A traffic flow simulation method and related equipment

Abstract

The present invention provides a traffic flow simulation method and related equipment. During the traffic flow simulation process, the motion state of a simulated vehicle in the next simulation step is adjusted in real time through a scene state quantity, and the motion state of the vehicle in the next simulation step is corrected based on vehicle control parameters and preset vehicle motion state restriction conditions, so that the motion state of the vehicle is more consistent with the actual vehicle, thereby improving the reliability of the traffic flow simulation result.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving test technology, and in particular to a traffic flow simulation method and related equipment. Background Art

[0002] Traffic flow simulation refers to the use of simulation technology to study traffic behavior. It is a technology that tracks and describes the changes in traffic movement over time and space.

[0003] Existing traffic flow simulation systems generally use regional travel demand information to perform spatiotemporal distribution of traffic flow between different parts of the region, and then simulate the traffic flow on each road in the region at each time, and make each vehicle in the above traffic flow run from the starting point to the end point according to some simplified kinematic rules, and meet the settings of quantity and duration.

[0004] First of all, the existing traffic flow simulation system focuses on the simulation of macro traffic laws, which is mainly used to study indicators such as regional traffic efficiency, parking time, average vehicle speed, traffic density and average energy consumption. The vehicle movement rules it sets are just to achieve the required longitudinal speed of the vehicle through a simple constant acceleration or deceleration process, and the lateral speed also completes the lateral displacement process evenly with a fixed lane change time.

[0005] The applicant has discovered through research that the existing traffic flow simulation system simplifies vehicle movement, resulting in the inability of the existing simulation system to accurately reflect the continuous movement of vehicles during the simulation process, which in turn leads to certain deviations in its evaluation indicators for regional traffic and is unable to relatively accurately reflect the actual situation.

[0006] Therefore, how to simulate the real situation of the vehicle has become one of the technical problems that technical personnel in this field need to solve urgently. Summary of the invention

[0007] In view of this, an embodiment of the present invention provides a traffic flow simulation method and related devices to improve the reliability of the simulation results of traffic flow simulation.

[0008] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0009] A traffic flow simulation method, comprising:

[0010] Acquire a scene state quantity of a current simulation step corresponding to the target driver model, wherein the scene state quantity includes: vehicle state information and traffic state information of the current simulation step;

[0011] Loading the scenario state quantity into the target driver model, obtaining the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step length calculated by the target driver model based on the scenario state quantity, wherein the target driver model calculates the expected longitudinal speed and expected lateral displacement to be achieved by the vehicle at the next moment based on required parameters, and the required parameters include the current speed of the vehicle, the lane it is in, and the distance to the vehicle in front;

[0012] Calculate the expected lateral velocity corresponding to the expected lateral displacement based on the time interval between two adjacent simulation steps and the expected lateral displacement;

[0013] Calculating an estimated longitudinal speed and an estimated lateral speed for the next simulation step corresponding to the expected longitudinal speed and the expected lateral displacement based on vehicle control parameters, wherein the control parameters at least include a maximum accelerator pedal opening and a maximum brake pedal opening in a safe state;

[0014] Correcting the expected longitudinal speed and the expected lateral speed based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain the actual longitudinal speed and the actual lateral speed for the next simulation step;

[0015] The true longitudinal speed and the true lateral speed are updated into the vehicle state information as the vehicle state of the next simulation step corresponding to the driver model, and the scene state quantity is updated.

[0016] A traffic flow simulation device, comprising:

[0017] A scene information acquisition unit, used to acquire a scene state quantity of a current simulation step length corresponding to a target driver model, wherein the scene state quantity includes: vehicle state information and traffic state information of the current simulation step length;

[0018] an expected driving state calculation unit, used for loading the scene state quantity into the target driver model, obtaining the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step calculated by the target driver model based on the scene state quantity, and calculating the expected lateral speed corresponding to the expected lateral displacement based on the time interval between two adjacent simulation steps and the expected lateral displacement; wherein the target driver model calculates the expected longitudinal speed and expected lateral displacement to be achieved by the vehicle at the next moment based on required parameters, and the required parameters include the current speed of the vehicle, the lane it is in, and the distance to the vehicle in front;

[0019] an actual driving state estimation unit, configured to calculate an estimated longitudinal speed and an estimated lateral speed for a next simulation step corresponding to the expected longitudinal speed and the expected lateral displacement based on vehicle control parameters, wherein the control parameters at least include a maximum accelerator pedal opening and a maximum brake pedal opening in a safe state;

[0020] a correction unit, configured to correct the expected longitudinal speed and the expected lateral speed based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain the actual longitudinal speed and the actual lateral speed for the next simulation step;

[0021] The scene updating unit is used to update the real longitudinal speed and the real lateral speed as the vehicle state of the next simulation step corresponding to the driver model into the vehicle state information, and update the scene state quantity.

[0022] A traffic flow simulation device, comprising:

[0023] A memory and a processor; the memory stores a program suitable for execution by the processor, and the program is used to execute each step of any one of the above-mentioned traffic flow simulation methods.

[0024] Based on the above technical scheme, the above scheme provided by the embodiment of the present invention obtains the scene state quantity of the current simulation step in the process of traffic flow simulation, and then calculates the expected longitudinal speed and expected lateral speed of the next simulation step based on the scene state quantity, and then calculates the estimated longitudinal speed and estimated lateral speed of the next simulation step corresponding to the expected longitudinal speed and expected lateral speed based on the vehicle control parameters, corrects the expected longitudinal speed and expected lateral speed with the estimated longitudinal speed and estimated lateral speed and the preset vehicle motion state restriction conditions to obtain the real longitudinal speed and real lateral speed of the next simulation step, updates the real longitudinal speed and real lateral speed as the vehicle state of the next simulation step corresponding to the driver model to the vehicle state information, and updates the scene state quantity. This scheme adjusts the motion state of the simulated vehicle in the next simulation step in real time through the scene state quantity, and corrects the motion state of the vehicle for the next simulation step based on the vehicle control parameters and the preset vehicle motion state restriction conditions, so that the motion state of the vehicle is more consistent with the actual vehicle, thereby improving the reliability of the traffic flow simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0026] Figure 1 A flow chart of a traffic flow simulation method disclosed in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of a traffic flow simulation device disclosed in an embodiment of the present application;

[0028] Figure 3 This is a schematic diagram of the structure of the traffic flow simulation device disclosed in the embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] In order to simulate the actual situation of the vehicle, the present application obtains the scene state quantity of each simulation step of the vehicle, calculates the expected state of the vehicle for the next simulation step based on the scene state quantity, and then controls the driving model based on the expected state of the next simulation step and the estimated state of the vehicle for the next simulation step by the vehicle control parameters. Finally, the expected state is corrected using the estimated state and preset vehicle motion state constraints to obtain the corrected real state of the vehicle in the next simulation step, and the scene state quantity of the vehicle for the next simulation step is updated based on the real state.

[0031] See also Figure 1 The traffic flow simulation method disclosed in the embodiment of the present application may include: steps S101-S106.

[0032] Step S101: Obtain the scene state quantity of the current simulation step corresponding to the target driver model.

[0033] In this step, the running state of the vehicle is controlled by the target driver model. Each target driver model can control one vehicle or multiple vehicles at the same time. Each vehicle corresponds to a scene state quantity with its own simulation step, and each simulation step corresponds to a group of scene state quantities. The scene state quantities are obtained by the SUMO simulation scene definition. The scene state quantities are used to characterize the vehicle state information of the vehicle, as well as the simulated position of the vehicle and the traffic state information of the related environment. The specific contents of the vehicle state information and the traffic state information can be set based on user needs. For example, the vehicle state information may include: the vehicle speed, the lane position, and the distance between the vehicle and the vehicle in front. The traffic state information may include the road speed limit of the simulated road where the vehicle is located, the distance between the vehicle's position and the intersection ahead, the status of the traffic lights at the intersection ahead, and other information.

[0034] Step S102: loading the scenario state quantity into the target driver model, and obtaining the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step length calculated by the target driver model based on the scenario state quantity.

[0035] The expected longitudinal speed may be the longitudinal speed of the vehicle, and the lateral displacement refers to the distance that the vehicle is expected to displace to the left or right relative to the vehicle's driving direction at the next moment, for example, the lateral displacement generated when controlling the vehicle to change lanes. Here, both lateral and longitudinal refer to the vehicle.

[0036] In this solution, the target driver model is used to obtain the required parameters from the scene state quantity, and calculate the longitudinal speed and lateral displacement that the vehicle wants to achieve at the next moment based on the required parameters. The required parameters may refer to the current speed of the vehicle, the lane it is in, and the distance from the vehicle in front. Based on these parameters, the target driver model can calculate the expected state of the vehicle expected at the next moment through an analysis algorithm, and the expected state may include the expected longitudinal speed and the expected lateral displacement. Wherein, when the target driver model can calculate the expected state of the vehicle expected at the next moment through an internal intelligent analysis algorithm, it can judge whether the vehicle has an acceleration demand based on the distance between the vehicle and the vehicle in front, judge whether the vehicle has a lane change demand by judging the distance between the vehicle and the vehicle in front and the current speed of the vehicle, judge whether the vehicle is speeding or whether lane change is allowed based on the road where the vehicle is located, etc., and calculate the expected state of the vehicle expected at the next moment based on these judgment results. For example, when it is detected that the distance between the vehicle and the vehicle in front is too large, it indicates that the vehicle has an acceleration demand. If it is detected that the distance between the vehicle and the vehicle in front is too close, and the current speed of the vehicle is too low, and the lane in which the vehicle is located allows lane change, it indicates that the vehicle has a lane change demand.

[0037] In the present solution, the type of the target driver model can be selected based on user needs. For operational efficiency considerations, the target driver model can be a driver model based on the open source simulation software SUMO. Since the program code in the driver model and the SUMO software body are compiled together and share the internal variables and functional functions of the software, no external communication or interface calling means are required. Therefore, the driver model based on the open source simulation software SUMO has high operational efficiency.

[0038] Step S103: Calculate an expected lateral velocity corresponding to the expected lateral displacement based on the time interval between two adjacent simulation steps and the expected lateral displacement.

[0039] In this step, when calculating the expected lateral speed, the calculation can be based on the time difference between the two simulation steps and the expected lateral displacement. The time difference between the two simulation steps is used as the driving time, and the expected lateral displacement is used as the driving distance. The driving speed is calculated based on the relationship between the driving time and the driving distance, and this speed is the expected lateral speed.

[0040] Step S104: Calculating an estimated longitudinal speed and an estimated lateral speed of the next simulation step corresponding to the expected longitudinal speed and the expected lateral displacement based on the vehicle control parameters.

[0041] In this step, the control parameters include at least the maximum accelerator pedal opening and the maximum brake pedal opening in a safe state;

[0042] In this step, different control parameters will be configured for different vehicles. The control parameters may include: maximum accelerator pedal opening, maximum brake pedal opening and other data under safe conditions. When the expected state of the vehicle at the next moment is obtained, it is necessary to calculate the estimated longitudinal speed and estimated lateral speed of the next simulation step corresponding to the expected longitudinal speed and expected lateral speed based on these control parameters.

[0043] Specifically, before calculating the estimated longitudinal speed and the estimated lateral speed, it is necessary to calculate the vehicle control parameters required to achieve the expected longitudinal speed and the expected lateral speed in the next simulation step. In this scheme, the vehicle control parameters include accelerator pedal opening, brake pedal opening, and angle control parameters. After calculating the required accelerator pedal opening or brake pedal opening and angle control parameters, the angle control parameter can be considered as the steering angle of the vehicle. Then, based on the required accelerator pedal opening, brake pedal opening and angle control parameters, the estimated longitudinal speed and estimated lateral speed that can be achieved by the vehicle in the next simulation step are calculated. For example, the current longitudinal speed of the vehicle is a, and the lateral speed is b. The expected longitudinal speed of the next simulation step is A, and the expected lateral speed is B. If the longitudinal speed of the vehicle is to be switched from a to A in the next simulation step, and the lateral speed of the vehicle is to be switched from b to B in the next simulation step, the corresponding accelerator pedal opening, brake pedal opening and angle control parameters are the above-mentioned vehicle control parameters.

[0044] In the technical solution disclosed in the embodiment of the present application, in order to ensure the safe driving of the vehicle, in this solution, it is necessary to set some vehicle control parameter restriction conditions, and the control parameters are restricted by these control parameter restriction conditions to ensure the safe driving of the vehicle. Therefore, after obtaining the control parameters (accelerator pedal opening or brake pedal opening) required for the desired longitudinal speed and desired lateral displacement to be achieved by the vehicle, it is necessary to compare these control parameters with preset safety parameters (such as the maximum change amount of the accelerator pedal opening and the maximum change amount of the brake pedal opening), and use the safety parameters as the restriction conditions of the vehicle control parameters to correct the calculated vehicle control parameters to obtain the corrected vehicle control parameters, and then based on the current vehicle state and the corrected vehicle control parameters, calculate the estimated longitudinal speed and estimated lateral speed that the vehicle can reach in the next simulation step. During the correction process, if the safety parameter is less than the required vehicle control parameter, the safety parameter is used as the vehicle control parameter, otherwise the required vehicle control parameter is kept unchanged.

[0045] Step S105: Correcting the expected longitudinal speed and expected lateral displacement based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain a corrected true longitudinal speed and true lateral speed for the next simulation step.

[0046] In this step, different scenarios may have different restriction conditions, for example, the vehicle is located at a solid line position and lane change is not allowed, the speed limit of the vehicle is N, the distance between the vehicle and the rear vehicle is too close, lane change is not allowed, etc. These restriction conditions can be directly obtained from the scene state quantity or further calculated. Of course, the restriction conditions can also be directly used as preset data and directly called during the simulation process.

[0047] After obtaining the vehicle motion state constraints and the estimated longitudinal speed and the estimated lateral speed, these data are used as constraints to correct the expected longitudinal speed and the expected lateral speed, and the corrected expected longitudinal speed and the expected lateral speed are used as the actual longitudinal speed and the actual lateral speed of the vehicle in the next simulation step.

[0048] Specifically, in this step, the expected lateral speed is corrected based on the estimated lateral speed and the vehicle motion state constraint, including: obtaining the lateral speed defined in the vehicle motion state constraint; determining the minimum lateral speed among the estimated lateral speed, the expected lateral speed and the lateral speed defined in the vehicle motion state constraint; marking the minimum lateral speed as the corrected expected lateral speed. The expected longitudinal speed is corrected based on the estimated longitudinal speed and the vehicle motion state constraint, including: determining the minimum longitudinal speed among the estimated longitudinal speed, the expected longitudinal speed and the longitudinal speed defined in the vehicle motion state constraint, and using the minimum longitudinal speed as the corrected expected longitudinal speed. In the technical solution disclosed in the embodiment of the present application, when calculating the estimated lateral speed, the steering angle of the vehicle needs to be involved. The greater the longitudinal speed of the vehicle, the greater the steering angle, the greater the lateral speed of the vehicle. Therefore, on the basis of determining the longitudinal speed of the vehicle, the vehicle can reach the required lateral speed by setting the adapted steering angle. In this solution, since the lateral speed is generally generated by the vehicle changing lanes during the vehicle driving process, its speed is generally small, and its influence on the longitudinal speed of the vehicle can be ignored. During vehicle driving, if the vehicle steering angle is too large, it will affect the safe driving of the vehicle, for example, causing the vehicle to roll over, etc. Therefore, in this solution, a safe steering angle of the vehicle is also set.

[0049] At this time, the method marks the minimum lateral speed as the corrected expected lateral speed, which specifically includes:

[0050] Determine the minimum longitudinal speed among the estimated longitudinal speed, the expected longitudinal speed and the longitudinal speed defined in the vehicle motion state constraint, and use the minimum longitudinal speed as the real longitudinal speed for the next simulation step; calculate the angle between the vector generated by the minimum lateral speed and the real longitudinal speed and the longitudinal direction of the vehicle; if the angle is not greater than the preset safe steering angle, mark the minimum lateral speed as the corrected expected lateral speed; if the angle is greater than the preset safe steering angle, mark the lateral speed corresponding to the safe steering angle as the corrected expected lateral speed, thereby ensuring the safe driving of the vehicle. Step S106: Update the real longitudinal speed and the real lateral speed as the vehicle state for the next simulation step corresponding to the driver model to the vehicle state information, and update the scene state quantity.

[0051] In this step, when the true longitudinal speed and true lateral speed of the next simulation step are calculated, the position of the vehicle on the simulated road, the distance from the vehicle in front, the state of the traffic lights at the intersection, etc. will change. At this time, it is necessary to update the scene state quantity corresponding to the vehicle of the next step based on the true longitudinal speed and true lateral speed, thereby achieving accurate simulation of the vehicle driving state and making the traffic flow simulation more reliable.

[0052] The purpose of traffic flow simulation is to test the vehicle speed of a certain road section within a certain period of time. Therefore, in this solution, after obtaining the traffic state information of each simulated vehicle corresponding to the target driver model at each simulation step, the average speed of the vehicle during the simulation process can be calculated based on the scene state quantity. For example, the average driving speed of the vehicle during the simulation process and the time taken by the vehicle to pass the road can be calculated based on parameters such as the vehicle speed of the simulated vehicle at each simulation step in the scene state quantity, the distance between the vehicle and the intersection ahead, etc.

[0053] Corresponding to the above method, this embodiment discloses a traffic flow simulation device. For the specific working contents of each unit in the device, please refer to the contents of the above method embodiment.

[0054] The traffic flow simulation device provided by an embodiment of the present invention is described below. The traffic flow simulation device described below and the traffic flow simulation method described above can be referred to each other.

[0055] See also Figure 2 The traffic flow simulation device disclosed in the embodiment of the present application includes:

[0056] A scene information acquisition unit A, which corresponds to step S101 in the above method, is used to obtain the scene state quantity of the current simulation step corresponding to the target driver model, and the scene state quantity includes: vehicle state information and traffic state information of the current simulation step;

[0057] The expected driving state calculation unit B corresponds to step S102 in the above method and is used to

[0058] The scene state quantity is loaded into the target driver model, and the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step calculated by the target driver model based on the scene state quantity are obtained, wherein the target driver model calculates the expected longitudinal speed and expected lateral displacement to be achieved by the vehicle at the next moment based on required parameters, and the required parameters include the current speed of the vehicle, the lane it is in, and the distance to the vehicle in front; and the expected lateral speed corresponding to the expected lateral displacement is calculated based on the time interval between two adjacent simulation steps and the expected lateral displacement;

[0059] an actual driving state estimation unit C, which corresponds to step S103 in the above method, and is used to calculate an estimated longitudinal speed and an estimated lateral speed for the next simulation step corresponding to the expected longitudinal speed and the expected lateral displacement based on vehicle control parameters, wherein the control parameters at least include a maximum accelerator pedal opening and a maximum brake pedal opening in a safe state;

[0060] A correction unit D, corresponding to step S104 in the above method, is used to correct the expected longitudinal speed and the expected lateral speed based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain the actual longitudinal speed and the actual lateral speed for the next simulation step;

[0061] The scene updating unit E, which corresponds to step S105 in the above method, is used to update the real longitudinal speed and the real lateral speed as the vehicle state of the next simulation step corresponding to the driver model into the vehicle state information, and update the scene state quantity.

[0062] Corresponding to the above method, the actual driving state estimation unit is specifically used for:

[0063] Based on the current vehicle state, calculating the vehicle control parameters required to achieve the desired longitudinal speed and the desired lateral displacement;

[0064] Based on the current vehicle state and the vehicle control parameters, an estimated longitudinal speed and an estimated lateral speed that can be achieved by the target vehicle model in the next simulation step are calculated.

[0065] Corresponding to the above method, when the control parameters include the maximum accelerator pedal opening and the maximum brake pedal opening in the safety state, the actual driving state estimation unit is further used to calculate the estimated longitudinal speed and the estimated lateral speed:

[0066] The vehicle control parameters are corrected using the maximum accelerator pedal opening and the maximum brake pedal opening in the safety state, and an estimated longitudinal speed and an estimated lateral speed that can be achieved by the target vehicle model in the next simulation step are calculated based on the current vehicle state and the corrected vehicle control parameters.

[0067] Corresponding to the above method, when the actual driving state estimation unit uses the maximum accelerator pedal opening and the maximum brake pedal opening in the safety state to correct the vehicle control parameters, it is specifically used to: when the accelerator pedal opening in the vehicle control parameters meets the condition that it is greater than the maximum accelerator pedal opening in the safety state, use the maximum accelerator pedal opening in the safety state as the accelerator pedal opening in the vehicle control parameters of the vehicle; when the accelerator pedal opening in the vehicle control parameters does not meet the condition that it is greater than the maximum accelerator pedal opening in the safety state, keep the accelerator pedal opening in the vehicle control parameters unchanged;

[0068] When the brake pedal opening in the vehicle control parameters meets the condition that it is greater than the maximum brake pedal opening in the safety state, the maximum brake pedal opening in the safety state is used as the brake pedal opening in the vehicle control parameters of the vehicle; when the brake pedal opening in the vehicle control parameters does not meet the condition that it is greater than the maximum brake pedal opening in the safety state, the brake pedal opening in the vehicle control parameters is kept unchanged.

[0069] Corresponding to the above method, when the correction unit corrects the expected lateral speed based on the estimated lateral speed and the vehicle motion state constraint, it is specifically used to: obtain the lateral speed specified in the vehicle motion state constraint; determine the minimum lateral speed among the estimated lateral speed, the expected lateral speed and the lateral speed specified in the vehicle motion state constraint; and mark the minimum lateral speed as the corrected expected lateral speed.

[0070] When the correction unit marks the minimum lateral speed as the corrected expected lateral speed, the correction unit is specifically configured to:

[0071] Determine the minimum longitudinal speed among the estimated longitudinal speed, the expected longitudinal speed and the longitudinal speed defined in the vehicle motion state constraint condition, and use the minimum longitudinal speed as the real longitudinal speed of the next simulation step; calculate the angle between the vector generated by the minimum lateral speed and the real longitudinal speed and the longitudinal direction of the vehicle; determine whether the angle is greater than a preset safe steering angle; if it is not greater than the preset safe steering angle, mark the minimum lateral speed as the corrected expected lateral speed; if it is greater than the preset safe steering angle, mark the lateral speed corresponding to the safe steering angle as the corrected expected lateral speed.

[0072] Corresponding to the above method, the above device may also include a speed calculation unit, which is used to calculate the average driving speed of the vehicle during the simulation process, and the time taken by the vehicle to pass the simulated road based on parameters such as the vehicle speed of the vehicle at each simulation step in the scene state quantity, the distance between the vehicle and the intersection ahead, etc.

[0073] Corresponding to the above method, the present application also discloses a traffic flow simulation device, Figure 3 For a hardware structure diagram of the traffic flow simulation device provided by an embodiment of the present invention, see Figure 3 As shown, it may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0074] In the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 communicate with each other through the communication bus 400; obviously, Figure 3 The communication connections shown for the processor 100, the communication interface 200, the memory 300, and the communication bus 400 are merely optional;

[0075] Optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module;

[0076] The processor 100 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0077] The memory 300 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0078] The processor 100 is specifically used to execute the specific processes disclosed in the above-mentioned traffic flow simulation method embodiments of this application.

[0079] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0081] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0083] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0084] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A traffic flow simulation method, It is characterized in that include: Acquire a scene state quantity of a current simulation step corresponding to the target driver model, wherein the scene state quantity includes: vehicle state information and traffic state information of the current simulation step; Loading the scenario state quantity into the target driver model, obtaining the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step length calculated by the target driver model based on the scenario state quantity, wherein the target driver model calculates the expected longitudinal speed and expected lateral displacement to be achieved by the vehicle at the next moment based on required parameters, and the required parameters include the current speed of the vehicle, the lane it is in, and the distance to the vehicle in front; Calculate the expected lateral velocity corresponding to the expected lateral displacement based on the time interval between two adjacent simulation steps and the expected lateral displacement; Based on the current vehicle state, the vehicle control parameters required to achieve the desired longitudinal speed and the desired lateral displacement are calculated; based on the current vehicle state and the vehicle control parameters, the estimated longitudinal speed and the estimated lateral speed that can be achieved by the target driver model in the next simulation step are calculated, wherein the control parameters at least include the maximum accelerator pedal opening and the maximum brake pedal opening in a safe state; Correcting the expected longitudinal speed and the expected lateral speed based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain the actual longitudinal speed and the actual lateral speed for the next simulation step; The real longitudinal speed and the real lateral speed are updated into the vehicle state information as the vehicle state of the next simulation step corresponding to the driver model, and the scene state quantity is updated; The method of correcting the expected lateral speed based on the estimated lateral speed and the vehicle motion state constraint condition includes: Acquiring the lateral speed defined in the vehicle motion state constraint; determining the minimum lateral speed among the estimated lateral speed, the desired lateral speed, and the lateral speed defined in the vehicle motion state constraint; marking the minimum lateral speed as the corrected desired lateral speed; Correcting the expected longitudinal speed based on the estimated longitudinal speed and the vehicle motion state constraint condition includes: The minimum longitudinal speed among the estimated longitudinal speed, the expected longitudinal speed and the longitudinal speed defined in the vehicle motion state restriction condition is determined, and the minimum longitudinal speed is used as the corrected expected longitudinal speed.

2. The traffic flow simulation method according to claim 1, It is characterized in that In the case where the control parameters include a maximum accelerator pedal opening and a maximum brake pedal opening in a safe state, the method further includes: The vehicle control parameters are corrected using the maximum accelerator pedal opening and the maximum brake pedal opening in the safety state, and an estimated longitudinal speed and an estimated lateral speed that can be achieved by the target vehicle model in the next simulation step are calculated based on the current vehicle state and the corrected vehicle control parameters.

3. The traffic flow simulation method according to claim 2, It is characterized in that The vehicle control parameters are modified using the maximum accelerator pedal opening and the maximum brake pedal opening in the safety state, including: When the accelerator pedal opening in the vehicle control parameters satisfies the condition that it is greater than the maximum accelerator pedal opening in the safety state, the maximum accelerator pedal opening in the safety state is used as the accelerator pedal opening in the vehicle control parameters of the vehicle; when the accelerator pedal opening in the vehicle control parameters does not satisfy the condition that it is greater than the maximum accelerator pedal opening in the safety state, the accelerator pedal opening in the vehicle control parameters is kept unchanged; When the brake pedal opening in the vehicle control parameters meets the condition that it is greater than the maximum brake pedal opening in the safety state, the maximum brake pedal opening in the safety state is used as the brake pedal opening in the vehicle control parameters of the vehicle; when the brake pedal opening in the vehicle control parameters does not meet the condition that it is greater than the maximum brake pedal opening in the safety state, the brake pedal opening in the vehicle control parameters is kept unchanged.

4. The traffic flow simulation method according to claim 1, It is characterized in that The vehicle status information includes the vehicle speed, the lane position of the vehicle, and the distance between the vehicle and the preceding vehicle. The traffic status information includes: road speed limit, distance between the vehicle and the intersection ahead, and status of the traffic light ahead.

5. The traffic flow simulation method according to claim 1, It is characterized in that Marking the minimum lateral velocity as a corrected desired lateral velocity comprises: Determine the minimum longitudinal speed among the estimated longitudinal speed, the expected longitudinal speed and the longitudinal speed defined in the vehicle motion state constraint condition, and use the minimum longitudinal speed as the actual longitudinal speed for the next simulation step; Calculating an angle between a vector generated by the minimum lateral velocity and the true longitudinal velocity and a longitudinal direction of the vehicle; When the angle is not greater than a preset safe steering angle, marking the minimum lateral speed as a corrected expected lateral speed; When the included angle is greater than a preset safe steering angle, the lateral speed corresponding to the safe steering angle is marked as the corrected expected lateral speed.

6. The traffic flow simulation method according to claim 4, It is characterized in that Also includes: The average driving speed of the vehicle during the simulation process and the time taken by the vehicle to pass the simulated road are calculated based on parameters such as the vehicle speed at each simulation step in the scene state quantity, the distance between the vehicle and the front intersection, etc.

7. A traffic flow simulation device, It is characterized in that include: A scene information acquisition unit, used to acquire a scene state quantity of a current simulation step corresponding to a target driver model, wherein the scene state quantity includes: vehicle state information and traffic state information of the current simulation step; an expected driving state calculation unit, used for loading the scene state quantity into the target driver model, obtaining the expected longitudinal speed and expected lateral displacement corresponding to the next simulation step calculated by the target driver model based on the scene state quantity, and calculating the expected lateral speed corresponding to the expected lateral displacement based on the time interval between two adjacent simulation steps and the expected lateral displacement; wherein the target driver model calculates the expected longitudinal speed and expected lateral displacement to be achieved by the vehicle at the next moment based on required parameters, and the required parameters include the current speed of the vehicle, the lane it is in, and the distance to the vehicle in front; an actual driving state estimation unit, configured to calculate the vehicle control parameters required to achieve the desired longitudinal speed and the desired lateral displacement based on the current vehicle state; and to calculate the estimated longitudinal speed and the estimated lateral speed that can be achieved by the target driver model in the next simulation step based on the current vehicle state and the vehicle control parameters, wherein the control parameters at least include the maximum accelerator pedal opening and the maximum brake pedal opening in a safe state; a correction unit, configured to correct the expected longitudinal speed and the expected lateral speed based on the estimated longitudinal speed, the estimated lateral speed and the vehicle motion state constraint condition to obtain the actual longitudinal speed and the actual lateral speed for the next simulation step; A scene updating unit, used for updating the real longitudinal speed and the real lateral speed as the vehicle state of the next simulation step corresponding to the driver model into the vehicle state information, and updating the scene state quantity; The correction unit corrects the expected lateral speed based on the estimated lateral speed and the vehicle motion state constraint condition, including: The correction unit obtains the lateral speed defined in the vehicle motion state constraint; determines the minimum lateral speed among the estimated lateral speed, the desired lateral speed and the lateral speed defined in the vehicle motion state constraint; and marks the minimum lateral speed as the corrected desired lateral speed; The correction unit corrects the expected longitudinal speed based on the estimated longitudinal speed and the vehicle motion state restriction condition, including: The correction unit determines a minimum longitudinal speed among the estimated longitudinal speed, the desired longitudinal speed, and the longitudinal speed defined in the vehicle motion state restriction condition, and uses the minimum longitudinal speed as the corrected desired longitudinal speed.

8. A traffic flow simulation device, It is characterized in that include: Memory and processor; The memory stores a program suitable for execution by the processor, and the program is used to execute each step of the traffic flow simulation method according to any one of claims 1 to 6.

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