the i-th autonomous driving vehicle, is the speed of the i-th autonomous driving vehicle, v i is the speed of the ith vehicle.

[0200] Then, it can be seen that for the first CAV vehicle control input The constraints are:

[0201]

[0202] in, K-type function α(x)=kx, k∈R, R is a set of real numbers.

[0203] Similarly, in the subsystem dominated by the second CAV vehicle i2, the vehicle index j∈[i1+1,i2+1], the CBF is set as:

[0204]

[0205] Then, for the second CAV vehicle control input The constraints are:

[0206]

[0207] in,

[0208] In order to further ensure the safety of the closed-loop CAV system, the control input u needs to satisfy the proposed safety constraints and make u consistent with the nominal control input If the deviation is the smallest, the safety constraints described by CBF can be embedded into the closed-loop system through the following quadratic programming (QP):

[0209] Subsystem i1:

[0210]

[0211] Subsystem i2:

[0212]

[0213] Among them, CBF can be regarded as a "filter" that can filter out control inputs that violate safety, and u * It is the closest safety control input after being screened by CBF.

[0214] In summary, in the entire control process, the hybrid traffic system control model is established on the basis of the HDV following model. The behavior characteristics of the hybrid traffic system are simulated by integrating the HDV following model with the CAV dynamics model. Then, the inverse FVD following model is established based on the HDV following model, and a bidirectional multi-vehicle following topology controller for CAV pairs is designed for the hybrid traffic system control model. This control method effectively suppresses the diffusion of traffic waves and avoids the risk of collision between the front and rear vehicles caused by random disturbances of HDV.

[0215] CAV two-way following is based on the forward following model. It uses the opposite following strategy to enable CAV to adjust its speed in the opposite direction according to the motion state of the vehicle behind it, achieving the purpose of two-way following that takes into account the factors of the front and rear vehicles. Then, a two-way following controller for CAV is designed based on the inverse FVD model.

[0216] In order to analyze the effectiveness of the CBF-based controller and the nominal controller when the HDV suddenly accelerates or decelerates, this method assumes a situation where the front vehicle suddenly brakes and sets the first HDV to brake urgently at 15 seconds. Figure 8(a)-Figure 8(b) As shown in Figure 8(a), the CAV pair controller can achieve the series stability of the hybrid system, but cannot avoid the collision problem when following the vehicle; Figure 8(b) shows that the use of the CBF-based CAV pair controller can effectively avoid vehicle collisions and improve the safety of the following process.

[0217] At the same time, this method assumes a situation where the rear vehicle suddenly accelerates, and the control situation of the CAV is as follows: Figure 9(a)-Figure 9(b) As shown in Figure 9(a), assuming that the rear vehicle accelerates quickly, under the nominal CAV pair controller, the CAV cannot perceive the safety threat of the rear vehicle, resulting in a collision; in Figure 9(b), after the safety screening of the CBF, the CAV pair can adjust the control input in time to guide the mixed traffic system to accelerate, leaving a certain safety buffer space for the rear vehicle to avoid a collision.

[0218] In summary, the control method provided in this embodiment solves the safety problem caused by sudden acceleration or deceleration of vehicles in the traffic system. First, aiming at the following characteristics of the vehicle, an improved FVD model is proposed to describe the following behavior of the vehicle more realistically. Secondly, considering the complexity of the traffic system where HDV and CAV coexist, the method of describing the dynamic information of the vehicle is effectively simplified by constructing a new hybrid traffic system model. In order to further optimize the anti-interference ability of the hybrid traffic system, this method proposes a controller for CAV to follow in both directions to guide the following behavior of the hybrid traffic system. Then, a safety constraint based on CBF is designed to constrain the control input in real time in the controller to ensure collisions caused by sudden acceleration or deceleration of following vehicles, thereby improving the safety of the hybrid traffic system.

[0219] Example 2

[0220] A hybrid traffic system with random traffic disturbance in this embodiment is used to implement a driving control method for a hybrid traffic system for suppressing random traffic disturbance described in Embodiment 1, including:

[0221] A manually driven vehicle following module, including a manually driven vehicle following model, which is constructed based on a full speed difference model;

[0222] A hybrid traffic system control model building module, which builds a hybrid traffic system control model including a pair of autonomous driving vehicles according to a hybrid traffic scenario consisting of manually driven vehicles and autonomous driving vehicles. The autonomous driving vehicle pair refers to two autonomous driving vehicles in the hybrid traffic scenario that are paired within a communication range for collaborative control;

[0223] An autonomous driving vehicle following model building module is used to build a two-way autonomous driving vehicle following model based on an autonomous driving vehicle pair according to a full speed difference model and a mixed traffic control model;

[0224] The vehicle control module is used to control the driving of the autonomous driving vehicle in the hybrid traffic system control model according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, based on the constraints of the autonomous driving vehicle pair imposed by the control obstacle function. The specific implementation methods of each module have been described in the driving control method for a hybrid traffic system for suppressing random traffic disturbances in Example 1, and will not be repeated here.

[0225] Example 3

[0226] like Fig.10The embodiment shown provides an electronic device for implementing the driving control method of a hybrid traffic system for suppressing random traffic disturbances in the above-mentioned embodiment 1. The electronic device 100 includes at least one processor 102, a memory 101, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104. The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the driving control method of a hybrid traffic system for suppressing random traffic disturbances in embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0227] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0228] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a driving control method for a mixed traffic system for suppressing random traffic disturbances, and the processor 102 can execute the plurality of instructions to implement:

[0229] Construct a car-following model for manually driven vehicles based on the full speed difference model;

[0230] According to a mixed traffic scenario consisting of manually driven vehicles and autonomous driving vehicles, a mixed traffic system control model including an autonomous driving vehicle pair is constructed, wherein the autonomous driving vehicle pair refers to two autonomous driving vehicles in the mixed traffic scenario that are paired within a communication range for collaborative control;

[0231] A two-way autonomous driving vehicle following model based on autonomous driving vehicle pairs is constructed according to the full speed difference model and the mixed traffic control model.

[0232] In the mixed traffic system control model, according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, the driving of the autonomous driving vehicle is controlled based on the constraints of the control obstacle function on the autonomous driving vehicle pair.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A driving control method for a mixed traffic system for suppressing random traffic disturbances, characterized in that: include: Construct a car-following model for manually driven vehicles based on the full speed difference model; According to a mixed traffic scenario consisting of manually driven vehicles and autonomous driving vehicles, a mixed traffic system control model including an autonomous driving vehicle pair is constructed, wherein the autonomous driving vehicle pair refers to two autonomous driving vehicles in the mixed traffic scenario that are paired within a communication range for collaborative control; A two-way autonomous driving vehicle following model based on autonomous driving vehicle pairs is constructed according to the full speed difference model and the mixed traffic control model. In the mixed traffic system control model, according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle, the driving of the autonomous driving vehicle is controlled based on the constraints of the control obstacle function on the autonomous driving vehicle pair.

2. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 1, characterized in that: The method of constructing a car-following model for a manually driven vehicle based on a full speed difference model includes: the full speed difference model is: In the formula, v n (t) is the speed of the nth vehicle at time t, s n is the distance between the front vehicle and the following vehicle. is the speed difference between the leading vehicle and the following vehicle, α is the driver sensitivity coefficient, is the expected speed related to the distance between the front and rear vehicles and the speed difference, and β is the speed difference influence coefficient.

3. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 2, characterized in that: The expected speed related to the distance between the front and rear vehicles and the speed difference is: In the formula, is the expected speed, s i is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, sgo is the maximum spacing, v max is the maximum speed of the vehicle, σ is the weight factor, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

4. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 1, characterized in that: The constructing of a hybrid traffic system control model including the autonomous driving vehicle pair includes: respectively determining an equilibrium state, a system state, a system input, and a state space in the hybrid traffic system control model, wherein the hybrid traffic system control model takes a desired state as a control target, is adjusted based on an input signal in a control input and a system state, and is controlled through the state space; The equilibrium state is: F(s * ,0,v * )=0 Among them, v * is the expected speed, s * is the desired constant vehicle distance; The system status is: Among them, x(t) is the state of the mixed traffic system, x(t)∈R 2N , R is the number field, They represent the spacing error and speed error of the i-th vehicle respectively; The control input is: Among them, u i (t) is the control input of the autonomous vehicle indexed as i, consisting of the acceleration signal of the autonomous vehicle pair; The state space is: Where A is the system state coefficient matrix, which is obtained according to the driver sensitivity coefficient, B is the control input coefficient matrix, and H is the vehicle speed coefficient matrix. is the speed error of the leading vehicle.

5. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 3, characterized in that: The method of constructing a bidirectional autonomous driving vehicle following model according to the full speed difference model and the hybrid traffic control model includes: obtaining a reverse expected speed and a forward expected speed of the autonomous driving vehicle according to the full speed difference model, and obtaining a bidirectional autonomous driving vehicle following model according to the forward expected speed and the reverse expected speed; The reverse expected speed is: in, is the expected speed function of the reverse vehicle, s is the relative distance between the i-1th vehicle and the i-th vehicle, v i is the speed of the ith vehicle, is the speed difference between the i-1th car and the i-th car, s st is the minimum spacing, sgo is the maximum spacing, v max is the maximum speed of the vehicle, and the vehicle is an automatic driving vehicle or a manually driven vehicle.

6. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 5, characterized in that: After the bidirectional autonomous driving vehicle following model is constructed according to the full speed difference model and the mixed traffic control model, the method further includes controlling the autonomous driving vehicle based on the bidirectional autonomous driving vehicle following model, including: in, are the distance control gains between the autonomous driving vehicle pair and the manually driven vehicle, are the speed control gains of the autonomous driving vehicle pair relative to the manually driven vehicle, i1 is the first vehicle in the autonomous driving vehicle pair, i2 is the first vehicle in the autonomous driving vehicle pair, j is the manually driven vehicle, is the relative distance between the manual driving vehicle and the automatic driving vehicle, is the speed difference between the manual vehicle and the automatic vehicle, is the expected speed function of the autonomous vehicle.

7. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 5, characterized in that: The vehicle expected speed function adopts different expected speed functions according to different following directions, namely: Among them, i is the number of the current autonomous driving vehicle, and j is the number of the following vehicle.

8. The driving control method for a hybrid traffic system for suppressing random traffic disturbances according to claim 1, characterized in that: The method of controlling the driving of the autonomous driving vehicle pair based on the constraint of the control obstacle function includes: splitting the autonomous driving vehicle pair into two subsystems, and the two subsystems respectively satisfy the constraint conditions based on the control obstacle function; the control obstacle function is: in, is the control obstacle function, τ is the vehicle reaction time, s i is the relative distance between the i-1th vehicle and the i-th vehicle, is the relative distance between the i-1th vehicle and the i-th autonomous driving vehicle, is the speed of the i-th autonomous driving vehicle, v i is the speed of the i-th vehicle; The constraint condition based on the control barrier function is: In the formula, is the function h i About vector f i The Lie derivative of satisfies is the control input of the autonomous vehicle, h i The gradient of are coefficient matrices respectively.

9. A hybrid traffic system for suppressing random traffic disturbances, characterized in that: include: A manually driven vehicle following module, including a manually driven vehicle following model, which is constructed based on a full speed difference model; A hybrid traffic system control model building module, which builds a hybrid traffic system control model including a pair of autonomous driving vehicles according to a hybrid traffic scenario consisting of manually driven vehicles and autonomous driving vehicles. The autonomous driving vehicle pair refers to two autonomous driving vehicles in the hybrid traffic scenario that are paired within a communication range for collaborative control; An autonomous driving vehicle following model building module is used to build a two-way autonomous driving vehicle following model based on an autonomous driving vehicle pair according to a full speed difference model and a mixed traffic control model; The vehicle control module is used to control the driving of the autonomous driving vehicle in the mixed traffic system control model based on the constraints of the autonomous driving vehicle pair imposed by the control obstacle function according to the following model of the autonomous driving vehicle and the following model of the manually driven vehicle.

10. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement a driving control method for a hybrid traffic system for suppressing random traffic disturbances as claimed in any one of claims 1 to 8.

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