Self-adaptive cruise simulation method and device, electronic equipment and storage medium

Abstract

The invention discloses a self-adaptive cruise simulation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a driving track for simulating a transverse driving behavior of a driver; acquiring a planning track planned and output by the vehicle ACC module; the motion state of the vehicle model is controlled to track the shape of the driving track according to the planned track, and man-machine co-driving simulation testing of the ACC function is completed, so that transverse control of a vehicle-mounted control module during ACC function testing in an existing scheme is avoided. Because the generated driving track can represent the motion state of transverse control of a driver, and the planned track planned by the ACC module can represent the longitudinal motion behavior of the vehicle, the shape of the driving track is tracked by controlling the motion state of the vehicle model according to the planned data representing the longitudinal behavior of the vehicle on the planned track. Self-consistent linkage of transverse control and longitudinal control is achieved, the driving behavior of a driver is better simulated, and then the simulation precision of the ACC function is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to an adaptive cruise simulation method, device, electronic device and storage medium. Background Art

[0002] ACC (Adaptive Cruise Control) is a car control device that controls the vehicle's braking and driving to keep the vehicle following a certain distance or maintaining a certain speed. With the gradual development of driving assistance functions, L2-level semi-automatic intelligent driving assistance devices have gradually become popular. Currently, ACC is being continuously applied to general vehicles.

[0003] During the development process, simulation testing of the ACC function is required. In the existing simulation scheme, both the lateral and longitudinal control of the vehicle are controlled by the on-board control module, which can meet the L4 level NOA (Navigation On Autopilot, automatic assisted navigation driving) function test. However, it is illogical for the ACC function test, because for vehicles using the ACC function, the driver actually controls the steering wheel of the vehicle to complete the lateral control, and the longitudinal control is completed by the ACC function.

[0004] Therefore, the existing simulation scheme is not suitable for simulation testing of ACC function. Summary of the invention

[0005] The purpose of the present application is to propose an adaptive cruise simulation method, device, electronic device and storage medium to address the deficiencies of the above-mentioned prior art, and this purpose is achieved through the following technical solutions.

[0006] The first aspect of the present application provides an adaptive cruise simulation method, the method comprising:

[0007] Obtaining a driving trajectory simulating a driver's lateral driving behavior;

[0008] Get the planned trajectory output by the vehicle ACC module;

[0009] The motion state of the vehicle model is controlled to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

[0010] A second aspect of the present application provides an adaptive cruise simulation device, the device comprising:

[0011] A driving trajectory unit, used to obtain a driving trajectory simulating a driver's lateral driving behavior;

[0012] A planning trajectory unit, used to obtain the planning trajectory output by the vehicle ACC module;

[0013] The control unit is used to control the motion state of the vehicle model to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

[0014] The third aspect of the present application proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the first aspect above.

[0015] The fourth aspect of the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the method described in the first aspect above.

[0016] Based on the adaptive cruise simulation method and device described in the first and second aspects above, the present application has at least the following beneficial effects or advantages:

[0017] By generating a driving trajectory that simulates the driver's lateral driving behavior and obtaining the planned trajectory planned by the vehicle's ACC module, the vehicle model's motion state is controlled to track the shape of the driving trajectory with the planning data representing the vehicle's longitudinal behavior on the ACC planning trajectory, and the human-machine co-driving simulation test of the ACC function is completed to avoid the lateral control of the on-board control module during the ACC function test in the existing solution. Since the generated driving trajectory can represent the driver's lateral control motion state, and the planned trajectory planned by the ACC module can represent the vehicle's longitudinal motion behavior, the vehicle model's motion state is controlled to track the shape of the driving trajectory with the planning data representing the vehicle's longitudinal behavior on the planning trajectory, so as to achieve self-consistent linkage between lateral control and longitudinal control, better simulate the driver's driving behavior, and thus improve the simulation accuracy of the ACC function.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 The present invention is a flow chart of an embodiment of an adaptive cruise simulation method according to an exemplary embodiment;

[0021] Figure 2A control schematic diagram for realizing ACC simulation according to an exemplary embodiment is shown;

[0022] Figure 3 The following is a diagram showing an implementation architecture of implementing ACC simulation using a vehicle control module according to an exemplary embodiment;

[0023] Figure 4 is a schematic structural diagram of an adaptive cruise simulation device according to an exemplary embodiment;

[0024] Figure 5 is a schematic diagram of a hardware structure of an electronic device according to an exemplary embodiment;

[0025] Figure 6 The figure is a schematic diagram of the structure of a storage medium according to an exemplary embodiment. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0027] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0028] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0029] As mentioned above, if the vehicle control module in the existing simulation solution is used to perform a simulation test of the vehicle ACC module, the vehicle control module will perform lateral control and longitudinal control of the vehicle according to the planned trajectory output by the vehicle ACC module.

[0030] Since the planning trajectory output by the vehicle ACC module is the same as the definition of the actual driving trajectory, the data items representing the lateral behavior of the vehicle in the planning trajectory remain unchanged or empty, and what changes are the data items representing the longitudinal behavior of the vehicle (such as speed v, distance s, acceleration a, jerk j and time t, etc. The vehicle ACC module performs real-time planning and output according to the provided virtual scene information). Therefore, the lateral control information output to the vehicle by the on-board control module according to the planned trajectory does not conform to the actual driving behavior of the driver, which is equivalent to using the on-board control module in the existing simulation scheme to simulate the vehicle ACC module, which lacks the linkage between lateral control and longitudinal control.

[0031] In view of this, the present application proposes an ACC simulation method, which generates a driving trajectory that simulates the driver's lateral driving behavior and obtains the planned trajectory planned by the vehicle ACC module, thereby controlling the motion state of the vehicle model to track the shape of the driving trajectory with the planning data representing the longitudinal behavior of the vehicle on the ACC planning trajectory, and completing the human-machine co-driving simulation test of the ACC function, so as to avoid the lateral control of the on-board control module during the ACC function test in the existing solution. Since the generated driving trajectory can represent the motion state of the driver's lateral control, and the planned trajectory planned by the ACC module can represent the longitudinal motion behavior of the vehicle, the self-consistent linkage of lateral control and longitudinal control is achieved by controlling the motion state of the vehicle model to track the shape of the driving trajectory with the planning data representing the longitudinal behavior of the vehicle on the planning trajectory, so as to better simulate the driver's driving behavior, thereby improving the simulation accuracy of the ACC function.

[0032] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The several specific embodiments listed can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0033] Embodiment 1:

[0034] Figure 1 The flowchart of an embodiment of an adaptive cruise simulation method according to an exemplary embodiment includes the following steps:

[0035] Step 101: Obtain a driving trajectory simulating a driver's lateral driving behavior.

[0036] The driving trajectory is used to control the lateral behavior of the vehicle, that is, to drive the vehicle according to the shape of the driving trajectory, and the trajectory data representing the lateral behavior can reflect the driver's driving behavior, that is, reflect the lateral driving behavior of the driver manipulating the steering wheel of the vehicle. Optionally, the driving trajectory can be generated based on offline data.

[0037] In an optional implementation, historical vehicle roadside data may be acquired, and then a driving trajectory simulating the driver's lateral driving behavior may be generated based on the vehicle roadside data.

[0038] Among them, vehicle roadside data is vehicle-related data collected when the vehicle passes through the roadside unit, which can represent the real motion posture of the vehicle. Therefore, the driving trajectory generated based on the vehicle roadside data can well reflect the driver's driving behavior. Specifically, vehicle roadside data can include vehicle position information (x, y), heading information heading and road curvature information kappa, speed v, distance s, acceleration a, jerk j and time t, etc.

[0039] When generating a driving trajectory based on the vehicle roadside data, since the driving trajectory only controls the lateral behavior of the vehicle, it is only necessary to pay attention to the data of the trajectory shape, and there is no need to pay attention to the longitudinal information of the vehicle. Therefore, the data representing the lateral information of the vehicle in the vehicle roadside data can be used to generate the driving trajectory.

[0040] As mentioned above, among the various data included in the vehicle roadside data, the data representing the lateral information of the vehicle may include one or more of the vehicle position information (x, y), the heading information heading and the road curvature information kappa.

[0041] In another optional implementation, pre-drawn trajectory points may be acquired, and then a driving trajectory simulating the driver's lateral driving behavior may be generated according to the acquired trajectory points.

[0042] Among them, the pre-drawn track points can be adapted to some artificially constructed virtual scenes. Some special road scenes may not exist in the real vehicle roadside data, so the track points can be pre-drawn using a drawing tool to simulate the driving track shape of such special road scenes.

[0043] When generating the driving trajectory according to the acquired trajectory points, the trajectory line formed by fitting the trajectory points may be smoothed first, and then the processed trajectory line may be sampled to obtain the driving trajectory.

[0044] As mentioned above, the driving trajectory includes vehicle position information (x, y), heading information heading and road curvature information kappa. Specifically, the sampling points on the trajectory line are used as the vehicle position information (x, y), and the heading information heading and road curvature information kappa of the sampling points can be calculated based on the positions of the sampling points on the trajectory line.

[0045] It should be noted that, in order to meet the trajectory definition specifications, when generating the driving trajectory, in addition to the data representing the lateral information of the vehicle, the data representing the longitudinal information of the vehicle can be filled with a unified default value.

[0046] Step 102: Obtain the planned trajectory output by the vehicle ACC module.

[0047] In the embodiment of the present application, the vehicle ACC module is a module to be simulated and tested to implement the ACC function. Since the ACC module is an automobile control unit that controls the vehicle's braking and driving to achieve the vehicle maintaining a certain distance from the following vehicle or maintaining a certain speed, the lateral control of the vehicle is still controlled by the driver operating the vehicle steering wheel. Therefore, the data items representing the longitudinal behavior of the vehicle on the planned trajectory output by the vehicle ACC module are valid data items, while the data items representing the lateral behavior of the vehicle on the planned trajectory are actually invalid data items.

[0048] It is understandable that during simulation testing, virtual scene information is usually provided to the vehicle model, so the vehicle ACC module can plan and publish the planned trajectory in real time based on the provided virtual scene information (such as high-precision map data, vehicle perception data, vehicle positioning data, sensor data, etc.).

[0049] It can be seen that the planning trajectory released by the vehicle ACC module is used to guide the longitudinal movement of the vehicle, and is invalid for guiding the lateral movement of the vehicle.

[0050] Comparing the planned trajectory with the aforementioned driving trajectory, the definitions of the two are the same and both comply with the trajectory definition specification. The planned trajectory focuses on the data used to control the longitudinal behavior of the vehicle, including speed v, distance s, acceleration a, jerk j, and time t, etc. The driving trajectory focuses on the data used to control the lateral behavior of the vehicle, including vehicle position information (x, y), heading information heading, and road curvature information kappa.

[0051] Step 103: Control the motion state of the vehicle model to follow the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

[0052] The vehicle model is used to simulate the vehicle to form a virtual vehicle, and the execution state parameters and vehicle posture parameters can be calculated based on the current motion state of the simulated virtual vehicle.

[0053] Optionally, the execution state parameter represents vehicle chassis information, including one or more of steering wheel angle, gear information, vehicle speed, and handbrake state information. The vehicle posture parameter represents vehicle positioning information, including one or more of vehicle position information, heading information, vehicle speed, and acceleration.

[0054] It is understandable that the vehicle model can be implemented based on a vehicle dynamics model or a vehicle kinematics model, and this application does not make any specific limitation here.

[0055] In the embodiment of the present application, the motion state of the vehicle model includes two types: longitudinal motion state and lateral motion state. As mentioned above, since the planning trajectory is used to guide the longitudinal motion of the vehicle and the driving trajectory is used to guide the lateral motion of the vehicle, the vehicle model is driven to operate by following the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory. This can well achieve self-consistent linkage between the lateral control and the longitudinal control of the vehicle model and improve the simulation accuracy of the ACC function.

[0056] Based on this, in an optional implementation, for the control process of the vehicle model, the vehicle lateral control information can be generated based on the driving trajectory, and the vehicle longitudinal control information can be generated based on the planned trajectory, and then the lateral control information and the longitudinal control information are used to control the motion state of the vehicle model.

[0057] Among them, the vehicle lateral control information is used to control the lateral motion state of the vehicle model, and the vehicle longitudinal control information is used to control the longitudinal motion state of the vehicle model. The lateral motion state and the longitudinal motion state can drive the vehicle model to perform virtual driving actions. Since the planning trajectory is used to guide the longitudinal motion of the vehicle, and the driving trajectory is used to guide the lateral motion of the vehicle, the virtual driving action that can be achieved by using the lateral control information and the longitudinal control information is to follow the shape of the driving trajectory with the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

[0058] Optionally, the lateral control information may be steering wheel angle information, and the longitudinal control information may be vehicle acceleration information.

[0059] It should be noted that when using the driving trajectory to generate the vehicle's lateral control information, and when using the planned trajectory to generate the vehicle's longitudinal control information, it is also necessary to refer to the execution state parameters and vehicle posture parameters output by the vehicle model that characterize the vehicle's current motion state to generate the vehicle's lateral control information and vehicle longitudinal control information, so as to form a closed-loop simulation of human-machine co-driving for adaptive cruise control.

[0060] Regarding the process of generating vehicle lateral control information, in one example, the vehicle lateral control information is generated by acquiring execution state parameters and vehicle posture parameters output by a vehicle model, and by using a first preset control algorithm, the driving trajectory, the acquired execution state parameters and vehicle posture parameters.

[0061] In another example, by acquiring execution state parameters and vehicle posture parameters output by a vehicle model, and through a second preset control algorithm, the vehicle lateral control information is generated using the driving trajectory, the acquired execution state parameters and vehicle posture parameters, and the vehicle longitudinal control information.

[0062] It is understandable that the control algorithms used to generate lateral control information usually include model predictive control (MPC) algorithm, linear quadratic control (LQR) algorithm, and proportional-integral-derivative control (PID) algorithm. Among these control algorithms, some control algorithms need to input the vehicle longitudinal control information in addition to the execution state parameters, vehicle posture parameters, and driving trajectory when generating lateral control information, such as the model predictive control algorithm, so the second preset control algorithm can be the model predictive control algorithm; some control algorithms only need to input the execution state parameters, vehicle posture parameters, and driving trajectory when generating lateral control information, such as the linear quadratic control algorithm and the proportional-integral-derivative control algorithm, so the first preset control algorithm can be the linear quadratic control algorithm or the proportional-integral-derivative control algorithm.

[0063] Regarding the process of generating vehicle longitudinal control information, in one example, the execution state parameters and vehicle posture parameters output by the vehicle model are obtained, and the planning information representing the longitudinal behavior of the vehicle in the planning trajectory is extracted, and then the vehicle longitudinal control information is generated using the extracted planning information, the obtained execution state parameters and the vehicle posture parameters.

[0064] As mentioned above, the data items representing the longitudinal behavior of the vehicle in the planned trajectory include speed v, distance s, acceleration a, jerk j and time t, so the extracted planning information may include one or more of speed v, distance s, acceleration a, jerk j and time t.

[0065] It can be understood that the control algorithms used to generate longitudinal control information generally include a model prediction control algorithm and a proportional integral derivative regulation control algorithm.

[0066] In order to understand the simulation process described in steps 101 to 103 more vividly, see Figure 2 As shown in the control schematic diagram for implementing ACC simulation, point A represents the vehicle model, human trajectory represents the driving trajectory, which is the route generated based on offline data simulation, and ACC trajectory represents the planned trajectory, which is planned in real time by the vehicle ACC module. During the simulation process, the vehicle model is controlled to drive according to the planned data (such as speed) representing the longitudinal behavior on the ACC trajectory, and the vehicle model is also controlled to drive according to the shape of the human trajectory.

[0067] At this point, the above Figure 1The adaptive cruise simulation process shown in the figure generates a driving trajectory that simulates the driver's lateral driving behavior and obtains the planned trajectory planned by the vehicle ACC module, thereby controlling the motion state of the vehicle model to track the shape of the driving trajectory with the planning data representing the longitudinal behavior of the vehicle on the ACC planning trajectory, and completing the human-machine co-driving simulation test of the ACC function to avoid the lateral control of the on-board control module during the ACC function test in the existing solution. Since the generated driving trajectory can represent the motion state of the driver's lateral control, and the planned trajectory planned by the ACC module can represent the longitudinal motion behavior of the vehicle, the self-consistent linkage of lateral control and longitudinal control is achieved by controlling the motion state of the vehicle model to track the shape of the driving trajectory with the planning data representing the longitudinal behavior of the vehicle on the planning trajectory, so as to better simulate the driver's driving behavior and thereby improve the simulation accuracy of the ACC function.

[0068] Embodiment 2:

[0069] Based on the above Figure 1 Based on the illustrated embodiment, a solution for implementing ACC simulation by combining a vehicle control unit in an existing simulation solution is given below.

[0070] See also Figure 3 As shown, the vehicle model implemented by the vehicle kinematic model or the vehicle dynamics model receives the longitudinal control instructions (i.e., the vehicle longitudinal control information) output by the on-board control module and the lateral control instructions (i.e., the vehicle lateral control information) output by the driver's driving control unit. The longitudinal control instructions are used to control the longitudinal movement of the vehicle model, and the lateral control instructions are used to control the lateral movement of the vehicle model. At the same time, the vehicle model will generate execution state parameters representing the vehicle chassis information and vehicle posture parameters representing the vehicle positioning information in real time according to the current motion state.

[0071] The on-board control module obtains the execution state parameters and vehicle posture parameters output by the vehicle model by calling the vehicle state interface, and transmits the obtained execution state parameters and vehicle posture parameters to the longitudinal controller and the lateral controller, and the on-board control module also obtains the planned trajectory output by the ACC module in real-time planning by calling the trajectory interface, and transmits the obtained planned trajectory to the longitudinal controller and the lateral controller; then the longitudinal controller in the on-board control module generates a longitudinal control instruction based on the execution state parameters, the vehicle posture parameters and the planned trajectory through the control algorithm and transmits it to the vehicle model, and because the lateral control instruction generated by the lateral controller in the on-board control module based on the execution state parameters, the vehicle posture parameters and the planned trajectory through the control algorithm is a planned trajectory of tracking, but the lateral control in the ACC simulation is controlled by the driving trajectory generated by the driver driving trajectory generator in the driver driving control unit, therefore the lateral control instruction generated by the lateral controller in the on-board control module will not be transmitted to the vehicle model, but will be discarded.

[0072] The driver driving module obtains the execution state parameters and vehicle posture parameters output by the vehicle model by calling the vehicle state interface, and transmits the obtained execution state parameters and vehicle posture parameters to the lateral controller, and the driving trajectory generated by the driver driving trajectory generator in the driver driving module is different from the planned trajectory obtained from the ACC module by the planned trajectory interface in the vehicle control module. The driver driving module obtains the driving trajectory generated by the driver driving trajectory generator by calling the trajectory interface and transmits it to the lateral controller; then the lateral controller in the driver driving module generates lateral control instructions based on the execution state parameters, vehicle posture parameters and driving trajectory through the control algorithm and transmits them to the vehicle model.

[0073] Finally, the vehicle model receives the lateral control instructions sent by the driver's driving module and the longitudinal control instructions sent by the on-board control module. The vehicle model implements lateral control according to the lateral control instructions and implements longitudinal control according to the longitudinal control instructions.

[0074] It can be seen that the vehicle model continuously receives lateral control instructions and longitudinal control instructions to realize lateral control and longitudinal control respectively. At the same time, it also generates execution state parameters representing vehicle chassis information and vehicle posture parameters representing vehicle positioning information in real time according to the motion state, thus continuously cycling to realize human-machine co-driving simulation of adaptive cruise.

[0075] It should be noted that during the simulation process, the lateral control instructions sent by the driver's driving module, the longitudinal control instructions sent by the vehicle control module, and the execution state parameters and vehicle posture parameters output by the vehicle model are continuously recorded. These recorded data are used as simulation data for analysis after the simulation is completed.

[0076] The execution subject of the embodiment of the present application may be an application, service, instance, software-based functional module, virtual machine (VM), container or cloud server, etc., or a hardware device (such as a server or terminal device) or hardware chip (such as CPU, GPU, FPGA, NPU, AI acceleration card or DPU) with data processing function. The device for implementing adaptive cruise simulation can be deployed on the computing device of the application party that provides the corresponding service or on a cloud computing platform that provides computing power, storage and network resources. The mode in which the cloud computing platform provides services to the outside world may be IaaS (Infrastructure as a Service), PaaS (Platform as a Service), SaaS (Software as a Service) or DaaS (Data as a Service). Taking the platform providing SaaS software as a service (Software as a Service) as an example, the cloud computing platform can use its own computing resources to provide training of adaptive cruise simulation models or functional execution of adaptive cruise simulation modules, and the specific application architecture can be built according to service requirements. For example, the platform can provide construction services based on the above model to application parties or individuals using platform resources, and further call the above model and implement online or offline adaptive cruise simulation functions based on adaptive cruise simulation requests submitted by relevant clients or servers and other devices.

[0077] Corresponding to the above-mentioned embodiment of the adaptive cruise simulation method, the present application also provides an embodiment of an adaptive cruise simulation device.

[0078] Figure 4 FIG. 1 is a schematic diagram of a structure of an adaptive cruise simulation device according to an exemplary embodiment. The device is used to execute the adaptive cruise simulation method provided in any of the above embodiments. Figure 4 As shown, the adaptive cruise simulation device includes:

[0079] A driving trajectory unit 410 is used to obtain a driving trajectory simulating a driver's lateral driving behavior;

[0080] The planning trajectory unit 420 is used to obtain the planning trajectory output by the vehicle ACC module;

[0081] The control unit 430 is used to control the motion state of the vehicle model to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

[0082] In an optional implementation, the control unit 430 is specifically used to generate vehicle lateral control information based on the driving trajectory; generate vehicle longitudinal control information based on the planned trajectory; and control the motion state of the vehicle model using the lateral control information and the longitudinal control information.

[0083] In an optional embodiment, the control unit 430 is specifically used to obtain the execution state parameters and vehicle posture parameters output by the vehicle model during the process of generating the vehicle lateral control information based on the driving trajectory; and generate the vehicle lateral control information by using the driving trajectory, the obtained execution state parameters and the vehicle posture parameters through a first preset control algorithm.

[0084] In an optional implementation, the control unit 430 is specifically used to obtain execution state parameters and vehicle posture parameters output by the vehicle model during the process of generating vehicle lateral control information based on the driving trajectory; and generate vehicle lateral control information through a second preset control algorithm using the driving trajectory, the obtained execution state parameters and vehicle posture parameters, and the vehicle longitudinal control information.

[0085] In an optional embodiment, the control unit 430 is specifically used to obtain execution state parameters and vehicle posture parameters output by the vehicle model during the process of generating vehicle longitudinal control information based on the planned trajectory; extract planning information representing the longitudinal behavior of the vehicle in the planned trajectory; and generate vehicle longitudinal control information using the planning information, the obtained execution state parameters and vehicle posture parameters.

[0086] In an optional implementation manner, the driving trajectory unit 410 is specifically used to obtain historical vehicle roadside data; and generate a driving trajectory that simulates the driver's lateral driving behavior based on the vehicle roadside data.

[0087] In an optional embodiment, the driving trajectory unit 410 is specifically used to generate a driving trajectory using data representing the lateral information of the vehicle in the vehicle roadside data during the process of generating a driving trajectory that simulates the driver's lateral driving behavior based on the vehicle roadside data; wherein the data representing the lateral information of the vehicle includes one or more of vehicle position information, heading information and road curvature information.

[0088] In an optional implementation manner, the driving trajectory unit 410 is specifically used to obtain pre-drawn trajectory points; and generate a driving trajectory that simulates the driver's lateral driving behavior according to the obtained trajectory points.

[0089] In an optional implementation, the driving trajectory unit 410 is specifically used to smooth the trajectory line formed by fitting the trajectory points in the process of generating a driving trajectory that simulates the driver's lateral driving behavior based on the acquired trajectory points; and sample the processed trajectory line to obtain the driving trajectory.

[0090] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0091] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only 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 present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0092] The embodiment of the present application also provides an electronic device corresponding to the adaptive cruise simulation method provided in the above embodiment, so as to execute the above adaptive cruise simulation method.

[0093] Figure 5 The hardware structure diagram of an electronic device according to an exemplary embodiment is shown, and the electronic device includes: a communication interface 601, a processor 602, a memory 603 and a bus 604; wherein the communication interface 601, the processor 602 and the memory 603 complete mutual communication through the bus 604. The processor 602 can execute the adaptive cruise simulation method described above by reading and executing the machine executable instructions corresponding to the control logic of the adaptive cruise simulation method in the memory 603. The specific content of the method is referred to the above embodiment, and will not be repeated here.

[0094] The memory 603 mentioned in this application can be any electronic, magnetic, optical or other physical storage device, and can contain storage information, such as executable instructions, data, etc. Specifically, the memory 603 can be RAM (Random Access Memory), flash memory, storage drive (such as hard disk drive), any type of storage disk (such as optical disk, DVD, etc.), or similar storage medium, or a combination thereof. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 601 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0095] The bus 604 may be an ISA bus, a PCI bus or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 603 is used to store programs, and the processor 602 executes the programs after receiving execution instructions.

[0096] Processor 602 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in processor 602 or an instruction in software form. The above-mentioned processor 602 can be a general-purpose processor, including a network processor (Network Processor, referred to as NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute.

[0097] The electronic device provided in the embodiment of the present application and the adaptive cruise simulation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0098] The present application also provides a computer-readable storage medium corresponding to the adaptive cruise simulation method provided in the above embodiment. Figure 6 As shown, the computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the adaptive cruise simulation method provided by any of the aforementioned embodiments will be executed.

[0099] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0100] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the adaptive cruise simulation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0101] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0102] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0103] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An adaptive cruise simulation method, characterized in that: The method comprises: Obtaining a driving trajectory simulating a driver's lateral driving behavior; Get the planning trajectory output by the vehicle adaptive cruise control ACC module; The motion state of the vehicle model is controlled to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

2. The method according to claim 1, characterized in that The controlling the vehicle model to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory includes: generating vehicle lateral control information based on the driving trajectory; generating vehicle longitudinal control information based on the planned trajectory; The motion state of the vehicle model is controlled using the lateral control information and the longitudinal control information.

3. The method according to claim 2, characterized in that The generating of the vehicle lateral control information based on the driving trajectory comprises: Obtaining execution state parameters and vehicle posture parameters output by the vehicle model; The vehicle lateral control information is generated by a first preset control algorithm using the driving trajectory, the acquired execution state parameters and the vehicle posture parameters.

4. The method according to claim 2, characterized in that: The generating of the vehicle lateral control information based on the driving trajectory comprises: Obtaining execution state parameters and vehicle posture parameters output by the vehicle model; The vehicle lateral control information is generated by a second preset control algorithm using the driving trajectory, the acquired execution state parameters and vehicle posture parameters, and the vehicle longitudinal control information.

5. The method according to claim 2, characterized in that: The generating of vehicle longitudinal control information based on the planned trajectory includes: Obtaining execution state parameters and vehicle posture parameters output by the vehicle model; extracting planning information representing longitudinal behavior of the vehicle from the planning trajectory; The planning information, the acquired execution state parameters and the vehicle posture parameters are used to generate vehicle longitudinal control information.

6. The method according to claim 1, characterized in that The step of obtaining a driving trajectory simulating the lateral driving behavior of the driver includes: Acquire historical vehicle roadside data; generate a driving trajectory simulating the driver's lateral driving behavior based on the vehicle roadside data; The generating of a driving trajectory simulating a driver's lateral driving behavior according to the vehicle roadside data comprises: The driving trajectory is generated using data representing the lateral information of the vehicle in the vehicle roadside data; wherein the data representing the lateral information of the vehicle includes one or more of vehicle position information, heading information and road curvature information.

7. The method according to claim 1, characterized in that The step of obtaining a driving trajectory simulating the lateral driving behavior of the driver includes: Acquire pre-drawn trajectory points; generate a driving trajectory simulating the driver's lateral driving behavior according to the acquired trajectory points; The step of generating a driving trajectory simulating the driver's lateral driving behavior according to the acquired trajectory points includes: The trajectory line formed by fitting the trajectory points is smoothed; and the processed trajectory line is sampled to obtain a driving trajectory.

8. An adaptive cruise simulation device, characterized in that: The device comprises: A driving trajectory unit, used to obtain a driving trajectory simulating a driver's lateral driving behavior; A planning trajectory unit, used to obtain the planning trajectory output by the vehicle adaptive cruise control ACC module; The control unit is used to control the motion state of the vehicle model to track the shape of the driving trajectory according to the planning data representing the longitudinal behavior of the vehicle on the planning trajectory.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes the program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the steps of the method according to any one of claims 1 to 7.

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