A CACC simulation platform for urban scenarios

Abstract

The present invention relates to a CACC simulation platform for urban scenes, comprising: a traffic generation module, which is used to randomly generate a road network containing urban scenes and traffic flows on the road network, wherein the traffic flows include CACC vehicles and human-driven vehicles; a traffic control module, which is used to generate traffic information of urban scenes, wherein the traffic information of each urban scene includes one or more of signal lights, conflict areas, and priority rules; a vehicle control module, which comprises a decision maker, a controller, and an actuator, wherein the decision maker is used to generate a mode switching instruction of a CACC vehicle according to the road network information and the traffic control instruction, the controller switches the vehicle mode of the CACC vehicle according to the mode switching instruction, and generates a corresponding vehicle control instruction according to the current vehicle mode, and the actuator controls the CACC vehicle to travel according to the vehicle control instruction. Compared with the prior art, the present invention realizes CACC simulation of urban scenes, and has the advantages of both a vehicle simulation platform and a traffic simulation platform.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and in particular to a CACC simulation platform for urban scenarios. Background Art

[0002] CACC (Cooperative Adaptive Cruise Control) is a key technology to alleviate urban congestion. It is an improvement on ACC technology and can obtain more traffic information from the time and space dimensions. It not only improves the perception and decision-making ability of a single vehicle, but also provides conditions for the collaborative perception and decision-making of a group of vehicles, and even realizes information sharing of the entire traffic information system. CACC vehicles travel in formation on the road. The front vehicle can pass information such as position and speed to the CACC vehicle, and then the algorithm maintains a small headway and moves forward steadily.

[0003] It is very important to evaluate the effect of CACC in urban scenarios. Many existing CACC studies are oriented towards highways, proving the benefits of CACC in this scenario. However, in urban road scenarios with mixed traffic backgrounds, the safety, efficiency and environmental protection of CACC have not been widely and effectively verified, which limits the application of CACC technology.

[0004] There are two ways to evaluate CACC: real vehicle testing and simulation testing. Because real vehicle testing has the disadvantages of high cost, low efficiency and limited scenarios, simulation testing is necessary. In order to conduct large-scale tests more efficiently to evaluate CACC, a simulation platform is necessary. Existing simulation platforms ignore the interaction between human-driven vehicles and CACC vehicles, and their ability to evaluate CACC in intersection scenarios is limited. The current simulation platforms are mainly divided into two categories: automobiles and traffic. The automobile simulation platform has a real vehicle dynamics model and controller, which can simulate intelligent single-vehicle behavior more realistically. However, the traffic events and traffic participant behaviors in the simulation scene are pre-set, which makes the simulation scene unrealistic, especially in an environment where interactive events frequently occur, such as intersections. Another type of simulation platform is the traffic simulation platform. Although they can realize large-scale mixed traffic flow simulation, due to the lack of a real autonomous driving system, the interaction between vehicles lacks randomness. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a CACC simulation platform for urban scenes, which realizes CACC simulation of urban scenes and has the advantages of both vehicle simulation platform and traffic simulation platform.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A CACC simulation platform for urban scenarios, including:

[0008] A traffic generation module, used to randomly generate a road network including an urban scene and traffic flows on the road network, wherein the traffic flows include CACC vehicles and human-driven vehicles;

[0009] A traffic control module, used to generate traffic information of urban scenes, where the traffic information of each urban scene includes one or more of traffic lights, conflict areas and priority rules;

[0010] The vehicle control module includes a decision maker, a controller and an actuator, wherein the decision maker is used to generate a mode switching instruction of the CACC vehicle according to the road network information and the traffic control instruction, the controller switches the vehicle mode of the CACC vehicle according to the mode switching instruction and generates a corresponding vehicle control instruction according to the current vehicle mode, and the actuator controls the CACC vehicle to travel according to the vehicle control instruction;

[0011] The vehicle modes of the CACC vehicle are divided into CACC mode, signal light vehicle mode and conflict vehicle mode. A CACC vehicle in CACC mode travels based on a CACC model, and a CACC vehicle in signal light vehicle mode and conflict vehicle mode slows down or stops.

[0012] The decision makers include a CACC decision maker, a signal light leading vehicle decision maker and a conflict leading vehicle decision maker;

[0013] The CACC decision maker is used to generate a CACC mode switching instruction;

[0014] The signal light vehicle decision maker is used to generate a signal light vehicle mode switching instruction when the CACC vehicle faces a city scene with signal lights;

[0015] The conflicting head vehicle decision maker is used to generate a conflicting head vehicle mode switching instruction;

[0016] The priorities of the signal light leading vehicle decision maker, conflict leading vehicle decision maker and CACC decision maker decrease in sequence.

[0017] Furthermore, the signal light head vehicle mode switching instruction includes a signal light head vehicle mode entry instruction and a signal light head vehicle mode exit instruction;

[0018] The specific process of the signal light vehicle decision maker generating the signal light vehicle mode switching instruction includes:

[0019] Periodically executing the first determination step;

[0020] The first determination step includes the following sub-steps:

[0021] S11, determining whether the current CACC vehicle is in the signal light vehicle mode, if so, executing sub-step S12, otherwise executing sub-step S13;

[0022] S12, determining whether the traffic light is green, if so, generating a traffic light head vehicle mode exit instruction, otherwise maintaining the current CACC vehicle current mode;

[0023] S13, determining whether the distance between the current CACC vehicle and the urban scene is less than the set distance, if so, executing step sub-step S14, otherwise maintaining the current mode of the current CACC vehicle;

[0024] S14, determine whether the current CACC vehicle turns right, if so, keep the current CACC vehicle in the current mode, otherwise, execute step sub-step S15;

[0025] S15, determining whether the traffic light in front of the current CACC vehicle is red, if so, marking the current CACC vehicle and executing sub-step S18, otherwise executing sub-step S16;

[0026] S16, determining whether the traffic light in front of the current CACC vehicle is yellow, if so, executing sub-step S17, otherwise maintaining the current CACC vehicle current mode;

[0027] S17, determining whether the current CACC vehicle has enough time to enter the city scene, if so, maintaining the current CACC vehicle mode, otherwise executing step sub-step S18;

[0028] S18. Determine whether the current CACC vehicle is the vehicle closest to the traffic light among all marked CACC vehicles. If so, generate a traffic light head vehicle mode entry instruction.

[0029] Furthermore, the specific process of the sub-step S17 includes:

[0030] Determine whether the following conditions are met:

[0031] d intersection <v*(t remaing_yellow_light -t safe )

[0032] Among them, d intersection is the distance between the CACC vehicle and the urban scene, v is the current speed of the CACC vehicle, and t remaing_yellow_light is the remaining yellow light time, t safe To set a safe time.

[0033] Furthermore, the specific process of the conflicting head vehicle decision maker generating the conflicting head vehicle mode switching instruction includes:

[0034] Periodically executing the second determination step;

[0035] The second determination step includes the following sub-steps:

[0036] S21, determine whether there is a conflict event in the city scene, if yes, execute sub-step S22, otherwise execute sub-step S26;

[0037] S22, determine whether the current CACC vehicle has the highest priority, if yes, execute sub-step S26, otherwise execute sub-step S23;

[0038] S23, judging whether the current CACC vehicle can pass in front of the high-priority vehicle according to the time when the current CACC vehicle arrives at the conflict area, if so, executing sub-step S26, otherwise executing sub-step S24;

[0039] S24, searching the conflict table for all CACC vehicles facing the same conflict area;

[0040] S25, determining whether the current CACC vehicle is the closest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction and executing step S27, otherwise executing sub-step S26;

[0041] S26, maintaining the current CACC vehicle current mode;

[0042] S27, update the conflict table;

[0043] The conflict table stores conflict events in all conflict areas.

[0044] Furthermore, the CACC mode is divided into a leading vehicle mode, a following vehicle mode and a human driving mode, the CACC mode switching instruction includes a leading vehicle mode entry instruction, a following vehicle mode entry instruction and a human driving mode entry instruction, and the specific process of the CACC decision maker generating the CACC mode switching instruction includes:

[0045] Periodically executing the third determination step;

[0046] The third determination step includes the following sub-steps:

[0047] S31, determining whether the current CACC vehicle has started lateral behavior, if so, generating a human driving mode entry instruction, otherwise executing sub-step S32;

[0048] S32, determining whether the current CACC vehicle has reached the destination, if so, generating a human driving mode entry instruction, otherwise executing sub-step S33;

[0049] S33, determining whether the vehicle ahead of the current CACC vehicle is a following vehicle, if so, executing sub-step S34, otherwise generating a leading vehicle mode entry instruction;

[0050] S34, determining whether the current CACC vehicle and the vehicle ahead are in the same convoy, if so, generating a vehicle following mode entry instruction, otherwise executing step S35;

[0051] S35, determining whether the distance between the current CACC vehicle and the vehicle ahead is less than the communication range, if so, executing step S36, otherwise generating a leading vehicle mode entry instruction;

[0052] S36, determining whether the total number of vehicles in the current CACC vehicle fleet is less than the maximum number of vehicles in the fleet, if so, executing step S37, otherwise generating a leading vehicle mode entry instruction;

[0053] S37. Determine whether the current CACC vehicle and the vehicle ahead are traveling in the same direction. If so, generate a following vehicle mode entry instruction; otherwise, generate a leading vehicle mode entry instruction.

[0054] Furthermore, the control model of the CACC vehicle in human driving mode is the Wiedemann 99 model.

[0055] Furthermore, when the CACC vehicle is in a signal light leading vehicle mode or a conflict leading vehicle mode, the controller generates a vehicle control instruction for controlling the CACC vehicle to decelerate, and the specific process includes:

[0056] Assuming that there is a stationary virtual car at the stop line, the IDM model is used to control the CACC vehicle to follow the virtual car to achieve deceleration and stop.

[0057] Furthermore, the calculation formula of the CACC vehicle's own vehicle acceleration during the deceleration process is:

[0058] If the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula for the vehicle acceleration is:

[0059]

[0060] Among them, a ACC is the vehicle acceleration, a free is the expected acceleration, s * is the expected distance, a is a constant, s is the actual distance between the CACC vehicle and the stop line, δ is the speed coefficient, v0 is the expected speed, and v is the current vehicle speed;

[0061] If the current speed of the CACC vehicle is greater than the expected speed, the calculation formula of the vehicle acceleration is:

[0062]

[0063] Where b is a constant;

[0064] The calculation formula of the expected distance is:

[0065]

[0066] Among them, s0 is the static safety distance and T is the safety time distance.

[0067] Furthermore, the actuator executes the vehicle control instruction through the vehicle dynamics model and obtains the actual acceleration.

[0068] Furthermore, the control model of the human driving the vehicle is the Wiedemann 99 model.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] (1) The CACC simulation platform of the present invention embeds a traffic control module, realizes CACC simulation of urban scenes, and has the advantages of both vehicle simulation platform and traffic simulation platform. It can simulate real vehicle micro-interaction behavior and has a real automatic driving system. The simulation process is random.

[0071] (2) The present invention collects CACC vehicle data during the simulation process of the CACC simulation platform and performs post-processing to calculate mobility indicators such as delay and capacity and safety indicators such as the number of emergency stops, so as to evaluate the efficiency and safety of CACC vehicles at intersections. The CACC simulation platform can be used as a development and testing tool in the process of CACC technology research and development, and also provides an evaluation tool for transportation practitioners, which is convenient for studying the impact of traffic control strategies on CACC traffic and the impact of CACC under mixed traffic on urban roads. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is the structural block diagram of the CACC simulation platform;

[0073] Figure 2 is a flow chart of the third determination step;

[0074] Figure 3 is a flow chart of the first judgment step;

[0075] Figure 4 is a flow chart of the second determination step;

[0076] Figure 5 Schematic diagram of the simulation visualization results of the CACC simulation platform. DETAILED DESCRIPTION

[0077] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0078] A CACC simulation platform for urban scenarios. The urban scenario is a three-lane signalized intersection with two-phase traffic lights, a signal cycle of 100 seconds, a yellow light time of 3 seconds, and a conflict area between left-turning vehicles and oncoming straight-moving vehicles. Straight-moving vehicles have higher priority than left-turning vehicles.

[0079] like Figure 1 , the CACC simulation platform includes:

[0080] Traffic generation module, used to randomly generate a road network containing urban scenes and traffic flows on the road network, including CACC vehicles and human-driven vehicles;

[0081] A traffic control module, used to generate traffic information of urban scenes, where the traffic information of each urban scene includes one or more of traffic lights, conflict areas and priority rules;

[0082] The vehicle control module includes a decision maker, a controller and an actuator. The decision maker is used to generate a mode switching instruction of the CACC vehicle according to the road network information and the traffic control instruction. The controller switches the vehicle mode of the CACC vehicle according to the mode switching instruction and generates a corresponding vehicle control instruction according to the current vehicle mode. The actuator controls the CACC vehicle to travel through the vehicle dynamics model according to the vehicle control instruction.

[0083] Among them, the vehicle modes of CACC vehicles are divided into:

[0084] CACC mode: vehicles in a connected fleet;

[0085] Signal light lead vehicle mode: the lead vehicle of the connected fleet facing the signal light

[0086] Conflict lead vehicle mode: the lead vehicle of the connected fleet facing the conflict area;

[0087] CACC vehicles in CACC mode travel based on the CACC model, and CACC vehicles in signal light head vehicle mode and conflict head vehicle mode slow down or stop;

[0088] The decision makers include CACC decision maker, signal light leading vehicle decision maker and conflict leading vehicle decision maker;

[0089] The CACC decision maker is used to generate CACC mode switching instructions;

[0090] The signal head vehicle decision maker is used to generate a signal head vehicle mode switching instruction when the CACC vehicle faces a city scene with signal lights;

[0091] The conflicting head vehicle decision maker is used to generate a conflicting head vehicle mode switching instruction;

[0092] The priorities of the signal light leading vehicle decision maker, conflict leading vehicle decision maker and CACC decision maker decrease in sequence.

[0093] In order to enable CACC vehicles to pass in urban scenarios, this embodiment designs a signal light head vehicle decision maker and a conflict area head vehicle decision maker, which are used to determine whether the connected autonomous driving vehicle needs to enter the signal light head vehicle mode or the conflict area head vehicle mode. Each vehicle will run two decision makers at each time step.

[0094] The priority of the signal head vehicle decision maker is higher than the conflict area decision maker, that is, the CACC vehicle will use the signal head vehicle decision maker first, because in some scenarios, there is often a signal light before the conflict area to allocate the right of way, avoiding the occurrence of conflict events, such as signal intersections. The conflict area decision maker is only required for scenarios that the signal head vehicle decision maker cannot handle.

[0095] The signal light head vehicle mode switching instruction includes a signal light head vehicle mode entry instruction and a signal light head vehicle mode exit instruction;

[0096] The specific process of the signal light head vehicle decision maker generating the signal light head vehicle mode switching instruction includes:

[0097] Periodically executing the first determination step;

[0098] like Figure 3 , the first judgment step includes the following sub-steps:

[0099] S11, determining whether the current CACC vehicle is in the signal light vehicle mode, if so, executing sub-step S12, otherwise executing sub-step S13;

[0100] S12, determining whether the traffic light is green, if so, generating a traffic light head vehicle mode exit instruction, otherwise maintaining the current CACC vehicle current mode;

[0101] S13, determining whether the distance between the current CACC vehicle and the urban scene is less than 50m, if so, executing step sub-step S14, otherwise maintaining the current mode of the current CACC vehicle;

[0102] S14, determine whether the current CACC vehicle is turning right (not controlled by the traffic light), if so, keep the current CACC vehicle in the current mode, otherwise, execute step sub-step S15;

[0103] S15, determining whether the traffic light in front of the current CACC vehicle is red (there is no other vehicle between the red light and the vehicle), if so, marking the current CACC vehicle and executing sub-step S18, otherwise executing sub-step S16;

[0104] S16, determining whether the traffic light in front of the current CACC vehicle is yellow, if so, executing sub-step S17, otherwise maintaining the current CACC vehicle current mode;

[0105] S17, determining whether the current CACC vehicle has enough time to enter the city scene, if so, maintaining the current CACC vehicle mode, otherwise executing step sub-step S18;

[0106] S18. Determine whether the current CACC vehicle is the vehicle closest to the traffic light among all marked CACC vehicles. If so, generate a traffic light head vehicle mode entry instruction.

[0107] The specific process of sub-step S17 includes:

[0108] Determine whether the following conditions are met:

[0109] d intersection <v*(t remaing_yellow_light -t safe )

[0110] Among them, d intersection is the distance between the CACC vehicle and the urban scene, v is the current speed of the CACC vehicle, and t remaingr_yellow_light is the remaining yellow light time, t safe To set a safe time.

[0111] When facing a conflict area, the connected autonomous driving vehicle will determine whether to enter the conflict mode based on the vehicle information and environmental information. The specific process of the conflict lead vehicle decision maker generating the conflict lead vehicle mode switching instruction includes:

[0112] Periodically executing the second determination step;

[0113] like Figure 4 , the second judgment step includes the following sub-steps:

[0114] S21, judging whether there is a conflict event in the urban scene according to the vehicle information and the environmental information, if so, executing sub-step S22, otherwise executing sub-step S26;

[0115] S22, determine whether the current CACC vehicle has the highest priority, if yes, execute sub-step S26, otherwise execute sub-step S23;

[0116] S23, judging whether the current CACC vehicle can pass in front of the high-priority vehicle according to the time when the current CACC vehicle arrives at the conflict area, if so, executing sub-step S26, otherwise executing sub-step S24;

[0117] S24, searching the conflict table for all CACC vehicles facing the same conflict area;

[0118] S25, determining whether the current CACC vehicle is the closest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction and executing step S27, otherwise executing sub-step S26;

[0119] S26, maintaining the current CACC vehicle current mode;

[0120] S27, update the conflict table;

[0121] The conflict table stores conflict events in all conflict areas. Given that there are multiple conflict areas at the same intersection, multiple conflict events may occur in each conflict area at the same time, and each connected autonomous driving vehicle may also be in multiple conflict events. Therefore, it is necessary to design a conflict table to record this complex information. The conflict table stores conflict events that will occur in all conflict areas. New conflict events are added in real time in each cycle of the second judgment step. After the conflict event disappears, it needs to be deleted from the conflict table.

[0122] In sub-step S23, the method for calculating the time when the current CACC vehicle arrives at the conflict area includes:

[0123] If the CACC vehicle is stationary, the time is infinite. If the absolute value of the acceleration is less than 0.2 / s 2 , then assume that the vehicle is moving at a constant speed and estimate the time, otherwise assume that the vehicle is moving at a uniformly variable speed and estimate the time.

[0124] The CACC model is divided into:

[0125] Lead vehicle mode: the first vehicle in the connected fleet;

[0126] Following vehicle mode: other vehicles in the connected fleet except the first vehicle;

[0127] Human driving mode: CACC vehicle changing lanes;

[0128] The CACC mode switching instructions include the leading vehicle mode entry instruction, the following vehicle mode entry instruction and the human driving mode entry instruction. The specific process of the CACC decision maker generating the CACC mode switching instructions includes:

[0129] Periodically executing the third determination step;

[0130] like Figure 2The third determination step includes the following sub-steps:

[0131] S31, determining whether the current CACC vehicle has started lateral behavior, if so, generating a human driving mode entry instruction, otherwise executing sub-step S32;

[0132] S32, determining whether the current CACC vehicle has reached the destination, if so, generating a human driving mode entry instruction, otherwise executing sub-step S33;

[0133] S33, determining whether the vehicle ahead of the current CACC vehicle is a following vehicle, if so, executing sub-step S34, otherwise generating a leading vehicle mode entry instruction;

[0134] S34, determining whether the current CACC vehicle and the vehicle ahead are in the same convoy, if so, generating a vehicle following mode entry instruction, otherwise executing step S35;

[0135] S35, determining whether the distance between the current CACC vehicle and the vehicle ahead is less than the communication range, if so, executing step S36, otherwise generating a leading vehicle mode entry instruction;

[0136] S36, determining whether the total number of vehicles in the current CACC vehicle fleet is less than the maximum number of vehicles in the fleet, if so, executing step S37, otherwise generating a leading vehicle mode entry instruction;

[0137] S37. Determine whether the current CACC vehicle and the vehicle ahead are traveling in the same direction. If so, generate a following vehicle mode entry instruction; otherwise, generate a leading vehicle mode entry instruction.

[0138] The controller is implemented by PID algorithm, and the control process of the controller is divided into longitudinal control process and lateral control process;

[0139] Longitudinal control process: When the CACC vehicle is in the signal light leading vehicle mode or conflict leading vehicle mode, the controller generates a vehicle control instruction for controlling the CACC vehicle to decelerate. The specific process includes:

[0140] Assuming that there is a stationary virtual car at the stop line, the IDM model is used to control the CACC vehicle to follow the virtual car to achieve deceleration and stop.

[0141] The calculation formula of the CACC vehicle's own vehicle acceleration during deceleration is:

[0142] If the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula for the vehicle acceleration is:

[0143]

[0144] Among them, a ACC is the vehicle acceleration, afree is the expected acceleration, s * is the expected distance, a is a constant, the value is 1, s is the actual distance between the CACC vehicle and the stop line, δ is the speed coefficient, which can be obtained by model parameter calibration, the value is 4 in this embodiment, v0 is the expected speed, the value is 30km / h in this embodiment, and v is the current vehicle speed;

[0145] If the current speed of the CACC vehicle is greater than the expected speed, the calculation formula of the vehicle acceleration is:

[0146]

[0147] Where b is a constant, and its value is 1;

[0148] The expected distance is calculated as:

[0149]

[0150] Wherein, s0 is the static safety distance, which is 2m in this embodiment, and T is the safety time interval, which is 2s in this embodiment.

[0151] Lateral control process: The lateral behavior of the autonomous driving vehicle in the simulation platform is mainly lane changing. If the autonomous driving vehicle is not in the target lane and is in an area where lane changing is possible, the autonomous driving mode will change to human driving mode to change lanes. The control models of the CACC vehicle in human driving mode and the human-driven vehicle are both Wiedemann 99 models.

[0152] The actuator includes a low-level controller and a vehicle dynamics model. The low-level controller outputs throttle and brake commands according to the expected acceleration and current vehicle speed, and the vehicle dynamics model is used to obtain the actual acceleration.

[0153] The simulation effect of this embodiment is as follows Figure 5 As shown, a CACC vehicle in the traffic light leading vehicle mode will stop before the stop line, and a CACC vehicle in the conflict area leading vehicle mode will wait for the oncoming through vehicle to pass.

[0154] By collecting CACC vehicle data during the simulation and performing post-processing, mobility indicators such as delay and capacity, as well as safety indicators such as the number of emergency stops, can be calculated.

[0155] This embodiment proposes a CACC simulation platform for urban scenarios, which realizes CACC simulation of urban scenarios and has the advantages of both vehicle simulation platform and traffic simulation platform. It can simulate real vehicle micro-interaction behavior and has a real automatic driving system. The simulation process is random and can evaluate the efficiency and safety of CACC vehicles at intersections. The CACC simulation platform can be used as a development and testing tool in the process of CACC technology research and development, and also provides an evaluation tool for traffic practitioners, which is convenient for studying the impact of traffic control strategies on CACC traffic and the impact of CACC under mixed traffic on urban roads.

[0156] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A CACC simulation platform for urban scenarios, characterized by: include: A traffic generation module, used to randomly generate a road network including an urban scene and traffic flows on the road network, wherein the traffic flows include CACC vehicles and human-driven vehicles; A traffic control module, used to generate traffic information of urban scenes, where the traffic information of each urban scene includes one or more of traffic lights, conflict areas and priority rules; The vehicle control module includes a decision maker, a controller and an actuator, wherein the decision maker is used to generate a mode switching instruction of the CACC vehicle according to the road network information and the traffic control instruction, the controller switches the vehicle mode of the CACC vehicle according to the mode switching instruction and generates a corresponding vehicle control instruction according to the current vehicle mode, and the actuator controls the CACC vehicle to travel according to the vehicle control instruction; The vehicle modes of the CACC vehicle are divided into CACC mode, signal light vehicle mode and conflict vehicle mode. CACC mode: vehicles in a connected fleet; Signal light lead vehicle mode: the lead vehicle of the connected fleet facing the signal light; Conflict lead vehicle mode: the lead vehicle of the connected fleet facing the conflict area; CACC vehicles in CACC mode travel based on the CACC model, and CACC vehicles in signal light head vehicle mode and conflict head vehicle mode slow down or stop; The decision makers include a CACC decision maker, a signal light leading vehicle decision maker and a conflict leading vehicle decision maker; The CACC decision maker is used to generate a CACC mode switching instruction; The signal light vehicle decision maker is used to generate a signal light vehicle mode switching instruction when the CACC vehicle faces a city scene with signal lights; The conflicting head vehicle decision maker is used to generate a conflicting head vehicle mode switching instruction; The priorities of the signal light head vehicle decision maker, conflict head vehicle decision maker and CACC decision maker are decreased in sequence; The signal light head vehicle mode switching instruction includes a signal light head vehicle mode entry instruction and a signal light head vehicle mode exit instruction; The specific process of the signal light vehicle decision maker generating the signal light vehicle mode switching instruction includes: Periodically executing the first determination step; The first determination step includes the following sub-steps: S11, determining whether the current CACC vehicle is in the signal light vehicle mode, if so, executing sub-step S12, otherwise executing sub-step S13; S12, determining whether the traffic light is green, if so, generating a traffic light head vehicle mode exit instruction, otherwise maintaining the current CACC vehicle current mode; S13, determining whether the distance between the current CACC vehicle and the urban scene is less than the set distance, if so, executing step sub-step S14, otherwise maintaining the current mode of the current CACC vehicle; S14, determine whether the current CACC vehicle turns right, if so, keep the current CACC vehicle in the current mode, otherwise, execute step sub-step S15; S15, determining whether the traffic light in front of the current CACC vehicle is red, if so, marking the current CACC vehicle and executing sub-step S18, otherwise executing sub-step S16; S16, determining whether the traffic light in front of the current CACC vehicle is yellow, if so, executing sub-step S17, otherwise maintaining the current CACC vehicle current mode; S17, determining whether the current CACC vehicle has enough time to enter the city scene, if so, maintaining the current CACC vehicle mode, otherwise executing step sub-step S18; S18. Determine whether the current CACC vehicle is the vehicle closest to the traffic light among all marked CACC vehicles. If so, generate a traffic light head vehicle mode entry instruction.

2. A CACC simulation platform for urban scenarios according to claim 1, characterized in that: The specific process of the sub-step S17 includes: Determine whether the following conditions are met: d intersection <v*(t remaing_yellow_light -t safe ) Among them, d intersection is the distance between the CACC vehicle and the urban scene, v is the current speed of the CACC vehicle, and t remaing_yellow_light is the remaining yellow light time, t safe To set a safe time.

3. A CACC simulation platform for urban scenarios according to claim 1, characterized in that: The specific process of the conflict head vehicle decision maker generating the conflict head vehicle mode switching instruction includes: Periodically executing the second determination step; The second determination step includes the following sub-steps: S21, determine whether there is a conflict event in the city scene, if yes, execute sub-step S22, otherwise execute sub-step S26; S22, determine whether the current CACC vehicle has the highest priority, if yes, execute sub-step S26, otherwise execute sub-step S23; S23, judging whether the current CACC vehicle can pass in front of the high-priority vehicle according to the time when the current CACC vehicle arrives at the conflict area, if so, executing sub-step S26, otherwise executing sub-step S24; S24, searching the conflict table for all CACC vehicles facing the same conflict area; S25, determining whether the current CACC vehicle is the closest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction and executing step S27, otherwise executing sub-step S26; S26, maintaining the current CACC vehicle current mode; S27, update the conflict table; The conflict table stores conflict events in all conflict areas.

4. A CACC simulation platform for urban scenarios according to claim 1, characterized in that: The CACC mode is divided into a leading vehicle mode, a following vehicle mode and a human driving mode. The CACC mode switching instruction includes a leading vehicle mode entry instruction, a following vehicle mode entry instruction and a human driving mode entry instruction. The specific process of the CACC decision maker generating the CACC mode switching instruction includes: Periodically executing the third determination step; The third determination step includes the following sub-steps: S31, determining whether the current CACC vehicle has started lateral behavior, if so, generating a human driving mode entry instruction, otherwise executing sub-step S32; S32, determining whether the current CACC vehicle has reached the destination, if so, generating a human driving mode entry instruction, otherwise executing sub-step S33; S33, determining whether the vehicle ahead of the current CACC vehicle is a following vehicle, if so, executing sub-step S34, otherwise generating a leading vehicle mode entry instruction; S34, determining whether the current CACC vehicle and the vehicle ahead are in the same convoy, if so, generating a vehicle following mode entry instruction, otherwise executing step S35; S35, determining whether the distance between the current CACC vehicle and the vehicle ahead is less than the communication range, if so, executing step S36, otherwise generating a leading vehicle mode entry instruction; S36, determining whether the total number of vehicles in the current CACC vehicle fleet is less than the maximum number of vehicles in the fleet, if so, executing step S37, otherwise generating a leading vehicle mode entry instruction; S37. Determine whether the current CACC vehicle and the vehicle ahead are traveling in the same direction. If so, generate a following vehicle mode entry instruction; otherwise, generate a leading vehicle mode entry instruction.

5. A CACC simulation platform for urban scenarios according to claim 4, characterized in that: The control model of the CACC vehicle in human driving mode is the Wiedemann 99 model.

6. A CACC simulation platform for urban scenarios according to claim 1, characterized in that: When the CACC vehicle is in the signal light leading vehicle mode or the conflict leading vehicle mode, the controller generates a vehicle control instruction for controlling the CACC vehicle to decelerate, and the specific process includes: Assuming that there is a stationary virtual car at the stop line, the IDM model is used to control the CACC vehicle to follow the virtual car to achieve deceleration and stop.

7. A CACC simulation platform for urban scenarios according to claim 6, characterized in that: The calculation formula of the CACC vehicle's own vehicle acceleration during the deceleration process is: If the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula for the vehicle acceleration is: Among them, a ACC is the vehicle acceleration, a free is the expected acceleration, s * is the expected distance, a is a constant, s is the actual distance between the CACC vehicle and the stop line, δ is the speed coefficient, v0 is the expected speed, and v is the current vehicle speed; If the current speed of the CACC vehicle is greater than the expected speed, the calculation formula of the vehicle acceleration is: Where b is a constant; The calculation formula of the expected distance is: Among them, s0 is the static safety distance and T is the safety time distance.

8. A CACC simulation platform for urban scenarios according to claim 7, characterized in that: The actuator executes the vehicle control instruction through the vehicle dynamics model and obtains the actual acceleration.

9. The CACC simulation platform for urban scenarios according to claim 1, characterized in that: The control model of a human driving a vehicle is the Wiedemann 99 model.

Images