IVCPS-based non-signalized intersection cooperative passing method under passenger and freight mixed traffic

Abstract

The invention relates to a non-signalized intersection cooperative passing method based on IVCPS under passenger and freight mixed traffic, and belongs to the field of intelligent network connection vehicles. The method comprises the step of judging the vehicle density of a non-signalized intersection in an information space. When the vehicle density is low, a real vehicle model is constructed based on a rectangular model, modeling is performed on a non-signalized intersection through a three-vehicle model, meanwhile, modeling is performed on a vehicle track, and a track model is solved to obtain a vehicle passing sequence and a speed plan; when the vehicle density is high, vehicles in a non-signalized intersection area are formed, and cooperative control is carried out through a queue-based double-layer control planning method. According to the double-layer control planning method, the upper layer plans the passing sequence of the motorcade through an improved MCTS algorithm, and the lower layer plans the vehicle track through an MPC algorithm. According to the invention, the control scheme can be adaptively adjusted according to the vehicle density, the traffic efficiency of the non-signalized intersection is optimized, and the fuel consumption is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent network-connected vehicles and relates to a collaborative traffic method for an unsignalized intersection based on IVCPS under mixed passenger and freight traffic. Background Art

[0002] Unsignalized intersections are an important part of the road traffic system. Vehicles from different directions converge here. Therefore, there are a lot of conflicts and interferences between vehicles at unsignalized intersections. Vehicles must make complex judgments and decisions when passing through unsignalized intersections, which leads to frequent accidents at unsignalized intersections and also reduces the traffic efficiency of unsignalized intersections. Frequent starting and stopping of vehicles leads to increased energy consumption. Unsignalized intersections in cities near ports, industrial parks, construction sites, etc. contain both buses and trucks. Since buses and trucks have great differences from cars in terms of size, acceleration and deceleration performance, speed performance, energy consumption, etc., the interference between different types of vehicles is serious and may lead to mobile bottlenecks, which may further lead to frequent accidents at unsignalized intersections, reduced traffic efficiency, and increased energy consumption due to frequent starting and stopping of vehicles. Therefore, in unsignalized intersections with mixed passenger and freight traffic, corresponding collaborative traffic control schemes should be formulated to improve safety, traffic efficiency and reduce vehicle fuel consumption.

[0003] In recent years, with the rapid development of computer, communication and automation technology, CPS technology and vehicle-road-cloud integration technology have been applied to the field of intelligent vehicles, thus forming a unique IVCPS intelligent vehicle information-physical system technology. Using IVCPS intelligent vehicle information-physical system technology, it is possible to obtain, process and transmit information between the vehicle, road and cloud, so as to map the physical space and information space of the unsignalized intersection one by one, obtain vehicle information such as vehicle physical size, speed and acceleration, traffic status information such as traffic flow and congestion status, timestamp of vehicle arrival at a certain moment, vehicle location and other spatiotemporal information. Under the framework of IVCPS intelligent vehicle information-physical system, a collaborative control method is designed in the scenario of unsignalized intersection with mixed passenger and freight traffic, which is expected to improve the safety, low traffic efficiency and increased fuel consumption of vehicles in unsignalized intersections.

[0004] The existing cooperative traffic method for unsignalized intersections has the following shortcomings:

[0005] 1. In the existing research on cooperative traffic at unsignalized intersections, most of the research focuses on a single spatiotemporal scale, a single vehicle type, and a single traffic density. The IVCPS technology is not fully utilized to establish a framework system that can span multiple scales, be compatible with different vehicle types, and adaptively adjust the control method according to vehicle density, thus failing to fully optimize the performance of unsignalized intersections.

[0006] 2. In the existing research on the design of cooperative traffic methods for unsignalized intersections, the focus is on designing a method that is compatible with different traffic flows. However, when the vehicle density is low, the research method may waste a lot of time and space resources and does not take into account the control decision-making problems caused by the differences between different vehicle types;

[0007] 3. When the vehicle density at an unsignalized intersection is high, existing research institutes have proposed traditional collaborative traffic methods based on, for example, centralized control. It is difficult to combine information space and physical space at a single scale to form a hierarchical and complete control method. At the same time, in this scenario, there are problems such as high computational complexity and difficulty in solving, failure to take into account indicators such as traffic efficiency and fuel consumption, and difficulty in applying to mixed passenger and freight traffic scenarios. Summary of the invention

[0008] In view of this, the purpose of the present invention is to provide a collaborative traffic method for unsignalized intersections based on IVCPS under mixed passenger and freight traffic, so that the control system can adaptively adjust the control scheme according to the vehicle density under dynamically changing traffic conditions and vehicle types, thereby optimizing the traffic efficiency of unsignalized intersections and reducing fuel consumption.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A cooperative passage method for unsignalized intersections based on IVCPS under mixed passenger and freight traffic. The method transmits vehicle-end and road section information to the information space through on-board sensors and roadside units, judges the vehicle density of unsignalized intersections in the information space, and adopts different control strategies according to different vehicle densities to achieve cooperative passage at unsignalized intersections.

[0011] Among them, when the vehicle density is low, the unsignalized intersection is divided into sections, and a real vehicle model is constructed based on the rectangular model. The unsignalized intersection is modeled through a three-vehicle model, and the vehicle trajectory is modeled at the same time. The trajectory model is solved to obtain the vehicle passage order and speed planning, so that vehicles can pass through the unsignalized intersection in a coordinated manner;

[0012] When the vehicle density is high, the unsignalized intersection is divided into sections, and the vehicles in the unsignalized intersection area are platooned. Cooperative control is performed through a queue-based two-layer control planning method so that the vehicles can pass through the unsignalized intersection in a coordinated manner. In the two-layer control planning method, the upper layer plans the passage order of the fleet through the improved MCTS algorithm, and the lower layer plans the vehicle trajectory through the MPC algorithm.

[0013] Furthermore, when the vehicle density is low, the unsignalized intersection is divided into an observation section, an uncontrolled section, a target optimization section, and a conflict area. In the uncontrolled section, the vehicle is not controlled and the vehicle maintains the speed to the target optimization section; in the target optimization section and the conflict area, the vehicle is controlled until the vehicle leaves the conflict area.

[0014] Based on the segment division, the unsignalized intersection is modeled by a three-car model, including:

[0015] With the center of the unsignalized intersection as the origin O, an XOY coordinate system is established, and the direction in which there are two conflicting areas in the direction of movement of a single vehicle is defined as the Y axis; vehicles from three directions enter the target optimization section at the same time, and the lane width of the unsignalized intersection is defined as W, and the vehicle width is w i,j , the vehicle length is l i,j , the vehicle speed and acceleration are v i,j (t) and a i,j (t), where i represents the lane, j represents the vehicle in the lane, and t represents the time.

[0016] The conflict area between the first vehicle and the second vehicle is defined as the first conflict area, and the conflict area between the second vehicle and the third vehicle is defined as the second conflict area; i1, i2, i3 represent lanes where the first vehicle, the second vehicle, and the third vehicle are located respectively, and j1, j2, j3 represent vehicle numbers of the first vehicle, the second vehicle, and the third vehicle in the lanes respectively.

[0017] Define the initial time and initial position of the trajectory planned by the cloud for the first vehicle as t0 and Define the end time and end position of the first vehicle trajectory planning as and represents the time when the front of the first vehicle arrives at the first conflict area; the initial time and initial position of the trajectory planning of the second vehicle in the cloud are defined as t0 and Define the end time and end position of the second vehicle trajectory planning as and represents the time when the front of the second vehicle arrives at the first conflict area; the initial time and initial position of the third vehicle trajectory planning in the cloud are defined as t0 and The end time and end position of the third vehicle trajectory planning are defined as and Indicates the time when the front end of the third vehicle arrives at the second conflict area.

[0018] Furthermore, a quintic polynomial is used as the vehicle trajectory model, which is expressed as:

[0019] s i,j (t) = r i,j +r i,j,1 t i,j +r i,j,2 t i,j 2 +r i,j,3 t i,j 3 +r i,j,4 t i,j 4 +r i,j,5 t i,j 5

[0020] In the formula, r i,j 、r i,j,1 、r i,j,2 、r i,j,3 、r i,j,4 、r i,j,5 Both represent coefficients, t i,j represents the travel time of vehicle j in lane i; the first-order and second-order differentials of the above formula are calculated to obtain the functions of velocity and acceleration with respect to time:

[0021] v i,j (t) = r i,j,1 +2r i,j,2 t i,j +3r i,j,3 t i,j 2 +4r i,j,4 t i,j 3 +5r i,j,5 t i,j 4

[0022] a i,j (t) = 2r i,j,2 +6r i,j,3 t i,j +12r i,j,4 t i,j 2 +20r i,j,5 t i,j 3

[0023] The position, velocity and acceleration can be converted into time-related equations through a quintic polynomial, and the trajectory of each vehicle is obtained by solving the optimal end time of each vehicle, where the end time is the moment when each vehicle arrives at the conflict area.

[0024] The trajectory planning problem is transformed into a multi-objective optimization problem. The objective functions designed include the shortest travel time objective function, fuel consumption objective function and comfort objective function. At the same time, constraints are constructed. Among them, the travel time objective function takes minimizing the end time as the optimization goal. Multiple objective functions are transformed into a single objective function through weighted summation, and then the simulated annealing algorithm is used to solve the single objective function to obtain the optimal end time.

[0025] Furthermore, when the vehicle density is high, the unsignalized intersection is divided into an observation section, a formation section, a decision section and a conflict area; the vehicles are arranged into queues in the formation section; and the two-layer control planning method is used to perform collaborative control in the decision section.

[0026] In the formation section, arranging vehicles into a queue includes: setting the vehicle spacing between vehicle j and vehicle j+1 to be D S , the maximum vehicle distance between vehicle j and vehicle j+1 is D H , when the following relationship is satisfied between vehicle j and vehicle j+1, vehicle j and vehicle j+1 are grouped into the same fleet:

[0027]

[0028] In the formula, v i,j (t0) represents the speed of the vehicle at time t0, v i,j,min Indicates the minimum speed of the vehicle, L B Indicates the length of the observation segment.

[0029] Furthermore, in the two-layer control planning method, the upper layer plans the passage order of the fleet by improving the MCTS algorithm, including: in the MCTS algorithm, each node of the search tree represents a state, and each state contains the position and speed information of all vehicles at the current moment; the MCTS algorithm repeatedly executes the four stages of selection, expansion, simulation and backtracking, and stops after reaching the predetermined calculation time or number of iterations.

[0030] In the selection phase, starting from the root node, an optimal child node is selected for expansion through the UCT algorithm;

[0031] In the expansion phase, starting from the selected optimal child node, a new vehicle passage sequence is simulated, a new child node is generated and added to the search tree;

[0032] In the simulation phase, for the expanded child nodes, starting from the root node, the process of vehicles passing through the unsignalized intersection is simulated by randomly selecting the passing order of vehicles, and the simulation process is performed downward along a path of the search tree passing through the expanded child nodes until all vehicles pass through the unsignalized intersection;

[0033] In the backtracking phase, the simulation result is traced back to the root node through the path selected in the simulation phase, and the values ​​and access times of the passed nodes are updated at the same time.

[0034] The UCT algorithm is used to evaluate the value of each child node in the selection phase, which is expressed as:

[0035]

[0036] In the formula, β u represents the total reward value of node u, p u represents the number of times node u is visited, P represents the total number of times the parent node is visited, and C represents the trade-off coefficient used to adjust the exploration part and the utilization part, where the utilization part is Explore the The average delay of vehicles is selected as β u Calculation of value:

[0037]

[0038] In the formula, t i,j,ac represents the actual travel time of a single vehicle through an unsignalized intersection, t i,j,min Indicates the minimum travel time of a single vehicle.

[0039] Furthermore, the UCT algorithm is improved by using a dynamic window method to adjust the C value and defining the sliding window size W m , adjustment factor k m , number of iterations n, initial C value C init and a positive number η;

[0040] Calculate the average delay time AD within the sliding window W :

[0041]

[0042] Calculate the overall average delay time AD T :

[0043]

[0044] Then the C value is adjusted by the following formula:

[0045]

[0046] C=max(C,η)

[0047] Furthermore, the two-layer control planning method, the lower layer plans the vehicle trajectory through the MPC algorithm including:

[0048] 1) Define the vehicle's state variables, including position, velocity, and acceleration;

[0049] 2) Define the dynamic model of the vehicle in discrete form:

[0050]

[0051] v i,j (t+1)=v i,j (t)+a i,j (t)ΔT

[0052] In the formula, s i,j (t) is the position of vehicle j in lane i at time t, v i,j (t) represents the speed of vehicle j in lane i at time t, ΔT represents the simulation step length, and a i,j (t) represents the acceleration of vehicle j in lane i at time t;

[0053] 3) Define optimization objectives and constraints; the optimization objectives include vehicle fuel consumption and comfort, and the constraints include vehicle speed, acceleration constraints, and vehicle spacing constraints;

[0054] 4) According to the current state and the predicted control input sequence, the future state of the vehicle in multiple time steps is calculated to generate a series of control input sequences, namely candidate trajectories, and the state sequence under the generated control input sequence is predicted in the vehicle dynamics model; each candidate trajectory is evaluated, the corresponding objective function value is calculated, and it is checked whether all constraints are met; the trajectory with the minimum objective function value and satisfying all constraints is selected from the candidate trajectories as the optimal trajectory at the current moment; wherein the control input sequence includes the state sequence and the predicted sequence;

[0055] 5) Apply the state sequence of the optimal trajectory to the vehicle;

[0056] 6) Repeat the following steps in each control cycle:

[0057] Update Status: Get the latest status of the vehicle.

[0058] Reforecast: Based on the updated state, reforecast the future state.

[0059] Re-optimization: Resolve the optimization problem to obtain a new optimal control input sequence.

[0060] The beneficial effects of the present invention are:

[0061] 1) The present invention relies on IVCPS technology to calculate the current intersection vehicle density in real time, and identify different vehicle types (vehicle sizes and weights are inconsistent). It can span multiple scales, be compatible with different vehicle types, and adaptively adjust the control method and perception control range according to vehicle density;

[0062] 2) Considering that the actual collision area of ​​different types of vehicles when passing through an intersection changes with the size of the vehicle, the present invention constructs a real vehicle model based on a rectangular model when the vehicle density is low, which reduces the perception control range of the control system and improves the control accuracy. The controllable area of ​​the vehicle is expanded from the stop line of the current lane to the conflict area, and the fuel emission model is improved according to the difference between different vehicle types, which reduces fuel consumption while ensuring better traffic efficiency;

[0063] 3) When the vehicle density is high, the present invention proposes a queue-based double-layer planning control method for unsignalized intersections, and uses the proposed improved MCTS algorithm in the upper-layer planning, thereby improving the algorithm solution efficiency and vehicle traffic efficiency; in the lower-layer planning, a distributed control method is used to optimize the speed and acceleration of the fleet with fuel consumption as an indicator. This achieves a balance between high traffic efficiency and low fuel consumption on the basis of low computational complexity.

[0064] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 Schematic diagram of the division of different time and space scales under IVCPS;

[0067] Figure 2 Schematic diagram of cooperative control framework for unsignalized intersections under IVCPS;

[0068] Figure 3 Schematic diagram of the cooperative traffic framework under low vehicle density;

[0069] Figure 4 It is a schematic diagram of the three-car model and the vehicle model;

[0070] Figure 5 This is a schematic diagram of the cooperative traffic framework under high vehicle density;

[0071] Figure 6 It is a structural diagram of the upper and lower level control planning method;

[0072] Figure 7 Schematic diagram of the search steps of the MCTS algorithm. DETAILED DESCRIPTION

[0073] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0074] Based on the multi-temporal and spatial scales of IVCPS technology, the present invention proposes a multi-temporal and spatial scale cooperative traffic control system for signal-free intersections, divides signal-free intersections into multiple areas, and transforms system-level signal-free vehicle cooperative tasks into multiple subsystem-level control tasks with hierarchical progression. Relying on IVCPS technology, the current intersection vehicle density can be calculated in real time, and different vehicle types (vehicle size and weight are inconsistent) can be identified at the same time. Finally, a framework system is established that can span multiple scales, be compatible with different vehicle types, adaptively adjust control methods according to vehicle density, and perceive control range.

[0075] In addition, considering that the actual collision area of ​​different types of vehicles passing through the intersection changes with the change of their vehicle size. Therefore, when the vehicle density is low, a low-vehicle-density cooperative traffic framework is designed, and a real vehicle model is constructed based on a rectangular model. In order to reduce the perception control range of the control system and improve the control accuracy, a cooperative traffic method based on a three-vehicle model is proposed. Considering the actual size of different vehicles, the vehicle's traffic order and speed planning are converted into a vehicle trajectory described by a fifth-order polynomial, so that the vehicle's controllable area can be extended from the stop line of the current lane in previous studies to the conflict area, and the fuel emission model is improved according to the difference between different vehicle types to ensure that fuel consumption is reduced under the premise of better traffic efficiency. When the vehicle density is high, a high-vehicle-density cooperative communication framework is designed, and the vehicle teaming rules are designed, and the vehicle queue after teaming is regarded as an extra-long vehicle. A queue-based double-layer planning control method for unsignalized intersections is proposed. In the upper-layer planning, an improved MCTS algorithm is used to improve the algorithm solution efficiency and the order of the fleet is obtained with the traffic efficiency as the indicator. In the lower-layer planning, a distributed control method is used to optimize the fleet speed and acceleration with fuel consumption as the indicator. The calculation complexity is reduced while taking into account the traffic efficiency and fuel consumption indicators.

[0076] According to the above content, an embodiment of the present invention provides a method for cooperative passage at an unsignalized intersection based on IVCPS under mixed passenger and freight traffic. The method is specifically described as follows:

[0077] 1. Division of different spatiotemporal scales under IVCPS

[0078] IVCPS information-physical systems have six major characteristics, including complexity, heterogeneity, openness, closed-loop, emergence, and evolution. It is difficult to analyze and design a complex large system at the same time and space scale. For IVCPS, the scales can be divided into node level, unit level, subsystem level, system level, and regional level from small to large in terms of time and space. Figure 1 At different time and space scales, the information space and physical space include as shown in Table 1:

[0079] Table 1 Multi-scale division of intelligent vehicle cyber-physical system

[0080]

[0081]

[0082] The signal-free intersection traffic control architecture based on IVCPS at multiple scales can well combine information space and physical space, and use vehicle-road-cloud integration technology to achieve precise control. The signal-free intersection targeted by this embodiment is a "422" scenario, that is, 4 directions, 2 intersecting roads, and 2 lanes. Here, only one lane of a road in one direction is taken as an example. The remaining lanes are the same. When the vehicle leaves the conflict area, it will no longer be controlled. This embodiment will be based on the signal-free intersection traffic control framework at multiple scales, and the signal-free intersection area will be further divided into observation sections, control sections, and collision areas to form a corresponding collaborative control framework, such as Figure 2 shown.

[0083] Under this framework, the information space can receive information from the vehicle and road sections, and formulate corresponding control strategies based on the information provided. In the physical space, smart cars can be connected with other vehicles and road equipment in real time. After receiving the information transmitted by the information space, the vehicles in the physical space will continuously adjust their driving status to ensure precise control of unsignaled intersections.

[0084] Under this framework, the complex task of coordinated traffic at unsignalized intersections is broken down and refined into observation sections, control sections, and collision areas, achieving local optimization and global optimization. The main functions of each section are as follows:

[0085] Observation section: After the vehicle arrives at the observation section, the system will use the vehicle-road-cloud integration technology to judge the vehicle density at the system-level unsignalized intersection at the current moment, and at the same time accelerate the vehicle to the maximum speed to pass through the observation section.

[0086] Control section: When the vehicle arrives at the control section, the system will execute the corresponding control strategy based on the vehicle's judgment of the vehicle density in the observation section. Under high vehicle density, the control area is further subdivided into a formation area and a decision area. After the vehicles are teamed in the formation area, they can pass through the unsignalized intersection in a queue, reducing the control calculation complexity of the system and reducing the fuel consumption of the vehicle. After the vehicle arrives at the decision area, an upper and lower control model is constructed. The upper layer plans the vehicle's passage order, and the lower layer optimizes the vehicle's speed to achieve fuel consumption while ensuring the traffic efficiency. Under low vehicle density, the intersection is modeled based on the three-vehicle model, and the vehicle trajectory is modeled. Finally, the passage order and speed planning are solved by the solution algorithm.

[0087] Conflict area: Under high vehicle density, after the control section adjusts the vehicle status, vehicles in all directions will pass through the conflict area without conflict; under low vehicle density, vehicles will continue to be controlled in this area until they leave the conflict area without conflict.

[0088] In order to focus on the coordinated control of unsignalized intersections with mixed passenger and freight traffic, this embodiment makes the following assumptions for the scenario:

[0089] (1) All controlled vehicles are intelligent networked vehicles, which are divided into intelligent networked manually driven vehicles and intelligent networked autonomous driving vehicles. Non-motor vehicles and pedestrians are not considered for the time being;

[0090] (2) Connected vehicles in different scenarios strictly follow the corresponding control strategies, and there is no subjective interference of drivers in vehicle operation in intelligent connected vehicles;

[0091] (3) Both the intelligent vehicle-mounted terminal and the intelligent roadside terminal operate in an ideal state, with no packet loss, communication delay, etc.

[0092] (4) The selected environments are all within the coverage of the Internet of Things, and the vehicle does not overtake or change lanes after entering the scene.

[0093] 2. Cooperative Traffic Methods under Different Vehicle Densities

[0094] 1. Cooperative traffic method under low vehicle density

[0095] When the vehicle density at an unsignalized intersection is low, in order to reduce the system's perception control range, Figure 2 The collaborative control framework shown in the figure divides the unsignalized intersection into observation section, uncontrolled section, target optimization section, and conflict area. Figure 3As shown in the figure. The uncontrolled section means that in this section, the vehicle will not be controlled externally and will maintain the speed until it reaches the target optimization section. When the vehicle is in the target optimization section or the conflict area, it will be controlled by the cloud until it leaves the conflict area.

[0096] A cooperative traffic method based on the three-vehicle model is proposed, and the target optimization section and conflict area are modeled. Figure 4 shown. Figure 4 In the example, vehicles from three directions enter the target optimization section at the same time. The lane width is W and the width of the vehicle is w. i,j , the length is l i,j , the acceleration is a i,j (t), with a velocity of v i,j (t), where i represents the lane number, j represents the jth vehicle in the current lane, and t represents time. An XOY coordinate system is established at the center of the unsignalized intersection, and the direction in which there are two conflicting areas in the direction of movement of a single vehicle is defined as the Y axis. The lane numbers of vehicle 1, vehicle 2, and vehicle 3 are represented by i1, i2, and i3, respectively, and the vehicle numbers of vehicle 1, vehicle 2, and vehicle 3 in the current lane are represented by j1, j2, and j3, respectively.

[0097] Assume that the initial time is t0, the conflict area between vehicle 1 and vehicle 2 is conflict area 1, and its size is The length and time of vehicle 1's front end arriving at conflict area 1 are The length and time of the rear end of the vehicle leaving conflict area 1 are The position of the center of mass of vehicle 1 in the direction of motion at time t0 is The initial coordinates in the XOY coordinate system are The length and time of vehicle 2’s front end arriving at conflict area 1 are The length and time of the rear end of the vehicle leaving conflict area 1 are The position of the center of mass of vehicle 2 in the direction of motion at time t0 is

[0098] The conflict area between vehicle 2 and vehicle 3 is conflict area 2, and its size is The length and time of vehicle 2's front end arriving at conflict area 2 are The length and time of the rear end of the vehicle leaving conflict area 2 are The length and time of vehicle 3’s front end arriving at conflict area 1 are The length and time of the rear end of the vehicle leaving conflict area 1 are The position of the center of mass of vehicle 3 in the direction of motion at time t0 is The initial coordinates in the XOY coordinate system are

[0099] For the three-vehicle model, define the initial time and initial position of the cloud for vehicle 1 trajectory planning And the end time and end location of the vehicle Initial time and initial position of vehicle 2 trajectory planning And the end time and end location of the vehicle Initial time and initial position of vehicle 3 trajectory planning And the end time and end location of the vehicle After the vehicle completes the trajectory planning designed by the cloud, it will maintain the speed at the end of the trajectory planning until all vehicles have left the conflict area. The solution of some parameters defined above is as follows:

[0100]

[0101] A quintic polynomial is used as the vehicle trajectory model:

[0102] s i,j (t) = r i,j +r i,j,1 t i,j +r i,j,2 t i,j 2 +r i,j,3 t i,j 3 +r i,j,4 t i,j 4 +r i,j,5 t i,j 5

[0103] By taking the first and second order differentials of the above equation, we can get the functions of velocity and acceleration with respect to time:

[0104] v i,j (t) = r i,j,1 +2r i,j,2 t i,j +3r i,j,3 t i,j 2 +4r i,j,4 t i,j 3 +5r i,j,5 t i,j 4

[0105] a i,j (t) = 2r i,j,2 +6r i,j,3 t i,j +12r i,j,4 ti,j 2 +20r i,j,5 t i,j 3

[0106] In the trajectory planning of the vehicle, if the initial time and initial position of the vehicle (t0, s i,j (t0)) and the end time and end position of the vehicle (t i,j,f ,s i,j (t f )), then all unknown quantities can be solved. With the support of IVCPS technology, the system can obtain s i,j (t0). Therefore, the position, velocity, and acceleration equations can all be converted into equations related to time, and then the optimal end time of each vehicle can be solved. Thus, the trajectory of each vehicle is obtained, and then the trajectory planning problem is transformed into a multi-objective problem, and the coordinated traffic problem of the three vehicles is transformed into a multi-constraint problem. The process is as follows:

[0107] 1) Construction of multi-objective optimization function

[0108] Design of the objective function of the shortest travel time: In the cooperative traffic method designed based on the three-car model, the shortest travel time of the cooperative traffic method should be the shortest time for the rear ends of the three cars to pass through the conflict area. Its objective function is expressed as:

[0109]

[0110] Fuel consumption objective function design: VT-Mirco is selected as the fuel consumption model, and the concept of unit efficiency (MOE) is introduced into the model. The calculation formula is as follows:

[0111]

[0112] Among them, MOE e is the unit change in vehicle fuel consumption and emissions, MOE for regression model e Regression coefficient for velocity in g power and acceleration in h power, v i,j is the instantaneous speed of the vehicle, a i,j is the instantaneous acceleration of the vehicle. Considering the difference in mass between different types of vehicles, this embodiment adjusts the VT-Mirco model to obtain the constructed energy consumption formula:

[0113]

[0114] Among them, M X is the weight of different vehicle types, specifically, M A For passenger cars, MB Truck, M C For a car. Therefore, the fuel consumption objective function F2 is:

[0115]

[0116] Comfort objective function design: In order to ensure the stability and comfort of the vehicle during driving and reduce energy consumption, it is necessary to reduce the frequent acceleration and deceleration of the vehicle, that is, to reduce the changes in vehicle speed and acceleration.

[0117]

[0118] Where ΔT is the simulation step length. Based on the above objective function, the multi-objective function can be transformed into a single objective function optimization:

[0119]

[0120] in, is the weight coefficient, which can be adjusted according to actual needs.

[0121] 2) Constructing constraints

[0122] Collision constraints:

[0123]

[0124] Speed ​​and acceleration constraints:

[0125] v n,min ≤v i,j (t)≤v n,max

[0126] a n,min ≤a i,j (t)≤a n,max

[0127] Among them, n = 1, 2, 3 represent three different types of vehicles: sedan, truck, and bus, respectively. n,max and v n,min are the maximum speed and minimum speed of the road respectively, a n,max and a n,min They are the maximum acceleration and minimum acceleration restricted by the road.

[0128] 3) Model solution

[0129] The simulated annealing algorithm is used to solve the single objective optimization function proposed above. Since the above objective optimization function contains constraints, the constraint function is converted into a penalty factor, and the objective function is converted into:

[0130]

[0131] Among them, γ is the penalty factor, which can be an integer between 0 and infinity, G Z (x) represents the constraints mentioned above.

[0132] 2. Cooperative traffic method under high vehicle density

[0133] When the vehicle density at an unsignalized intersection is high, in order to improve the traffic efficiency of the unsignalized intersection and reduce the fuel consumption of vehicles, the unsignalized intersection is divided into observation section, formation section, decision section, and conflict area, such as Figure 5 shown.

[0134] In this framework, vehicles form a platoon in the platoon section according to the platoon rules. Figure 5 The red vehicle in the middle represents the leading vehicle in the convoy, and the black vehicle represents the following vehicle. Figure 6 The upper and lower layer controllers shown in the figure use the improved MCTS algorithm to plan the passage sequence of the fleet at the upper layer, and the MPC algorithm to plan the vehicle trajectory at the lower layer.

[0135] In the platoon section, when the vehicle distance D between vehicle j and vehicle j+1 is S Less than the maximum vehicle distance D H When the following equation is satisfied, the two vehicles are transformed into the same team:

[0136]

[0137] Among them, L B is the length of the observation segment. s , D H The calculation formulas are expressed as:

[0138]

[0139] In the formation section, the two vehicles will not participate in the formation of other platoons before forming a stable platoon. A stable platoon means that the speed and acceleration of the vehicles in the platoon are consistent, and the distance between vehicles is the expected distance between vehicles. After entering the decision section, the platoon will be regarded as a whole. The speed of the platoon is the minimum speed of all vehicles in the platoon, the maximum acceleration and deceleration of the platoon is the minimum acceleration and deceleration of all vehicles in the platoon, and the maximum and minimum speeds of the platoon are the maximum and minimum speeds of all vehicles in the platoon.

[0140] 1) MCTS algorithm plans the passage sequence

[0141] The improved MCTS algorithm is used in the upper controller to sort the passage order of vehicles. The basic idea is to evaluate the potential value of each decision by simulating a large number of possible decision paths, and guide the next search based on the simulation results. The algorithm mainly consists of four stages: selection, expansion, simulation, and backpropagation.

[0142] The selection phase mainly performs the following steps: starting from the root node, an unexplored child node is selected according to a certain strategy (such as the UCB algorithm) until a leaf node is reached or a limiting condition is met.

[0143] The expansion phase mainly performs the following: if the currently selected node has unexplored child nodes, one or more child nodes are expanded according to a certain strategy (such as exploring unknown areas or prioritizing nodes with high potential value).

[0144] The simulation phase mainly performs the following steps: for the expanded child nodes, a certain number of simulations are performed through random simulation or heuristic simulation to obtain a result.

[0145] The backtracking phase mainly performs the following tasks: tracing the simulation results back to the root node through the node path and updating the statistical information of each node for use in the next round of selection.

[0146] like Figure 7 As shown, it shows an iteration of the MCTS-based strategy, which includes the above four steps: selection, expansion, simulation, and backtracking.

[0147] In this embodiment, the UCT algorithm is used as the value calculation for evaluating each child node in the selection step:

[0148]

[0149] Among them, β u is the total reward value of node u, p u is the number of times node u is visited, P is the total number of times the parent node is visited, and C is the trade-off coefficient used to adjust the exploration part and the utilization part, where the utilization part is Explore the The average delay of vehicles is selected as β u Calculation of value:

[0150]

[0151] Among them, t i,j,ac represents the actual travel time of a single vehicle through an unsignalized intersection, t i,j,minIndicates the minimum travel time of a single vehicle. In previous studies, the C value is often set to a constant. In this embodiment, the dynamic window method is used to adjust the C value, and the size of the sliding window is set to W. M =30, the adjustment factor is k m =0.05, the minimum positive number is η=0.5, and the initial value of C is C init =1.4. The average delay time in the sliding window is:

[0152]

[0153] Calculate the overall average delay time:

[0154]

[0155] The calculation formula of C value is as follows:

[0156]

[0157] C=max(C,η)

[0158] 2) MPC controller plans vehicle trajectory

[0159] The vehicle trajectory planning process based on MPC is as follows:

[0160] 1. Define the vehicle's state variables, position, speed, and acceleration;

[0161] 2. Define the dynamic model of the vehicle in discrete form:

[0162]

[0163] v i,j (t+1)=v i,j (t)+a i,j (t)ΔT

[0164] 3. Define optimization objectives and constraints;

[0165] 4. Based on the current state and the predicted control input sequence, calculate the future state of the vehicle in multiple time steps, generate a series of possible control input sequences (candidate trajectories), and predict the state sequence under these control inputs based on the vehicle dynamics model. Evaluate each candidate trajectory, calculate its corresponding objective function value, and check whether it meets all constraints. Select the trajectory with the minimum objective function value and that meets all constraints from the candidate trajectories as the optimal trajectory at the current moment;

[0166] 5. Apply the first control input of the optimal trajectory to the vehicle;

[0167] 6. Repeat the steps in each control cycle:

[0168] Update Status: Get the latest status of the vehicle.

[0169] Reforecast: Based on the updated state, reforecast the future state.

[0170] Re-optimization: Resolve the optimization problem to obtain a new optimal control input sequence.

[0171] Among them, the optimization objectives and constraints of the MPC controller are as follows:

[0172] Set the prediction horizon length of the MPC controller to N P , control the time domain length to be N C , then the objective function of the controller is designed as follows:

[0173]

[0174] The objective function is divided into two parts:

[0175] Vehicle fuel consumption objective function:

[0176] Comfort objective function design:

[0177] The constraints are: vehicle speed and acceleration constraints:

[0178] v n,min ≤v i,j (t)≤v n,max

[0179] a n,min ≤a i,j (t)≤a n,max

[0180] Vehicle spacing distance constraint:

[0181]

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A cooperative traffic method for unsignalized intersections based on IVCPS under mixed passenger and freight traffic, characterized by: The vehicle-side and road section information is transmitted to the information space through the vehicle-mounted sensors and roadside units, and the vehicle density at the unsignalized intersection is judged in the information space. Different control strategies are adopted according to different vehicle densities to achieve coordinated passage at the unsignalized intersection. Among them, when the vehicle density is low, the unsignalized intersection is divided into sections, and a real vehicle model is constructed based on the rectangular model. The unsignalized intersection is modeled through a three-vehicle model, and the vehicle trajectory is modeled at the same time. The trajectory model is solved to obtain the vehicle passage order and speed planning, so that vehicles can pass through the unsignalized intersection in a coordinated manner; When the vehicle density is high, the unsignalized intersection is divided into sections, and the vehicles in the unsignalized intersection area are platooned. Cooperative control is performed through a queue-based two-layer control planning method so that the vehicles can pass through the unsignalized intersection in a coordinated manner. In the two-layer control planning method, the upper layer plans the passage order of the fleet through the improved MCTS algorithm, and the lower layer plans the vehicle trajectory through the MPC algorithm.

2. The method for cooperative passage at an unsignalized intersection according to claim 1, characterized in that: When the vehicle density is low, the unsignalized intersection is divided into an observation section, an uncontrolled section, a target optimization section and a conflict area; in the uncontrolled section, the vehicle is not controlled and the vehicle maintains the speed to the target optimization section; in the target optimization section and the conflict area, the vehicle is controlled until the vehicle leaves the conflict area.

3. The method for cooperative passage at an unsignalized intersection according to claim 2, characterized in that: Based on the segment division, the three-vehicle model is used to model the unsignalized intersection, including: taking the center of the unsignalized intersection as the origin O, establishing an XOY coordinate system, and stipulating the direction of two conflicting areas in the direction of movement of a single vehicle as the Y axis; vehicles from three directions enter the target optimization segment at the same time, defining the lane width of the unsignalized intersection as W, and the vehicle width as w i,j , the vehicle length is l i,j , the vehicle speed and acceleration are v i,j (t) and a i,j (t), where i represents the lane, j represents the vehicle in the lane, and t represents the time; Define the conflict area between the first vehicle and the second vehicle as the first conflict area, and the conflict area between the second vehicle and the third vehicle as the second conflict area; i1, i2, i3 represent the lanes where the first vehicle, the second vehicle, and the third vehicle are located, respectively; j1, j2, j3 represent the vehicle numbers of the first vehicle, the second vehicle, and the third vehicle in the lanes, respectively; Define the initial time and initial position of the trajectory planned by the cloud for the first vehicle as t0 and Define the end time and end position of the first vehicle trajectory planning as and represents the time when the front of the first vehicle arrives at the first conflict area; the initial time and initial position of the trajectory planning of the second vehicle in the cloud are defined as t0 and Define the end time and end position of the second vehicle trajectory planning as and represents the time when the front of the second vehicle arrives at the first conflict area; the initial time and initial position of the third vehicle trajectory planning in the cloud are defined as t0 and The end time and end position of the third vehicle trajectory planning are defined as and Indicates the time when the front end of the third vehicle arrives at the second conflict area.

4. The method for cooperative passage at an unsignalized intersection according to claim 1, characterized in that: A quintic polynomial is used as the vehicle trajectory model, which is expressed as: s i,j (t)=r i,j +r i,j,1 t i,j +r i,j,2 t i,j 2 +r i,j,3 t i,j 3 +r i,j,4 t i,j 4 +r i,j,5 t i,j 5 In the formula, r i,j 、r i,j,1 、r i,j,2 、r i,j,3 、r i,j,4 、r i,j,5 Both represent coefficients, t i,j represents the travel time of vehicle j in lane i; the first-order and second-order differentials of the above formula are calculated to obtain the functions of velocity and acceleration with respect to time: v i,j (t)=r i,j,1 +2r i,j,2 t i,j +3r i,j,3 t i,j 2 +4r i,j,4 t i,j 3 +5r i,j,5 t i,j 4 a i,j (t)=2r i,j,2 +6r i,j,3 t i,j +12r i,j,4 t i,j 2 +20r i,j,5 t i,j 3 The trajectory of each vehicle is obtained by solving the optimal end time of each vehicle, where the end time is the time when each vehicle arrives at the conflict area; The trajectory planning problem is transformed into a multi-objective optimization problem. The objective functions designed include the shortest travel time objective function, fuel consumption objective function and comfort objective function. At the same time, constraints are constructed. Among them, the travel time objective function takes minimizing the end time as the optimization goal. Multiple objective functions are transformed into a single objective function through weighted summation, and then the simulated annealing algorithm is used to solve the single objective function to obtain the optimal end time.

5. The method for cooperative passage at an unsignalized intersection according to claim 1, characterized in that: When the vehicle density is high, the unsignalized intersection is divided into an observation section, a formation section, a decision section and a conflict area; the vehicles are arranged into queues in the formation section; and the collaborative control is performed in the decision section by using the two-layer control planning method.

6. The method for cooperative passage at an unsignalized intersection according to claim 5, characterized in that: In the formation section, arranging the vehicles into a queue includes: setting the vehicle spacing between vehicle j and vehicle j+1 to be D S , the maximum vehicle distance between vehicle j and vehicle j+1 is D H , when the following relationship is satisfied between vehicle j and vehicle j+1, vehicle j and vehicle j+1 are grouped into the same fleet: In the formula, v i,j (t0) represents the speed of the vehicle at time t0, v i,j,min Indicates the minimum speed of the vehicle, L B Indicates the length of the observation segment.

7. The method for cooperative passage at an unsignalized intersection according to claim 1, characterized in that: The upper layer plans the passage order of the fleet by improving the MCTS algorithm, including: in the MCTS algorithm, each node of the search tree represents a state, and each state contains the position and speed information of all vehicles at the current moment; the MCTS algorithm repeatedly executes the four stages of selection, expansion, simulation and backtracking, and stops after reaching a predetermined calculation time or number of iterations; In the selection phase, starting from the root node, an optimal child node is selected for expansion through the UCT algorithm; In the expansion phase, starting from the selected optimal child node, a new vehicle passage sequence is simulated, a new child node is generated and added to the search tree; In the simulation stage, for the expanded child nodes, starting from the root node, the process of vehicles passing through the unsignalized intersection is simulated by randomly selecting the passing order of vehicles, and the simulation process is performed downward along a path of the search tree passing through the expanded child nodes until all vehicles pass through the unsignalized intersection; In the backtracking phase, the simulation result is traced back to the root node through the path selected in the simulation phase, and the values ​​and access times of the passed nodes are updated at the same time.

8. The method for cooperative passage at an unsignalized intersection according to claim 7, characterized in that: The UCT algorithm is used to evaluate the value of each child node in the selection phase, expressed as: In the formula, β u represents the total reward value of node u, p u represents the number of times node u is visited, P represents the total number of times the parent node is visited, and C represents the trade-off coefficient used to adjust the exploration part and the utilization part, where the utilization part is Explore the section The average delay of vehicles is selected as β u Calculation of value: In the formula, t i,j,ac represents the actual travel time of a single vehicle through an unsignalized intersection, t i,j,min Indicates the minimum travel time of a single vehicle.

9. The method for cooperative passage at an unsignalized intersection according to claim 8, characterized in that: The UCT algorithm is improved by using a dynamic window method to adjust the C value and define the sliding window size W m , adjustment factor k m , number of iterations n, initial C value C init and a positive number η; Calculate the average delay time AD within the sliding window W : Calculate the overall average delay time AD T : Then the C value is adjusted by the following formula: C=max(C,η).

10. The method for cooperative passage at an unsignalized intersection according to claim 1, characterized in that: The lower layer plans the vehicle trajectory through the MPC algorithm including: 1) Define the vehicle's state variables, including position, velocity, and acceleration; 2) Define the dynamic model of the vehicle in discrete form: v i,j (t+1)=v i,j (t)+a i,j (t)ΔT In the formula, s i,j (t) is the position of vehicle j in lane i at time t, v i,j (t) represents the speed of vehicle j in lane i at time t, ΔT represents the simulation step length, and a i,j (t) represents the acceleration of vehicle j in lane i at time t; 3) Define optimization objectives and constraints; 4) According to the current state and the predicted control input sequence, the future state of the vehicle in multiple time steps is calculated to generate a series of control input sequences, namely candidate trajectories, and the state sequence under the generated control input sequence is predicted in the vehicle dynamics model; each candidate trajectory is evaluated, the corresponding objective function value is calculated, and it is checked whether all constraints are met; the trajectory with the minimum objective function value and satisfying all constraints is selected from the candidate trajectories as the optimal trajectory at the current moment; wherein the control input sequence includes the state sequence and the predicted sequence; 5) Apply the state sequence of the optimal trajectory to the vehicle; 6) Repeat the following steps in each control cycle: Update status: Get the latest status of the vehicle; Re-prediction: Based on the updated state, re-predict the future state; Re-optimization: Resolve the optimization problem to obtain a new optimal control input sequence.

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