A virtual traffic light intersection scheduling system based on a fuzzy controller

Abstract

The application provides a virtual traffic light intersection scheduling system based on a fuzzy controller, and belongs to the technical field of automatic driving specific scenarios. The intersection scheduling system is based on an intelligent network connection collaborative automatic driving system, and vehicle scheduling is performed at intersections without traffic lights. The V2X vehicle-road cloud collaborative system is used to control traffic flow in a manner similar to traffic lights in a factory or other scene without traffic lights, thereby improving the vehicle passing efficiency and safety of intersections without traffic lights. The fuzzy controller is introduced to establish an intersection scheduling system, which can adjust the lighting time of the traffic light in real time according to the traffic state information, thereby improving the intelligent degree and response speed of the traffic control system. The virtual traffic light intersection scheduling system based on the fuzzy controller reduces the cost and manpower investment of intersection management control, and improves the efficiency and benefit of traffic management.

Description

Technical Field

[0001] The present invention belongs to the technical field of specific scenarios of autonomous driving, and in particular relates to a virtual traffic light intersection scheduling system based on a fuzzy controller. Background Art

[0002] With economic development, the number of cars is also increasing, and with it comes the increasing incidence of traffic accidents and congestion. These incidents most often occur at intersections without traffic lights. Without traffic lights, the order of traffic flow depends entirely on the game between drivers. Some aggressive drivers, in order to pass quickly, constantly cut in, posing a significant safety threat to other vehicles and pedestrians. This makes intersections prone to collisions, which often cause traffic jams, significantly reducing road efficiency and even triggering secondary accidents, leading to further escalation. Summary of the Invention

[0003] To address these issues, this paper proposes a virtual traffic light intersection scheduling system based on a fuzzy controller. This system aims to achieve a traffic light-like control of traffic flow at intersections through V2X (Vehicle to Everything) wireless communication technology, thereby improving traffic efficiency and safety at intersections without traffic lights. The technical solutions provided by this invention are as follows:

[0004] A virtual traffic light intersection dispatching system based on a fuzzy controller. The intersection dispatching system is implemented based on an intelligent networked collaborative autonomous driving system and dispatches vehicles at intersections without traffic lights. Specifically, the intersection dispatching system includes a vehicle-side, a road-side, and a cloud-side.

[0005] The vehicle side includes intelligent networked autonomous driving vehicles and manual vehicles; the intelligent networked autonomous driving vehicles achieve autonomous driving through an intelligent networked collaborative autonomous driving system, and both the intelligent networked autonomous driving vehicles and manual vehicles are equipped with vehicle-to-everything (V2X) onboard unit (OBU) communication equipment for wireless communication technology to communicate with the road side and the cloud; the manual vehicle is also equipped with display alarm equipment;

[0006] The roadside equipment is installed on the roadside, including roadside sensors, edge computing units (MECs), and roadside units (RSUs). Roadside sensors are used to detect vehicles and road conditions in the area. MECs perform structured processing on the data monitored by sensors in real time, and RSUs communicate with the cloud and vehicle-side.

[0007] The cloud server analyzes and calculates the vehicle and road condition information obtained in real time from the vehicle and road sides. It uses a fuzzy controller to judge the vehicle and road condition information, outputs the travel time for each direction at the intersection, and sends it to the roadside equipment, which then sends it to the vehicle side via the RSU.

[0008] Specifically, the process of the vehicle receiving the travel time of each direction at the intersection is as follows:

[0009] For autonomous vehicles, based on the intelligent network-connected collaborative autonomous driving system, the vehicle reports its own positioning and other information to the cloud. The cloud directly sends the travel time in each direction of the intersection and the vehicle trajectory to the vehicle through the fuzzy controller.

[0010] For manual vehicles, the V2X OBU device on the vehicle side communicates with the RSU on the road side. The sensors on the road side obtain vehicle information in real time and perform structured processing. The RSU on the road side uploads the real-time roadside information obtained and the information uploaded to the road side by the manual vehicle OBU to the cloud. The cloud sends the travel time in each direction of the intersection to the road side through the fuzzy controller. The RSU on the road side forwards the travel time in each direction of the intersection sent by the cloud to the vehicle side. After receiving the information, the OBU on the vehicle side prompts the driver of the traffic conditions at the intersection ahead by displaying an alarm device.

[0011] Furthermore, the specific construction process of the fuzzy controller is as follows:

[0012] Step 1: Traverse the traffic phase according to the road conditions at the intersection to obtain the traffic phase sequence;

[0013] Step 2: Select the deviation e between the actual and target traffic flows as the observed variable, and the traffic light duration u as the controlled variable. For one of the traffic phases, the expected traffic flow during the duration u when the traffic light is green is C, but the actual measured traffic flow during this phase is X. The deviation e = ΔX = XC is obtained.

[0014] Step 3: Fuzzify the observed and controlled variables using the triangle membership function;

[0015] Step 4: Divide the deviation e into at least five fuzzy sets, including negative large, negative small, zero, positive small, and positive large. A negative e indicates that the current traffic flow is lower than the target traffic flow, and a positive e indicates that the current traffic flow is higher than the target traffic flow. Set the value range of the deviation e to [-E, E], where E is a positive integer.

[0016] The control variable u is divided into at least 5 fuzzy sets, including negative large, negative small, zero, positive small, and positive large; u is negative, indicating an increase in the duration of the phase signal light, and u is positive, indicating a decrease in the duration of the phase signal light. The value range of u is set to [-D, D];

[0017] Step 5: According to the traffic flow control rules, formulate fuzzy rules and find the fuzzy relationship R;

[0018] Step 6: The control quantity output by the fuzzy controller is defuzzified according to the "maximum membership principle" to obtain the travel time for that phase. The cloud sends the travel time output by the fuzzy controller to the vehicle and road ends. The design process of the fuzzification rules for the remaining phases is the same, and the rules between each phase are mutually exclusive. Only one phase is passable at a time point.

[0019] Furthermore, the intelligent connected autonomous driving vehicle is equipped with intelligent sensors, intelligent driving controllers, positioning equipment, and V2X OBU communication equipment; wherein the intelligent sensors are responsible for sensing the vehicle's driving status and driving environment; the positioning equipment includes the global navigation satellite system GNSS and the inertial navigation system INS. The GNSS uses real-time dynamic technology to provide high-precision positioning for autonomous driving vehicles. The INS monitors the vehicle's operating status and calculates the path, providing redundancy for vehicle positioning. The intelligent driving controller receives vehicle control instructions from the cloud, makes decisions based on the data from the intelligent sensors, and sends the vehicle control instructions to the vehicle controller local area network; the V2X OBU communication equipment is responsible for connecting the cloud and the intelligent driving controller. The communication equipment receives vehicle control instructions from the cloud server and transmits the instructions to the intelligent driving controller, while uploading data from the vehicle-side intelligent sensors to the cloud server.

[0020] Furthermore, the roadside sensors include low-latency cameras and solid-state lidars, which are used to detect all targets in the area and output the target's location, speed, and other vehicle and road condition information.

[0021] Furthermore, the cloud server communicates with the vehicle side and the road side via the TCP / IP protocol.

[0022] Furthermore, the road conditions at the intersection include a one-way two-lane cross intersection, and the lane vehicle selection passage phase of the intersection includes a four-phase sequence: east-west execution, east-west left turn, north-south execution, and north-south left turn.

[0023] Furthermore, the formula for fuzzification using the triangle membership function is as follows:

[0024]

[0025] Where x is the independent variable, which is the level of change of the deviation e; u(x) is the dependent variable, which is the membership value corresponding to the level of change; a, b, and c are the left endpoint, vertex, and right endpoint of the isosceles triangle; the lengths of ab and bc in each fuzzy set are set to 2 unit lengths.

[0026] Furthermore, the fuzzy rules are formulated as follows:

[0027] If e is negative and large, then u is negative and large;

[0028] If e is negatively small, then u is negatively small;

[0029] If e is zero, then u is zero;

[0030] If e is small, then u is small;

[0031] If e is positive, then u is positive;

[0032] When u is negative, the phase green light time is shortened, and when u is positive, the phase green light time is prolonged.

[0033] The beneficial effects of this method are as follows:

[0034] 1) Through the V2X vehicle-road-cloud collaborative system, a traffic light-like effect is achieved in scenes without traffic lights, such as factories, to control traffic flow, thereby improving the efficiency and safety of vehicles at intersections without traffic lights.

[0035] 2) By introducing a fuzzy controller to establish an intersection dispatching system, the lighting time of traffic lights can be adjusted in real time according to traffic status information, thereby improving the intelligence and response speed of the traffic control system.

[0036] 3) By establishing a virtual traffic light intersection dispatching system based on fuzzy controller, the cost and manpower input of intersection management and control are reduced, and the efficiency and benefits of traffic management are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the architecture diagram of the intelligent connected collaborative autonomous driving system;

[0038] Figure 2 This is a schematic diagram of roadside equipment;

[0039] Figure 3 This is a diagram of the virtual traffic light architecture;

[0040] Figure 4 It is the phase diagram of the classic intersection;

[0041] Figure 5 This is the structure diagram of the fuzzy controller based on the prediction results. DETAILED DESCRIPTION

[0042] In order to better understand the purpose, structure and function of the present invention, the following is a further detailed description of a virtual traffic light intersection scheduling method and system based on a fuzzy controller of the present invention in conjunction with the accompanying drawings.

[0043] This embodiment provides a virtual traffic light intersection dispatching system based on a fuzzy controller. The system is based on an intelligent networked collaborative autonomous driving system. Its architecture is as follows: Figure 1 As shown in the figure, the system is divided into three parts: vehicle side, field side and cloud side.

[0044] The vehicle side includes intelligent connected autonomous vehicles and manual vehicles. Specifically, intelligent connected autonomous vehicles are equipped with intelligent sensors, intelligent driving controllers (including industrial computers and cloud controllers), positioning equipment (GNSS and INS), and V2X OBU (On Board Unit) communication equipment. The intelligent sensors can be lidars, responsible for sensing the vehicle's driving status and driving environment. The GNSS (Global Navigation Satellite System) uses RTK technology (Real-time Kinematic) to provide centimeter-level and meter-level high-precision positioning for autonomous vehicles. The INS (Inertial Navigation System) can monitor the vehicle's operating status and calculate the path, providing redundancy for vehicle positioning. The intelligent driving controller includes an industrial computer and a cloud controller. The cloud controller is responsible for receiving vehicle control commands from the cloud. The industrial computer makes decisions based on the data from the intelligent sensors and controls the cloud controller to send vehicle control commands to the vehicle's CAN (Controller Area Network) network. The V2X OBU communication device is responsible for connecting the cloud and the intelligent driving controller. The communication device receives vehicle control commands from the cloud server and transmits the commands to the cloud controller. At the same time, it uploads data from the vehicle-side intelligent sensors to the cloud server. The intelligent driving controller includes the vehicle controller VCU, the electronic power steering system EPS, the electronic stability system ESC, etc. Manual vehicles are equipped with V2X OBU devices, display alarm devices, etc. The V2X OBU device is used to communicate with the roadside RSU (Road Side Unit), thereby indirectly uploading its own information to the system cloud platform or receiving warning and travel time information. The display alarm device is used to provide the driver with the intersection travel time and warning information issued by the intersection dispatch system. The field end is a roadside device installed on the roadside, such as Figure 2 As shown in the figure, the roadside sensors on the roadside equipment include two types of sensors: low-latency cameras and solid-state lidars, which are used to detect all targets in the area and output information such as the target's location and speed, providing redundancy for vehicle safety. At the same time, they are equipped with MEC (Multi-access Edge Computing), which can structure the data of low-latency cameras and solid-state lidars. Finally, they are also equipped with roadside units (RSUs) that can communicate with the cloud and the vehicle.

[0045] The cloud mainly refers to the cloud server, which analyzes and calculates the vehicle and road condition information uploaded in real time by the vehicle and road ends through the TCP / IP protocol, and sends the vehicle control instructions to the vehicle end.

[0046] At present, traffic signal scheduling at intersections mainly changes the phase and color of traffic lights to reduce traffic congestion, avoid traffic accidents, and optimize traffic efficiency. However, in scenarios such as factories, the construction of physical traffic lights often has some problems. In order to solve this technical problem, such as Figure 3 As shown, this embodiment proposes a virtual traffic light solution based on a cloud platform and V2X devices. This solution aims to address vehicle traffic flow issues at intersections without traffic lights, such as factories, or where installing traffic lights is inconvenient. This intersection is equipped with a V2X roadside unit (RSU) that can communicate with the cloud or vehicles equipped with V2X onboard units (OBUs). Roadside sensors are also installed on the roadside to monitor traffic flow in all directions of the intersection in real time (traffic flow is defined as the number of vehicles passing through within a specified time period). This data is then processed by the MEC and uploaded to the cloud. Vehicles passing through this intersection must be either intelligent, connected, and autonomous vehicles that can communicate with and be controlled by the cloud and the roadside, or at least equipped with a V2X OBU that can communicate with the roadside and has a corresponding display to provide traffic guidance to drivers.

[0047] The cloud server in the cloud uses a fuzzy controller to judge the traffic flow and traffic priority in each direction of the intersection, outputs the travel time in each direction of the intersection, and sends it to the roadside equipment, and then sends it to all vehicles via the RSU.

[0048] like Figure 3 As shown in the figure, at an intersection without traffic lights, vehicles passing through follow the instructions of the intersection dispatch system. Specifically, these vehicles can be intelligent, connected, and autonomous vehicles equipped with V2X OBU communication equipment, or manually driven vehicles equipped with V2X OBU equipment and display and alarm equipment. Roadside equipment located on the roadside is equipped with sensors, MEC, and RSU. The MEC performs a certain degree of fusion processing on the information sensed by the sensors and communicates with the cloud and vehicle side through the RSU. The cloud server within the cloud receives data from the vehicle side and the road side, processes and makes decisions, and uses a fuzzy controller to determine the travel time for each direction based on traffic flow. Specifically, for autonomous vehicles, the vehicle side reports its own positioning and other information to the cloud, which directly transmits the travel time for each direction at the intersection and the vehicle trajectory to the vehicle side. For manual vehicles, the V2X OBU device on the vehicle side communicates with the RSU on the road side. The sensors on the road side acquire vehicle information and perform structured processing. The RSU on the road side uploads the acquired real-time roadside information and the information uploaded by the manual vehicle OBU to the cloud. The cloud side sends the travel time in each direction of the intersection to the road side. The RSU on the road side forwards the travel time in each direction of the intersection sent by the cloud side to the vehicle side. After receiving the information, the OBU on the vehicle side prompts the driver of the traffic conditions at the intersection ahead by displaying an alarm device.

[0049] The construction process of the fuzzy controller is as follows:

[0050] Fuzzy control is an intelligent control method based on fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning. It behaviorally mimics the human fuzzy reasoning and decision-making process. This method first compiles operator or expert experience into fuzzy rules. It then fuzzifies real-time sensor signals, uses the fuzzified signals as input to the fuzzy rules, and after fuzzy reasoning, applies the output to the actuators. Its characteristics include a simple structure and strong robustness; it does not require a precise mathematical model of the controlled object; fuzzy control is highly consistent with the characteristics of human brain activity; and its fuzziness and empirical nature are both based on long-term experience.

[0051] This embodiment takes the classic single-lane two-lane intersection as a reference, such as Figure 4 As shown, the lane vehicles choose the passage phase in the classic four-phase sequence: east-west execution, east-west left turn, north-south execution, and north-south left turn.

[0052] From the perspective of actual traffic control, when the queue time at the intersection is long, the signal cycle should be lengthened, but generally not more than 200 seconds, to prevent drivers from getting bored; when the queue time is short, the signal cycle should be shortened, but generally not less than 15 seconds, to avoid the phenomenon that vehicles are too late to pass through the intersection or speed up to pass through the intersection. Figure 5 As shown, the specific process is as follows:

[0053] First, we need to select the observed and controlled variables. The observed variable is the deviation e between the actual and expected traffic flow, and the controlled variable is the traffic light duration u. Assume that during one phase of the traffic light control, the expected traffic flow during the green light period u is C, but the actual measured traffic flow during that phase is X. The deviation is then calculated as e = ΔX = XC.

[0054] Afterwards, the observed quantity (output) and the controlled quantity (input) are fuzzified. Here, the classic triangular membership function (the shape of the triangular membership function is an isosceles triangle, hence the name. It is usually defined by three parameters: the left endpoint (a), the vertex (b), and the right endpoint (c). These three parameters together determine the shape and range of the function) is used to fuzzify the traffic flow deviation value and the virtual signal light control travel time. The formula is as follows:

[0055]

[0056] Here, x is the independent variable, representing the level of change e in the deviation value, and u(x) is the dependent variable, representing the corresponding membership value at that level of change. a, b, and c are the left, vertex, and right endpoints of an isosceles triangle. In each fuzzy set in this paper, the lengths of ab and bc are set to 2 unit lengths.

[0057] Divide the deviation e into 5 fuzzy sets, negative large (NB e ), negative small (NS e ), zero (O e )、正小(PS e ), Zhengda (PB e ), a negative e indicates that the current traffic flow is lower than the target traffic flow, and a positive e indicates that the current traffic flow is higher than the target traffic flow. The value range of e is set to [-3, 3] (the value range is based on experience), and the corresponding fuzzy table is as follows:

[0058]

[0059] Similarly, the control quantity u is divided into 5 fuzzy sets, negative large (NB u ), negative small (NS u ), zero (O u )、正小(PS u ), Zhengda (PB u ), u is negative, indicating an increase in the duration of the phase signal light, and u is positive, indicating a decrease or increase in the duration of the phase signal light. Set the value range of u to [-D, D] (the input and output of the fuzzy controller are selected based on experience and can be modified at any time), and the corresponding fuzzy table is as follows:

[0060]

[0061] Then, according to the traffic flow control rules, fuzzy rules are formulated, which are as follows:

[0062] If e is negative and large, then u is negative and large;

[0063] If e is negatively small, then u is negatively small;

[0064] If e is zero, then u is zero;

[0065] If e is positively small, then u is positively small;

[0066] If e is positive, then u is positive;

[0067] When u is negative, the phase green light time should be shortened, and when u is positive, the phase green light time should be prolonged. The rules are as follows:

[0068]

[0069] Then, the fuzzy relation R can be obtained, which can be expressed as the Cartesian product of the input and output quantities. The calculation method is similar to that of the matrix, but in the fuzzy relation calculation, multiplication is taken as AND, and addition is taken as OR. According to the above fuzzy rules, we can get:

[0070] R=(NB e ×NBu )∪(NS e ×NS u )∪(0 e ×0 u )∪(PS e ×PS u )∪(PB e ×PB u )

[0071] Easy to get:

[0072]

[0073] Finally, the output can be controlled by the fuzzy controller. The phase control quantity transit time u=e°R. For example, when e=NB,

[0074] u=e°R=[1.0 0.5 0.5 0.5 0.0 0.0 0.0 0.0 0.0]

[0075] The controller output is a simulation vector, then:

[0076]

[0077] Defuzzification based on the "maximum membership principle" results in a control variable of u = -D, shortening the green light timing for that phase by D. For connected autonomous vehicles that can communicate directly with the cloud, the cloud sends this command to the vehicle. For external vehicles that cannot communicate directly with the cloud but are equipped with a V2X OBU capable of communicating with a roadside V2X RSU, the cloud sends the command to the roadside RSU, which then broadcasts it to the vehicle. The design process for fuzzification rules for the remaining phases is the same, and the rules for each phase are mutually exclusive, so only one phase has a green light at any given time.

[0078] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. A virtual traffic light intersection dispatching system based on fuzzy controller, characterized in that: The intersection dispatching system is based on an intelligent network-connected collaborative autonomous driving system and dispatches vehicles at intersections without traffic lights. Specifically, the intersection dispatching system includes a vehicle-side, a road-side, and a cloud-side. The vehicle side includes intelligent networked autonomous driving vehicles and manual vehicles; the intelligent networked autonomous driving vehicles achieve autonomous driving through an intelligent networked collaborative autonomous driving system, and both the intelligent networked autonomous driving vehicles and manual vehicles are equipped with vehicle-to-everything (V2X) onboard unit (OBU) communication equipment for wireless communication technology to communicate with the road side and the cloud; the manual vehicle is also equipped with display alarm equipment; The roadside equipment is installed on the roadside, including roadside sensors, edge computing units (MECs), and roadside units (RSUs). Roadside sensors are used to detect vehicles and road conditions in the area. MECs perform structured processing on the data monitored by sensors in real time, and RSUs communicate with the cloud and vehicle-side. The cloud server analyzes and calculates the vehicle and road condition information obtained in real time from the vehicle and road sides. It uses a fuzzy controller to judge the vehicle and road condition information, outputs the travel time for each direction at the intersection, and sends it to the roadside equipment, which then sends it to the vehicle side via the RSU. Specifically, the process of the vehicle receiving the travel time of each direction at the intersection is as follows: For autonomous vehicles, based on the intelligent network-connected collaborative autonomous driving system, the vehicle reports its own positioning information to the cloud, and the cloud directly sends the travel time in each direction of the intersection and the vehicle trajectory to the vehicle through the fuzzy controller; For manual vehicles, the OBU device on the vehicle side communicates with the RSU on the road side. The sensors on the road side obtain vehicle information in real time and perform structured processing. The roadside RSU uploads the acquired real-time roadside information and the information uploaded by the manual vehicle OBU to the cloud. The cloud sends the travel time for each direction of the intersection to the roadside through the fuzzy controller. The roadside RSU forwards the travel time for each direction of the intersection sent by the cloud to the vehicle. After receiving the information, the vehicle-side OBU uses the display alarm device to inform the driver of the traffic conditions of the intersection ahead. The specific construction process of the fuzzy controller is as follows: Step 1: Traverse the traffic phase according to the road conditions at the intersection to obtain the traffic phase sequence; Step 2: Select the deviation e between the actual and target traffic flows as the observed variable, and the traffic light duration u as the controlled variable. For one of the traffic phases, the target traffic flow during the duration u when the traffic light is green is C, but the actual traffic flow during this phase is X. The deviation e = ΔX = XC is obtained. Step 3: Fuzzify the observed and controlled variables using the triangle membership function; Step 4: Divide the deviation e into at least five fuzzy sets, including negative large, negative small, zero, positive small, and positive large. A negative e indicates that the current traffic flow is lower than the target traffic flow, and a positive e indicates that the current traffic flow is higher than the target traffic flow. Set the value range of the deviation e to [-E, E], where E is a positive integer. Divide the control variable u into at least 5 fuzzy sets, including negative large, negative small, zero, positive small, and positive large; u is negative, which means increasing the duration of the phase signal light; u is positive, which means decreasing the duration of the phase signal light. The value range of u is set to [-D, D]. Step 5: According to the traffic flow control rules, formulate fuzzy rules and find the fuzzy relationship R; Step 6: The fuzzy controller outputs the controlled variable and performs defuzzification according to the "maximum membership principle" to obtain the travel time for that phase. The cloud sends the travel time output by the fuzzy controller to the vehicle and road terminals. The design process for fuzzification rules for the remaining phases is the same, and the rules for each phase are mutually exclusive. Only one phase is passable at a time.

2. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 1, characterized in that: The intelligent connected autonomous vehicle is equipped with intelligent sensors, an intelligent driving controller, a positioning device, and a V2X OBU communication device. The intelligent sensors are responsible for sensing the vehicle's driving status and driving environment. The positioning device includes a global navigation satellite system (GNSS) and an inertial navigation system (INS). The GNSS uses real-time dynamic technology to provide high-precision positioning for the autonomous vehicle. The INS monitors the vehicle's operating status and calculates the path, providing redundancy for vehicle positioning. The intelligent driving controller receives vehicle control commands from the cloud, makes decisions based on the data from the intelligent sensors, and sends the vehicle control commands to the vehicle controller local area network. The V2XOBU communication device is responsible for connecting the cloud and the intelligent driving controller. The communication device receives vehicle control commands from the cloud server and transmits the commands to the intelligent driving controller. At the same time, it uploads data from the vehicle-side intelligent sensors to the cloud server.

3. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 2, characterized in that: The roadside sensors include low-latency cameras and solid-state lidars, which are used to detect all targets in the area and output the target's location, speed, and other vehicle and road condition information.

4. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 3, characterized in that: The cloud server communicates with the vehicle side and the road side via TCP / IP protocol.

5. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 4, characterized in that: The road conditions at the intersection include a one-way two-lane cross intersection, and the lane vehicle selection phase for the intersection includes a four-phase sequence: east-west execution, east-west left turn, north-south execution, and north-south left turn.

6. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 5, characterized in that: The formula for fuzzification using the triangle membership function is as follows: Where x is the independent variable, which is the level of change of the deviation e; u(x) is the dependent variable, which is the membership value corresponding to the level of change; a, b, and c are the left endpoint, vertex, and right endpoint of the isosceles triangle; the lengths of ab and bc in each fuzzy set are set to 2 unit lengths.

7. A virtual traffic light intersection dispatching system based on fuzzy controller according to claim 6, characterized in that: The fuzzy rules are formulated as follows: If e is negative and large, then u is negative and large; If e is negative and small, then u is negative and small; If e is zero, then u is zero; If e is small, then u is small; If e is positive, then u is positive; When u is negative, the phase green light time is shortened, and when u is positive, the phase green light time is prolonged.

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