CN114578711A - CACC simulation platform for urban scene - Google Patents
CACC simulation platform for urban scene Download PDFInfo
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Abstract
The invention relates to a CACC simulation platform facing to an urban scene, which comprises: the traffic generation module is used for randomly generating a road network containing urban scenes and traffic flow on the road network, wherein the traffic flow comprises CACC vehicles and human-driven vehicles; the traffic control module is used for generating traffic information of urban scenes, and the traffic information of each urban scene comprises one or more of signal lamps, conflict areas and priority rules; the vehicle control module comprises a decision maker, a controller and an actuator, wherein the decision maker is used for generating a mode switching instruction of the CACC vehicle according to road network information and a 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 run according to the vehicle control instruction. Compared with the prior art, the invention realizes CACC simulation of urban scenes and has the advantages of a vehicle simulation platform and a traffic simulation platform.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to a CACC simulation platform for urban scenes.
Background
CACC (Cooperative Adaptive Cruise Control) is a key technology for relieving urban congestion. The traffic information system is promoted from the ACC technology, more traffic information can be obtained from time and space dimensions, the perception and decision-making capability of a single vehicle is improved, conditions can be provided for collaborative perception and decision-making of vehicle groups, and even information sharing of the whole traffic information system is realized. The CACC vehicles run on the road in a formation mode, the front vehicles can transmit information such as position and speed to the CACC vehicles, and then the small time interval between the front vehicles is kept to stably advance through an algorithm.
The evaluation of the CACC effect in urban scenes is very important. Many existing CACC studies are directed to highways, demonstrating the benefits of CACCs in this scenario. However, in urban road scenes under the background of mixed traffic, the safety, efficiency and environmental protection of the CACC are still not widely and effectively verified, thereby limiting the application of CACC technology.
The evaluation of the CACC has two modes of real vehicle test and simulation test. Because real-vehicle testing has the disadvantages of high cost, low efficiency, and limited scenarios, simulation testing is necessary. In order to more efficiently conduct large-scale tests to evaluate CACCs, a simulation platform is necessary. The existing simulation platform ignores the interaction between human driving vehicles and CACC vehicles, and the evaluation capability of the human driving vehicles and the CACC vehicles on intersection scenes is limited. The current simulation platform mainly comprises an automobile and a traffic, and the automobile simulation platform is provided with a real vehicle dynamics model and a controller and can simulate an intelligent single-vehicle behavior more truly. However, the traffic events and the behaviors of the traffic participants in the simulation scene are preset, which causes unrealistic simulation scenes, especially in an intersection which is an environment with frequent interaction events. Another type of simulation platform is traffic simulation platforms, which, although they may enable large-scale mixed traffic flow simulation, lack randomness in the interaction between vehicles due to the lack of a real autonomous driving system.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a CACC simulation platform for an urban scene, realizes CACC simulation of the urban scene, and has the advantages of a vehicle simulation platform and a traffic simulation platform.
The purpose of the invention can be realized by the following technical scheme:
a CACC simulation platform facing city scenes comprises:
the traffic generation module is used for randomly generating a road network containing urban scenes and traffic flows on the road network, wherein the traffic flows comprise CACC vehicles and human-driven vehicles;
the traffic control module is used for generating traffic information of urban scenes, and the traffic information of each urban scene comprises one or more of signal lamps, conflict areas and priority rules;
the vehicle control module comprises a decision maker, a controller and an actuator, wherein the decision maker is used for generating a mode switching instruction of the CACC vehicle according to road network information and a traffic control instruction, the controller switches a 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 run according to the vehicle control instruction;
the CACC vehicle mode comprises a CACC mode, a signal light head vehicle mode and a conflict head vehicle mode, wherein the CACC vehicle in the CACC mode runs based on the CACC model, and the CACC vehicle in the signal light head vehicle mode and the conflict head vehicle mode decelerates or stops;
the decision-making device comprises a CACC decision-making device, a signal lamp head vehicle decision-making device and a conflict head vehicle decision-making device;
the CACC decision maker is used for generating a CACC mode switching instruction;
the signal lamp head-vehicle decision maker is used for generating a signal lamp head-vehicle mode switching instruction when the CACC vehicle faces an urban scene with signal lamps;
the conflict head vehicle decision device is used for generating a conflict head vehicle mode switching instruction;
the priority of the signal lamp head-vehicle decision maker, the conflict head-vehicle decision maker and the CACC decision maker is decreased in sequence.
Further, the signal lamp starting mode switching instruction comprises a signal lamp starting mode entering instruction and a signal lamp starting mode exiting instruction;
the specific process of generating the signal lamp head vehicle mode switching instruction by the signal lamp head vehicle decision device comprises the following steps:
periodically executing a first judgment step;
the first judging step comprises the following substeps:
s11, judging whether the current CACC vehicle is in a signal light head-vehicle mode, if so, executing a substep S12, otherwise, executing a substep S13;
s12, judging whether the signal lamp is green, if so, generating a signal lamp head vehicle mode exit instruction, and if not, keeping the current CACC vehicle mode;
s13, judging whether the distance between the current CACC vehicle and the city scene is smaller than a set distance, if so, executing a step S14, otherwise, keeping the current mode of the current CACC vehicle;
s14, judging whether the current CACC vehicle turns right, if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S15;
s15, judging whether a signal lamp in front of the current CACC vehicle is a red lamp, if so, marking the current CACC vehicle, and executing a substep S18, otherwise, executing a substep S16;
s16, judging whether a front signal lamp of the current CACC vehicle is a yellow lamp, if so, executing a substep S17, and if not, keeping the current mode of the current CACC vehicle;
s17, judging whether the current CACC vehicle has enough time to enter the city scene, if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S18;
and S18, judging whether the current CACC vehicle is the vehicle closest to the signal lamp in all the marked CACC vehicles, and if so, generating a signal lamp head-vehicle mode entering instruction.
Further, the specific process of the sub-step S17 includes:
judging whether the following conditions are met:
dintersection＜v*(tremaing_yellow_light-tsafe)
wherein d isintersectionIs the distance between the CACC vehicle and the city scene, v is the current speed of the CACC vehicle, tremaing_yellow_lightFor the remaining yellow light time, tsafeTo set a safe time.
Further, the specific process of generating the conflict headway mode switching instruction by the conflict headway decision device includes:
periodically executing a second judgment step;
the second judging step includes the following substeps:
s21, judging whether a conflict event exists in the city scene, if so, executing a substep S22, otherwise, executing a substep S26;
s22, judging whether the current CACC vehicle has the highest priority, if so, executing a substep S26, otherwise, executing a substep S23;
s23, judging whether the current CACC vehicle can pass in front of the vehicle with high priority according to the time of the current CACC vehicle reaching the conflict area, if so, executing a substep S26, otherwise, executing a substep S24;
s24, searching all CACC vehicles facing the same conflict area in the conflict list;
s25, judging whether the current CACC vehicle is the nearest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction, and executing a step S27, otherwise, executing a substep S26;
s26, keeping the current mode of the current CACC vehicle;
s27, updating a conflict table;
wherein, the conflict table stores conflict events in all conflict areas.
Further, the CACC mode is divided into a head car mode, a following car mode and a human driving mode, the CACC mode switching instruction includes a head car mode entering instruction, a following car mode entering instruction and a human driving mode entering instruction, and the specific process of the CACC decision-making device generating the CACC mode switching instruction includes:
periodically executing a third judgment step;
the third judging step comprises the following substeps:
s31, judging whether the current CACC vehicle starts transverse behavior, if so, generating a human driving mode entering instruction, otherwise, executing a substep S32;
s32, judging whether the current CACC vehicle reaches the destination, if so, generating a human driving mode entering instruction, otherwise, executing a substep S33;
s33, judging whether the front vehicle of the current CACC vehicle is a following vehicle, if so, executing a substep S34, and otherwise, generating a head-vehicle mode entering instruction;
s34, judging whether the current CACC vehicle and the front vehicle are in the same vehicle fleet, if so, generating a follow-up mode entering instruction, otherwise, executing a step S35;
s35, judging whether the distance between the current CACC vehicle and the front vehicle is smaller than the communication range, if so, executing a step S36, otherwise, generating a head vehicle mode entering instruction;
s36, judging whether the total number of the vehicles in the fleet after the current CACC vehicles are combined is smaller than the maximum fleet number, if so, executing a step S37, otherwise, generating a head vehicle mode entering instruction;
and S37, judging whether the driving directions of the current CACC vehicle and the front vehicle are the same, if so, generating a following mode entering instruction, and otherwise, generating a head vehicle mode entering instruction.
Further, the control model of the CACC vehicle in human driving mode is the Wiedemann 99 model.
Further, when the CACC vehicle is in the signal light vehicle-head mode or the collision vehicle-head mode, the controller generates a vehicle control command for controlling the CACC vehicle to decelerate, and the specific process includes:
assuming that a static virtual vehicle exists at the stop line, the IDM is adopted to control the CACC vehicle to follow the virtual vehicle, so as to realize deceleration and stop.
Further, the calculation formula of the vehicle acceleration of the CACC vehicle in the deceleration process is as follows:
if the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula of the acceleration of the vehicle is as follows:
wherein, aACCAs acceleration of the vehicle, afreeTo desired acceleration, s*A is a constant, s is the actual distance between the CACC vehicle and the stop line, δ is the speed coefficient, v0Is the desired speed, 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 acceleration of the vehicle is as follows:
wherein b is a constant;
the calculation formula of the expected distance is as follows:
wherein s is0T is a safe time distance.
Further, the actuator executes a vehicle control command through a vehicle dynamic model, and obtains an actual acceleration.
Further, the control model of the human-driven vehicle is a Wiedemann 99 model.
Compared with the prior art, the invention has the following beneficial effects:
(1) the traffic control module is embedded into the CACC simulation platform, CACC simulation of urban scenes is realized, the advantages of a vehicle simulation platform and a traffic simulation platform are achieved, real vehicle microscopic interactive behaviors can be simulated, a real automatic driving system is also provided, and the simulation process is random;
(2) the invention can calculate mobility indexes such as delay, traffic capacity and the like and safety indexes such as emergency stop times and the like by collecting the CACC vehicle data in the simulation process of the CACC simulation platform and carrying out post-processing, thereby evaluating the efficiency and safety of CACC vehicles at intersections.
Drawings
FIG. 1 is a block diagram of a CACC simulation platform;
FIG. 2 is a schematic flow chart of a third determination step;
FIG. 3 is a schematic flowchart of the first determining step;
FIG. 4 is a flowchart illustrating a second determination step;
fig. 5 is a schematic diagram of a simulation visualization result of the CACC simulation platform.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
A CACC simulation platform facing an urban scene is a three-lane signalized intersection, a signal lamp is in two phases, the signal period is 100s, the yellow lamp time is 3s, a conflict area exists between a left-turning vehicle and an opposite straight-driving vehicle, and the priority of the straight-driving vehicle is higher than that of the left-turning vehicle;
as shown in fig. 1, the CACC simulation platform includes:
the traffic generation module is used for randomly generating a road network containing urban scenes and traffic flow on the road network, wherein the traffic flow comprises CACC vehicles and human-driven vehicles;
the traffic control module is used for generating traffic information of urban scenes, and the traffic information of each urban scene comprises one or more of signal lamps, conflict areas and priority rules;
the vehicle control module comprises a decision maker, a controller and an actuator, wherein the decision maker is used for generating a mode switching instruction of the CACC vehicle according to road network information and a 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 run through a vehicle dynamics model according to the vehicle control instruction;
wherein, the vehicle mode of CACC vehicle divide into:
CACC mode: vehicles in a networked fleet;
signal lamp head-vehicle mode: head vehicle of internet fleet facing to signal lamp
Conflict head-vehicle mode: the head car of the networked fleet facing the conflict area;
the CACC vehicle in the CACC mode runs based on the CACC model, and the CACC vehicle in the signal light vehicle-head mode and the conflict vehicle-head mode decelerates or stops;
the decision maker comprises a CACC decision maker, a signal lamp head-vehicle decision maker and a conflict head-vehicle decision maker;
the CACC decision device is used for generating a CACC mode switching instruction;
the signal lamp head-vehicle decision maker is used for generating a signal lamp head-vehicle mode switching instruction when the CACC vehicle faces an urban scene with signal lamps;
the conflict head vehicle decision device is used for generating a conflict head vehicle mode switching instruction;
the priority of the signal lamp head vehicle decision device, the priority of the conflict head vehicle decision device and the priority of the CACC decision device are decreased in sequence.
In order to enable the CACC vehicle to pass in the urban scene, the signal light head vehicle decision maker and the conflict head vehicle decision maker are designed in the embodiment and are respectively used for judging whether the internet automatic driving vehicle needs to enter a signal light head vehicle mode or a conflict area head vehicle mode, and each vehicle runs two decision makers at each time step.
The priority of the signal light head vehicle decision device is greater than that of the conflict area decision device, namely, the CACC vehicle uses the signal light head vehicle decision device firstly, and because signal lights are always arranged in front of the conflict area in a part of scenes to distribute the right of way, the conflict event such as a signal intersection is avoided. And the scene which cannot be processed by the signal lamp head vehicle decision maker needs to enter the conflict area decision maker.
The signal lamp starting mode switching instruction comprises a signal lamp starting mode entering instruction and a signal lamp starting mode exiting instruction;
the specific process of generating the signal lamp head vehicle mode switching instruction by the signal lamp head vehicle decision device comprises the following steps:
periodically executing a first judgment step;
as shown in fig. 3, the first determination step includes the following sub-steps:
s11, judging whether the current CACC vehicle is in a signal light head-vehicle mode, if so, executing a substep S12, otherwise, executing a substep S13;
s12, judging whether the signal lamp is green, if so, generating a signal lamp head vehicle mode exit instruction, and if not, keeping the current CACC vehicle mode;
s13, judging whether the distance between the current CACC vehicle and the city scene is less than 50m, if so, executing a substep S14, otherwise, keeping the current mode of the current CACC vehicle;
s14, judging whether the current CACC vehicle turns right (is not controlled by a signal lamp), if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S15;
s15, judging whether a front signal lamp of the current CACC vehicle is a red light (and no other vehicle exists between the red light), if so, marking the current CACC vehicle, and executing a substep S18, otherwise, executing a substep S16;
s16, judging whether a front signal lamp of the current CACC vehicle is a yellow lamp, if so, executing a substep S17, and if not, keeping the current mode of the current CACC vehicle;
s17, judging whether the current CACC vehicle has enough time to enter the city scene, if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S18;
and S18, judging whether the current CACC vehicle is the vehicle closest to the signal lamp in all the marked CACC vehicles, and if so, generating a signal lamp head-vehicle mode entering instruction.
The specific process of the sub-step S17 includes:
judging whether the following conditions are satisfied:
dintersection＜v*(tremaing_yellow_light-tsafe)
wherein d isintersectionIs the distance between the CACC vehicle and the city scene, v is the current speed of the CACC vehicle, tremaingr_yellow_lightFor the remaining yellow light time, tsafeTo set a safe time.
When the networked automatic driving vehicle faces a conflict area, whether the networked automatic driving vehicle enters a conflict mode or not can be judged according to the vehicle information and the environment information, and the specific process that the conflict head vehicle decision maker generates the conflict head vehicle mode switching instruction comprises the following steps:
periodically executing a second judgment step;
as shown in fig. 4, the second judging step includes the following substeps:
s21, judging whether a conflict event exists in the city scene according to the vehicle information and the environment information, if so, executing a substep S22, otherwise, executing a substep S26;
s22, judging whether the current CACC vehicle has the highest priority, if so, executing a substep S26, otherwise, executing a substep S23;
s23, judging whether the current CACC vehicle can pass in front of the vehicle with high priority according to the time of the current CACC vehicle reaching the conflict area, if so, executing a substep S26, otherwise, executing a substep S24;
s24, searching all CACC vehicles facing the same conflict area in the conflict list;
s25, judging whether the current CACC vehicle is the nearest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction, and executing a step S27, otherwise, executing a substep S26;
s26, keeping the current mode of the current CACC vehicle;
s27, updating a conflict table;
the conflict table stores conflict events in all conflict areas, and since there are multiple conflict areas at the same intersection, multiple conflict events may occur in each conflict area at the same time, and multiple conflict events may occur in each networked automatic driving, it is necessary to design a conflict table to record these complex information. And storing all conflict events which can occur in the conflict area in the conflict table, adding a new conflict event in real time in each period for executing the second judgment step, and deleting the conflict events in the conflict table after the conflict events disappear.
In sub-step S23, the method of calculating the time at which the current CACC vehicle arrives at the conflict area includes:
if CACC vehicle is still, the time is infinite, if the absolute value of acceleration is less than 0.2/s2If not, assuming that the vehicle moves at a constant speed and estimating the time.
The CACC modes are divided into:
a head vehicle mode: the first vehicle of the networked vehicle fleet;
following mode: other vehicles except the first vehicle in the networked fleet;
human driving mode: a CACC vehicle that is changing lanes;
the CACC mode switching instruction comprises a head car mode entering instruction, a following car mode entering instruction and a human driving mode entering instruction, and the specific process of generating the CACC mode switching instruction by the CACC decision maker comprises the following steps:
periodically executing a third judgment step;
as shown in fig. 2, the third determination step includes the following sub-steps:
s31, judging whether the current CACC vehicle starts transverse behavior, if so, generating a human driving mode entering instruction, otherwise, executing a substep S32;
s32, judging whether the current CACC vehicle reaches the destination, if so, generating a human driving mode entering instruction, otherwise, executing a substep S33;
s33, judging whether the front vehicle of the current CACC vehicle is a following vehicle, if so, executing a substep S34, and otherwise, generating a head-vehicle mode entering instruction;
s34, judging whether the current CACC vehicle and the front vehicle are in the same vehicle fleet, if so, generating a follow-up mode entering instruction, otherwise, executing a step S35;
s35, judging whether the distance between the current CACC vehicle and the front vehicle is smaller than the communication range, if so, executing a step S36, otherwise, generating a head vehicle mode entering instruction;
s36, judging whether the total number of the vehicles in the fleet after the current CACC vehicles are combined is smaller than the maximum fleet number, if so, executing a step S37, otherwise, generating a head vehicle mode entering instruction;
and S37, judging whether the driving directions of the current CACC vehicle and the front vehicle are the same, if so, generating a following mode entering instruction, and otherwise, generating a head vehicle mode entering instruction.
The controller is realized through a PID algorithm, and the control process of the controller is divided into a longitudinal control process and a transverse control process;
and (3) longitudinal control process: when the CACC vehicle is in a signal lamp head-car mode or a conflict head-car mode, the controller generates a vehicle control command for controlling the CACC vehicle to decelerate, and the specific process comprises the following steps:
assuming that a static virtual vehicle exists at the stop line, the IDM is adopted to control the CACC vehicle to follow the virtual vehicle, so as to realize deceleration and stop.
The calculation formula of the self vehicle acceleration of the CACC vehicle in the deceleration process is as follows:
if the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula of the acceleration of the vehicle is as follows:
wherein, aACCAs acceleration of the vehicle, afreeTo desired acceleration, s*In order to obtain the expected distance, a is a constant and takes a value of 1, s is the actual distance between the CACC vehicle and the stop line, and delta is a speed coefficient and can be obtained by calibrating model parameters, in the embodiment, the value is 4, v0In the embodiment, the value is 30km/h for the expected speed, and v is the current speed;
if the current speed of the CACC vehicle is greater than the expected speed, the calculation formula of the acceleration of the vehicle is as follows:
wherein b is a constant and takes the value of 1;
the desired distance is calculated as:
wherein s is0The value is 2m in this embodiment, T is the safety time interval, 2s in this embodiment,
and (3) transverse control process: the transverse behavior of the automatic driving vehicle in the simulation platform is mainly lane change, if the automatic driving vehicle is not in a target lane and is in a lane change area, the automatic driving mode is changed into a human driving mode to change lanes, and the CACC vehicle in the human driving mode and the control model of the human driving vehicle are both Wiedemann 99 models.
The actuator comprises a bottom controller and a vehicle dynamic model, the bottom controller outputs an accelerator and a brake instruction according to the expected acceleration and the current vehicle speed, and the vehicle dynamic model is used for acquiring the actual acceleration.
The simulation effect of this embodiment is as shown in fig. 5, the CACC vehicle in the signal light head car mode stops before the stop line, and the CACC vehicle in the conflict area head car mode waits for the passage of the oncoming straight car.
By collecting CACC vehicle data in the simulation process and carrying out post-processing, mobility indexes such as delay and traffic capacity and safety indexes such as emergency stop times can be calculated.
The embodiment provides a CACC simulation platform for urban scenes, CACC simulation for urban scenes is realized, the vehicle simulation platform has the advantages of both the vehicle simulation platform and the traffic simulation platform, real vehicle microscopic interaction behaviors can be simulated, a real automatic driving system is also provided, the simulation process has randomness, the efficiency and the safety of CACC vehicles at intersections can be evaluated, the CACC simulation platform can be used as a development testing tool in the CACC technology research and development process, an evaluation tool is also provided for traffic practitioners, and the research on the influence of traffic control strategies on CACC traffic flows and the influence of CACC generation under mixed traffic flows in urban roads is facilitated.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A CACC simulation platform facing city scenes is characterized by comprising:
the traffic generation module is used for randomly generating a road network containing urban scenes and traffic flows on the road network, wherein the traffic flows comprise CACC vehicles and human-driven vehicles;
the traffic control module is used for generating traffic information of urban scenes, and the traffic information of each urban scene comprises one or more of signal lamps, conflict areas and priority rules;
the vehicle control module comprises a decision maker, a controller and an actuator, wherein the decision maker is used for generating a mode switching instruction of the CACC vehicle according to road network information and a traffic control instruction, the controller switches a 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 run according to the vehicle control instruction;
the CACC vehicle mode comprises a CACC mode, a signal light head vehicle mode and a conflict head vehicle mode, wherein the CACC vehicle in the CACC mode runs based on the CACC model, and the CACC vehicle in the signal light head vehicle mode and the conflict head vehicle mode decelerates or stops;
the decision device comprises a CACC decision device, a signal light decision device and a conflict head vehicle decision device;
the CACC decision device is used for generating a CACC mode switching instruction;
the signal lamp decision maker is used for generating a signal lamp head-vehicle mode switching instruction when the CACC vehicle faces an urban scene with signal lamps;
the conflict head vehicle decision device is used for generating a conflict head vehicle mode switching instruction;
the priority of the signal lamp head vehicle decision device, the priority of the conflict head vehicle decision device and the priority of the CACC decision device are decreased in sequence.
2. The urban scene-oriented CACC simulation platform according to claim 1, wherein the signal head vehicle mode switching command comprises a signal head vehicle mode entering command and a signal head vehicle mode exiting command;
the specific process of generating the signal lamp head vehicle mode switching instruction by the signal lamp head vehicle decision device comprises the following steps:
periodically executing a first judgment step;
the first judging step comprises the following substeps:
s11, judging whether the current CACC vehicle is in a signal light head-vehicle mode, if so, executing a substep S12, otherwise, executing a substep S13;
s12, judging whether the signal lamp is green, if so, generating a signal lamp head-vehicle mode exit instruction, and if not, keeping the current CACC vehicle mode;
s13, judging whether the distance between the current CACC vehicle and the city scene is smaller than a set distance, if so, executing a substep S14, otherwise, keeping the current mode of the current CACC vehicle;
s14, judging whether the current CACC vehicle turns right, if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S15;
s15, judging whether a front signal lamp of the current CACC vehicle is a red lamp, if so, marking the current CACC vehicle, and executing a substep S18, otherwise, executing a substep S16;
s16, judging whether a front signal lamp of the current CACC vehicle is a yellow lamp, if so, executing a substep S17, and if not, keeping the current mode of the current CACC vehicle;
s17, judging whether the current CACC vehicle has enough time to enter the city scene, if so, keeping the current mode of the current CACC vehicle, otherwise, executing the substep S18;
and S18, judging whether the current CACC vehicle is the vehicle closest to the signal lamp in all the marked CACC vehicles, and if so, generating a signal lamp head-vehicle mode entering instruction.
3. The city scene-oriented CACC simulation platform according to claim 2, wherein the specific process of the substep S17 comprises:
judging whether the following conditions are met:
dintersection＜v*(tremaing_yellow_light-tsafe)
wherein d isintersectionIs the distance between the CACC vehicle and the city scene, v is the current speed of the CACC vehicle, tremaing_yellow_lightFor the remaining yellow light time, tsafeTo set a safe time.
4. The urban scene-oriented CACC simulation platform according to claim 1, wherein the specific process of the conflict head car decider generating the conflict head car mode switching instruction comprises:
periodically executing a second judgment step;
the second judging step includes the following substeps:
s21, judging whether a conflict event exists in the city scene, if so, executing a substep S22, otherwise, executing a substep S26;
s22, judging whether the current CACC vehicle has the highest priority, if so, executing a substep S26, otherwise, executing a substep S23;
s23, judging whether the current CACC vehicle can pass in front of the vehicle with high priority according to the time of the current CACC vehicle reaching the conflict area, if so, executing a substep S26, otherwise, executing a substep S24;
s24, searching all CACC vehicles facing the same conflict area in the conflict list;
s25, judging whether the current CACC vehicle is the nearest vehicle facing the same conflict area, if so, generating a conflict head vehicle mode switching instruction, and executing a step S27, otherwise, executing a substep S26;
s26, keeping the current mode of the current CACC vehicle;
s27, updating a conflict table;
wherein, the conflict table stores conflict events in all conflict areas.
5. The urban scene-oriented CACC simulation platform according to claim 1, wherein the CACC modes are divided into a head car mode, a following car mode and a human driving mode, the CACC mode switching instruction comprises a head car mode entering instruction, a following car mode entering instruction and a human driving mode entering instruction, and the specific process of the CACC decision-maker for generating the CACC mode switching instruction comprises:
periodically executing a third judgment step;
the third judging step comprises the following substeps:
s31, judging whether the current CACC vehicle starts transverse behavior, if so, generating a human driving mode entering instruction, otherwise, executing a substep S32;
s32, judging whether the current CACC vehicle reaches the destination, if so, generating a human driving mode entering instruction, otherwise, executing a substep S33;
s33, judging whether the front vehicle of the current CACC vehicle is a following vehicle, if so, executing a substep S34, and otherwise, generating a head-vehicle mode entering instruction;
s34, judging whether the current CACC vehicle and the front vehicle are in the same vehicle fleet, if so, generating a follow-up mode entering instruction, otherwise, executing a step S35;
s35, judging whether the distance between the current CACC vehicle and the front vehicle is smaller than the communication range, if so, executing a step S36, otherwise, generating a head vehicle mode entering instruction;
s36, judging whether the total number of the vehicles in the vehicle fleet after the current CACC vehicle is combined is smaller than the maximum vehicle fleet number, if so, executing a step S37, and otherwise, generating a head vehicle mode entering command;
and S37, judging whether the driving directions of the current CACC vehicle and the front vehicle are the same, if so, generating a following mode entering instruction, and otherwise, generating a head vehicle mode entering instruction.
6. The urban scene-oriented CACC simulation platform according to claim 5, wherein the control model of the CACC vehicle in human driving mode is a Wiedemann 99 model.
7. The urban scene-oriented CACC simulation platform according to claim 1, wherein when the CACC vehicle is in a signal light head-car mode or a conflict head-car mode, the controller generates a vehicle control command for controlling the CACC vehicle to decelerate, and the specific process comprises:
assuming that a static virtual vehicle exists at the stop line, the IDM is adopted to control the CACC vehicle to follow the virtual vehicle, so as to realize deceleration and stop.
8. The urban scene-oriented CACC simulation platform according to claim 7, wherein the calculation formula of the vehicle acceleration of the CACC vehicle in the deceleration process is as follows:
if the current speed of the CACC vehicle is not greater than the expected speed, the calculation formula of the acceleration of the vehicle is as follows:
wherein, aACCAs acceleration of the vehicle, afreeTo desired acceleration, s*A is a constant, s is the actual distance between the CACC vehicle and the stop line, δ is the speed coefficient, v0Is the desired speed, 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 acceleration of the vehicle is as follows:
wherein b is a constant;
the calculation formula of the expected distance is as follows:
wherein s is0T is a safe time distance.
9. The urban scene-oriented CACC simulation platform according to claim 8, wherein the actuator executes a vehicle control command through a vehicle dynamics model and obtains an actual acceleration.
10. The urban scene-oriented CACC simulation platform according to claim 1, wherein the control model of the human-driven vehicle is a Wiedemann 99 model.
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| CN117079451B (en) * | 2023-07-11 | 2024-04-19 | 清华大学 | Control method and device for mixed traffic system in urban continuous intersection scene |
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