Vehicle control method and device and computer storage medium

Abstract

The invention provides a vehicle control method and device and a computer storage medium, the vehicle control method comprises the steps that input information of a decision module is obtained, and the input information comprises a plurality of obstacle targets, a cruise target vehicle speed, a cruise expected time interval and a self-vehicle decision action; inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle; inputting the input information into a jerk limiting module to obtain descending jerk and ascending jerk; and limiting the arbitration expected acceleration value by using the descent jerk, and determining a final acceleration value of the vehicle. By means of the mode, the obstacle with the following purpose is controlled, meanwhile, the obstacle can act on the threat obstacle with the non-following purpose, the obstacle information is provided by the decision module, and the control module makes reasonable control actions according to the information.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a vehicle control method, device and computer storage medium. Background Art

[0002] Automated driving assistance systems have developed rapidly in recent years. L2 functional level assisted driving can no longer meet market demand, and high-level intelligent driving has become the direction of efforts of manufacturers and suppliers.

[0003] Traditional intelligent driving has difficulty ensuring longitudinal safety, and can only perform acceleration and deceleration actions for obstacles for the purpose of following, but cannot make reasonable longitudinal actions for threatening obstacles that are not for the purpose of following. Summary of the invention

[0004] In order to solve the above technical problems, the present application proposes a vehicle control method, which includes: obtaining input information of a decision module, wherein the input information includes a number of obstacle targets, a cruising target speed, an expected cruising time interval, and a self-vehicle decision action; inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle; inputting the input information into an acceleration limitation module to obtain a descending jerk and an ascending jerk; and limiting the arbitration expected acceleration value using the descending jerk and the ascending jerk to determine the final acceleration value of the vehicle.

[0005] The step of inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle comprises: inputting the input information into a target tracking controller to determine target expected acceleration values ​​of the plurality of obstacle targets; inputting the input information into a cruise controller to determine a cruise expected acceleration value of the vehicle; and inputting the target expected acceleration value and the cruise expected acceleration value into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle.

[0006] Wherein, the target tracking controller includes a sliding mode controller and a creeping controller; the inputting of the input information into the target tracking controller to determine the target expected acceleration values ​​of the several obstacle targets includes: inputting the input information into the sliding mode controller to determine the sliding mode expected acceleration; inputting the input information into the creeping controller to determine the creeping expected acceleration; and determining the larger value of the sliding mode expected acceleration and the creeping expected acceleration of each obstacle target as the target expected acceleration value of the obstacle target.

[0007] The step of inputting the target expected acceleration value and the cruise expected acceleration value into an acceleration arbitration module to obtain the arbitration expected acceleration value of the vehicle includes: determining the minimum value of the cruise expected acceleration value and the target expected acceleration values ​​of each obstacle target as the arbitration expected acceleration value of the vehicle.

[0008] Wherein, inputting the input information into the cruise controller to determine the expected cruising acceleration value of the vehicle includes: inputting the vehicle speed and the cruise speed error in the input information into the cruise controller to determine the expected cruising acceleration value of the vehicle; wherein the cruise speed error is the difference between the vehicle speed and the cruise target speed.

[0009] After obtaining the input information of the decision module, the vehicle control method further includes: synchronizing the obstacle target information in the input information to the current timestamp of the vehicle; wherein the obstacle target information includes the distance, speed and acceleration of the obstacle target.

[0010] The obstacle targets include conventional obstacle targets and virtual obstacle targets; synchronizing the obstacle target information in the input information to the current timestamp of the vehicle includes: synchronizing the information of the conventional obstacle target to the current timestamp of the vehicle using the time difference between the perception module and the control module; synchronizing the information of the virtual obstacle target to the current timestamp of the vehicle using the time difference between the decision module and the control module.

[0011] Wherein, after synchronizing the obstacle target information in the input information to the current timestamp of the vehicle, the vehicle control method further includes: setting a filter to filter the synchronized obstacle target information; wherein the filtering mode of the filter includes a normal filtering mode, a fast filtering mode, and / or a reset filtering mode.

[0012] In order to solve the above technical problems, the present application proposes a vehicle control device, which includes a memory and a processor coupled to the memory; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above vehicle control method.

[0013] In order to solve the above technical problems, the present application proposes a computer storage medium, which is used to store program data. When the program data is executed by a computer, it is used to implement the above vehicle control method.

[0014] Different from the prior art, the beneficial effects of the present application are as follows: the vehicle control device obtains the input information of the decision module, wherein the input information includes several obstacle targets, cruising target speed, expected cruising time interval and self-vehicle decision action; the input information is input into the acceleration arbitration module to obtain the arbitration expected acceleration value of the vehicle; the input information is input into the acceleration limitation module to obtain the descending acceleration and the ascending acceleration; the arbitration expected acceleration value is limited by the descending acceleration and the ascending acceleration to determine the final acceleration value of the vehicle. In the above manner, in addition to controlling obstacles for the purpose of following, it also acts on threatening obstacles for non-following purposes. The obstacle information is provided by the decision module, and the control module makes reasonable control actions based on the above information. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 is a flow chart of a first embodiment of a vehicle control method provided by the present application;

[0017] Figure 2 It is a schematic diagram of the overall flow of the algorithm provided by this application;

[0018] Figure 3 is a flow chart of a second embodiment of a vehicle control method provided by the present application;

[0019] Figure 4 It is a schematic diagram of the sliding mode area division provided by this application;

[0020] Figure 5 It is a schematic diagram of the buffer area provided by this application;

[0021] Figure 6 is a structural schematic diagram of an embodiment of a vehicle control device provided by the present application;

[0022] Figure 7 It is a schematic diagram of the computer storage medium provided by the present application. DETAILED DESCRIPTION

[0023] 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.

[0024] The vehicle control method of the present application is applied to a vehicle control device, wherein the vehicle control device of the present application can be a server, or a system in which a server and a local terminal cooperate with each other. Accordingly, the various parts of the vehicle control device, such as various units, subunits, modules, and submodules, can all be set in the server, or can be set in the server and the local terminal respectively.

[0025] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here. In some possible implementations, the vehicle control method of the embodiment of the present application can be implemented by a processor calling computer-readable instructions stored in a memory.

[0026] In order to solve the above technical problems, the present application proposes a vehicle control method. In this embodiment, refer to Figure 1-Figure 2 , Figure 1 is a flow chart of a first embodiment of a vehicle control method provided by the present application; Figure 2 It is a schematic diagram of the overall flow of the algorithm provided in this application.

[0027] like Figure 1 As shown, the specific steps are as follows:

[0028] Step S11: Obtain input information of the decision module.

[0029] Among them, in the embodiment of the present application, the input information includes a number of obstacle targets, a cruising target speed, an expected cruising time interval, and a self-vehicle decision action.

[0030] like Figure 2 As shown, the vehicle control device controls the conventional and virtual obstacle results, the cruising target speed, the expected cruising time interval and the self-vehicle decision action generated by the receiving decision module.

[0031] The target selection results of the decision module may include: primary and secondary following targets in the main lane, left and right lane targets, cutting targets, virtual targets generated by threat targets, virtual static targets generated by stop decisions, virtual obstacles generated by navigation decisions, such as left and right turn speed limits, U-turn speed limits, active or navigation lane changes, and virtual obstacles generated by obstacle game decisions, such as merging, and interactions between left and right turns at intersections and straight-moving vehicles.

[0032] The attributes of each target selected by the decision module are shown in Table 1:

[0033] Table 1

[0034] Property Name Attribute representative symbol unit Select target id id / Select target speed velocity m / s Select target acceleration accel m / s^2 Select target heading angle heading rad Select the relative distance of the target range m Select target motion curvature curvature / Select the lane where the target is located lane_assignment / Target lateral motion state lat_motion / Target longitudinal motion state lon_motion / Target expected time desired_time_gap s Target stopping distance stop_distance m Target Type type / Target Source source / Perception Source obj_from /

[0035] The cruising target speed can be obtained through comprehensive arbitration of the following vehicle speeds: cruising speed set by the driver, map speed limit, traffic sign speed limit, road curvature speed limit, ramp speed limit, scenario speed limit, such as diversion scenario, entry and exit ramp scenario, entry toll station scenario, narrow road speed limit, etc.

[0036] Among them, the expected cruising time is set by the driver.

[0037] The vehicle's decision actions include: lane change direction (turn_state), navigation direction (segment_direction), and overtaking sign (is_overtk).

[0038] The target selection results of the decision-making module include: primary and secondary following targets in the main lane, left and right lane targets, cutting targets, virtual targets generated by threat targets, virtual static targets generated by stop decisions, virtual obstacles generated by navigation decisions, such as left and right turn speed limits, U-turn speed limits, active / navigation lane changes, virtual obstacles generated by obstacle game decisions such as merging, and interactions between left and right turns and straight vehicles at intersections.

[0039] After the vehicle control device obtains the input information of the decision module, the vehicle control device performs the following steps:

[0040] Step S21: Synchronize the obstacle target information in the input information to the current timestamp of the vehicle.

[0041] In this embodiment of the present application, the obstacle target information includes the distance, speed and acceleration of the obstacle target.

[0042] In a specific embodiment of the present application, obstacles include the following obstacles: main lane target (host_road_objs), left lane target (left_road_objs), right lane target (right_road_objs), cut-in target (cutin_objs), cut-out target (cutout_objs), static obstacle (static_objs), virtual obstacle (fake_objs).

[0043] In the embodiment of the present application, the above data may be data of multiple targets.

[0044] When the obstacle perception source obj_from is a regular obstacle, the obstacle source is real, and the obstacle lat_motion is in the cutout state, it is stored in cutout_objs.

[0045] When the obstacle perception source obj_from is a regular obstacle, the obstacle source is real, and the obstacle lat_motion is in the cutin state, it is stored in cutin_objs.

[0046] When the obstacle perception source obj_from is a regular obstacle, and the obstacle source is real, and the target lane_assignment attribute is the main lane, it is stored in host_road_objs.

[0047] When the obstacle perception source obj_from is a regular obstacle, and the obstacle source is real, and the vehicle motion attribute turn_state is left lane change, the target lane_assignment attribute is the left lane, and it is stored in left_road_objs.

[0048] When the obstacle perception source obj_from is a regular obstacle, and the obstacle source is real, and the vehicle motion attribute turn_state is right lane change, and the target lane_assignment attribute is the right lane, it is stored in right_road_objs.

[0049] When the obstacle perception source obj_from is a static obstacle, it is stored in static_objs;

[0050] When the obstacle source is virtual, it is stored in fake_objs.

[0051] The attributes of obstacles are shown in Table 2:

[0052] Table 2

[0053] Property Name Attribute representative symbol unit Target relative distance range m Target speed velocity m / s Target acceleration accel m / s^2 Target expected distance range_desired m Target expected time desired_time_gap s Target stopping distance stop_distance m Overtaking sign is_overtk / Target time time_gap s Target collision time ttc s

[0054] In an embodiment of the present application, the obstacle target includes a conventional obstacle target and a virtual obstacle target; the present application proposes an embodiment for synchronizing the obstacle target information in the input information to the current timestamp of the vehicle, as follows:

[0055] The vehicle control device synchronizes the information of the conventional obstacle target to the current timestamp of the vehicle by using the time difference between the perception module and the control module; and synchronizes the information of the virtual obstacle target to the current timestamp of the vehicle by using the time difference between the decision module and the control module.

[0056] Specifically, the vehicle control device synchronizes the threatening obstacle given by the decision to the current timestamp. In particular, for real obstacles, the time difference between the perception module and the control module needs to be synchronized; for virtual obstacles, the time difference between the decision module and the control module needs to be synchronized. The specific calculation is as follows:

[0057]

[0058] Among them, obs_range_orign, obs_velocity_orign, and obs_accel are the original distance, velocity, and acceleration of the obstacle, respectively. obs_range and obs_velocity are the distance, velocity, and acceleration of the obstacle after time synchronization. _ time_syn is the time difference that needs to be synchronized.

[0059] Furthermore, after synchronizing the obstacle target information in the input information to the current timestamp of the vehicle, the vehicle control device sets a filter to filter the synchronized obstacle target information; wherein the filtering mode of the filter includes a normal filtering mode, a fast filtering mode, and / or a reset filtering mode.

[0060] Specifically, the vehicle control device uses the obstacle ID as the maintenance basis and designs filters for the obstacle distance, speed, and acceleration of each ID. It is particularly noteworthy that the filter is designed with three filtering modes, namely, the normal filtering mode, the fast filtering mode, and the reset filtering mode, which are respectively applied when the filtering result of the previous frame is not much different from the current frame result, when the filtering result of the previous frame is significantly different from the current frame result, and when the obstacle decision result changes, for example, the yield decision becomes an overtaking decision.

[0061] Step S12: inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle.

[0062] Specifically, the present application proposes an embodiment for obtaining the arbitration expected acceleration value of the vehicle. For details, please refer to Figure 3 , Figure 3 It is a flow chart of the second embodiment of the vehicle control method provided by the present application.

[0063] like Figure 3 As shown, the specific steps are as follows:

[0064] Step S31: inputting the input information into a target tracking controller to determine target expected acceleration values ​​of the plurality of obstacle targets.

[0065] Each target tracking controller consists of two controllers, namely a sliding mode controller and a creeping controller. The sliding mode controller is designed as follows:

[0066] First, according to the longitudinal following model, the sliding mode controller is designed:

[0067] For the controlled system:

[0068]

[0069] Where Δr is the distance error, Δv is the velocity error, and Δa is the acceleration error.

[0070] Design sliding mode variable s = α1Δr + α2Δv, design approach rate: Substituting into the solution, we can get:

[0071]

[0072] Where a_req_sm is the expected acceleration of the sliding mode controller, a lead is the acceleration of the obstacle car.

[0073] Obviously, the controller structure consists of three parts: the speed error related part, the sliding mode variable error related part, and the target vehicle acceleration related part.

[0074] The expected acceleration value of each tracking target controller is obtained by weighting the five sub-controllers, which represent different tracking states, namely, the following area, the fast catching-up area, the catching-up area, the lagging area, and the overtaking adjustment area. Figure 4 As shown, Figure 4 is a schematic diagram of sliding mode area division provided by this application, Figure 5 is a schematic diagram of the buffer area provided by this application. There is a transition area between each area to ensure smoothness when transitioning between areas, such as Figure 5 shown.

[0075] The specific design is as follows:

[0076] a_req=gamma_cu*a_cu+gamma_acu*a_acu+gamma_fb*a_fb

[0077] +gamma_os*a_os+gamma_fo*a_fo

[0078] Among them, a_req is the expected acceleration value of the tracked target, gamma_cu, gamma_acu, gamma_fb, gamma_os, and gamma_fo are the weight values ​​of the five areas respectively, and a_cu, a_acu, a_fb, a_os, and a_fo are the expected acceleration values ​​of the five areas respectively.

[0079] In one embodiment of the present application, a creeping controller is designed, and the specific control method is as follows:

[0080] The target tracking controller includes a sliding mode controller and a creeping controller; the step of inputting the input information into the target tracking controller to determine the target expected acceleration values ​​of the plurality of obstacle targets includes: inputting the input information into the sliding mode controller to determine the sliding mode expected acceleration; inputting the input information into the creeping controller to determine the creeping expected acceleration; and determining the larger value of the sliding mode expected acceleration and the creeping expected acceleration of each obstacle target as the target expected acceleration value of the obstacle target.

[0081] Specifically, the vehicle control device calibrates the expected speed according to the distance of the tracking target, and the controller tracks the expected speed and the expected distance of the target to obtain the expected acceleration of creeping.

[0082] The expected speed is calculated as follows:

[0083] creep_des_vel=inter_1d(vel_tab_x_range, vel_tab_y_range, obs_range)

[0084] Among them, inter_1d is a one-dimensional linear interpolation function, vel_tab_x_range and vel_tab_y_range form a two-dimensional table. Taking obs_range obstacle distance as the x-axis table difference, the corresponding y-axis coordinate can be obtained as creep_des_vel;

[0085] The creep controller output is calculated as follows:

[0086]

[0087] Among them, s=a1Δr+a2Δr, Δr=range_desired-range, Δv=creep_des_vel-obs_velocity.

[0088] It can be seen that the creep controller only depends on the distance of the tracking target, not on the speed and acceleration of the tracking target, because in the process of low-speed creeping, the front vehicle is in a stop-and-go process, and sudden acceleration and deceleration are inevitable, which puts higher requirements on perception. And because the self-vehicle and the front vehicle are in a stable following and close-range state, the front vehicle's speed and acceleration will be more sensitive. Therefore, in order to reduce the dependence on the accuracy of the front vehicle perception in the environment, this application designs a creep controller.

[0089] The present application uses a creep controller to reduce the sensitivity to the speed and acceleration of the vehicle ahead when following a vehicle at a close distance and at a low speed, and also has a greater tolerance for perception errors.

[0090] Step S32: inputting the input information into a cruise controller to determine the expected cruising acceleration value of the vehicle.

[0091] Specifically, the vehicle control device inputs the vehicle speed and the cruise speed error in the input information into the cruise controller to determine the expected cruise acceleration value of the vehicle; wherein the cruise speed error is the difference between the vehicle speed and the cruise target speed.

[0092] The step of inputting the input information into a cruise controller to determine the desired cruise acceleration value of the vehicle comprises: inputting the vehicle speed and a cruise speed error in the input information into the cruise controller to determine the desired cruise acceleration value of the vehicle; wherein the cruise speed error is the difference between the vehicle speed and the cruise target speed.

[0093] This application designs a pi controller, which takes the error between the vehicle speed and the cruise speed as input to obtain the desired cruise acceleration. In particular, the proportional coefficient and the integral coefficient are related to the input error to achieve a following process that is more in line with the driver's driving habits.

[0094] The specific design is as follows:

[0095] a_cruise_req=kp(vhost-set_speed)+ki∫(vhost-set_speed)

[0096] Among them, kp, ki are the proportional coefficient and integral coefficient respectively, vhost is the vehicle speed, set_speed is the cruising speed, and a_cruise_req is the expected cruising acceleration.

[0097] Step S33: inputting the target expected acceleration value and the cruise expected acceleration value into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle.

[0098] Specifically, the vehicle control device determines the minimum value of the cruise expected acceleration value and the target expected acceleration values ​​of each obstacle target as the arbitration expected acceleration value of the vehicle.

[0099] First, the vehicle control device arbitrates the two-part controller results of each tracking target, and takes the larger value of the sliding mode controller and the creep controller as the expected acceleration of this target; then, the smallest value of the cruise expected acceleration and the expected acceleration of each tracking target is taken as the arbitration expected acceleration value.

[0100] Step S13: input the input information into the jerk limiting module to obtain the descending jerk and the ascending jerk.

[0101] In the embodiment of the present application, a rising acceleration is designed, and this value is obtained by calibration, and may be related to the vehicle speed, the expected acceleration, and the difference between the actual acceleration.

[0102] In the embodiment of the present application, the descent jerk is designed by first calculating the pre-input quantity:

[0103]

[0104] Among them, inter_1d and inter_2d are the linear difference function and the two-dimensional linear difference function respectively, obs_range and obs_ttc are the distance and collision time of the tracking target corresponding to the minimum acceleration respectively, ratein_range is the pre-output related to the obstacle distance, and ratein_ttc_vhost is the pre-variable related to the collision time of the following target and the vehicle speed.

[0105] Furthermore, the normal descent jerk is calculated:

[0106]

[0107] Where a_diff is the difference between the expected acceleration and the actual acceleration.

[0108] Furthermore, the comfortable descent jerk is calculated:

[0109]

[0110] Where counter is the proportional coefficient, which is obtained by looking up the table with a_diff_count, ratein_a_diff is an intermediate variable, and a_diff_count is a measure of the difference between the expected acceleration and the actual acceleration, which includes the consideration of time. You can refer to the following design:

[0111] a_diff_count=a_diff_count_pre+current_count

[0112] Among them, current_count is proportional to a_diff, that is, the larger a_diff is, the larger current_count is, and its value is between 0 and 2.

[0113] Get the final descent jerk:

[0114] failing_jerk=min(common_failing_jerk,soft_failing_jerk)

[0115] There is also a jerk design for the cruise acceleration, which will not be elaborated on in detail. The final desired acceleration is obtained by limiting the jerk.

[0116] Step S14: limiting the arbitration expected acceleration value by using the descending jerk and the ascending jerk to determine a final acceleration value of the vehicle.

[0117] Based on the traditional control and following vehicle framework, this application adds the design of a virtual tracking target, which can solve the disadvantageous and dangerous scenarios of the traditional solution, such as obstacle game at intersections, merging and giving way, oncoming vehicles, etc. In addition, the present invention expands the traditional control framework to high-level intelligent driving solutions, which can support traffic light start and stop, active lane change, navigation lane change, merging and splitting, etc. In terms of control algorithm design, in addition to the conventional tracking controller, this application also designs a creeping controller to reduce the dependence on the perceived obstacle speed and acceleration accuracy. In terms of algorithm resource usage, the present invention divides different following vehicle control areas on the control end to meet the comfort and safety of following vehicles under various road conditions, no longer relying on the prediction module and speed planning scheme, and saving computing resources.

[0118] In order to implement the vehicle control method of the above embodiment, the present application also provides a vehicle control device. Figure 6 , Figure 6 It is a structural schematic diagram of an embodiment of a vehicle control device provided by the present application.

[0119] like Figure 6As shown, the vehicle control device 600 of this embodiment includes a processor 61 , a memory 62 , an input / output device 63 , and a bus 64 .

[0120] The processor 61 , the memory 62 , and the input / output device 63 are respectively connected to the bus 64 . The memory 62 stores a computer program, and the processor 61 is used to execute the computer program to implement the vehicle control method of the above embodiment.

[0121] In this embodiment, the processor 61 may also be referred to as a CPU (Central Processing Unit). The processor 61 may be an integrated circuit chip having the ability to process signals. The processor 61 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 61 may also be a GPU (Graphics Processing Unit), also known as a display core, a visual processor, or a display chip, which is a microprocessor that is specifically used for image computing on computers, workstations, game consoles, and some mobile devices (such as tablet computers, smart phones, etc.). The purpose of the GPU is to convert and drive the display information required by the computer system, and to provide a line scan signal to the display to control the correct display of the display. It is an important component that connects the display and the computer motherboard. As an important component of the computer host, the graphics card is responsible for outputting display graphics. The general-purpose processor may be a microprocessor or the processor 61 may also be any conventional processor, etc.

[0122] The present application also provides a computer storage medium, such as Figure 7 As shown, Figure 7 It is a schematic diagram of a computer storage medium provided in the present application. The computer storage medium 700 is used to store a computer program 71. When the computer program 71 is executed by a processor, it is used to implement the method described in the embodiment of the vehicle control method of the present application.

[0123] The method involved in the embodiment of the vehicle control method of the present application, when implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a device, such as a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various implementation methods of the present application.

[0128] The above description is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle control method, characterized in that: The vehicle control method comprises: Obtaining input information of a decision module, wherein the input information includes a number of obstacle targets, a cruising target speed, an expected cruising time interval, and a self-vehicle decision action; Inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle; Inputting the input information into the jerk limiting module to obtain the descending jerk and the ascending jerk; The arbitration expected acceleration value is limited by using the descending jerk and the ascending jerk to determine a final acceleration value of the vehicle.

2. The vehicle control method according to claim 1, characterized in that: The step of inputting the input information into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle includes: Inputting the input information into a target tracking controller to determine target expected acceleration values ​​of the plurality of obstacle targets; Inputting the input information into a cruise controller to determine a cruise expected acceleration value of the vehicle; The target expected acceleration value and the cruise expected acceleration value are input into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle.

3. The vehicle control method according to claim 2, characterized in that: The target tracking controller includes a sliding mode controller and a creeping controller; The step of inputting the input information into a target tracking controller to determine target expected acceleration values ​​of the plurality of obstacle targets includes: Inputting the input information into a sliding mode controller to determine a sliding mode desired acceleration; Inputting the input information into a creep controller to determine a creep desired acceleration; The larger value of the sliding mode expected acceleration and the creeping expected acceleration of each obstacle target is determined as the target expected acceleration value of the obstacle target.

4. The vehicle control method according to claim 2, characterized in that: The step of inputting the target expected acceleration value and the cruise expected acceleration value into an acceleration arbitration module to obtain an arbitration expected acceleration value of the vehicle includes: The minimum value of the cruise expected acceleration value and the target expected acceleration values ​​of each obstacle target is determined as the arbitration expected acceleration value of the vehicle.

5. The vehicle control method according to claim 2, characterized in that: The step of inputting the input information into a cruise controller to determine the expected cruise acceleration value of the vehicle includes: Inputting the vehicle speed and the cruising speed error in the input information into the cruise controller to determine the expected cruising acceleration value of the vehicle; The cruising speed error is the difference between the vehicle speed and the cruising target speed.

6. The vehicle control method according to claim 1, characterized in that: After obtaining the input information of the decision module, the vehicle control method further includes: Synchronizing obstacle target information in the input information to a current timestamp of the vehicle; The obstacle target information includes the distance, speed and acceleration of the obstacle target.

7. The vehicle control method according to claim 6, characterized in that: The obstacle targets include conventional obstacle targets and virtual obstacle targets; The step of synchronizing the obstacle target information in the input information to the current timestamp of the vehicle includes: Synchronizing the information of the conventional obstacle target to the current timestamp of the vehicle by utilizing the time difference between the perception module and the control module; The information of the virtual obstacle target is synchronized to the current timestamp of the vehicle by utilizing the time difference between the decision module and the control module.

8. The vehicle control method according to claim 7, characterized in that: After synchronizing the obstacle target information in the input information to the current timestamp of the vehicle, the vehicle control method further includes: Setting a filter for filtering the synchronized obstacle target information; The filtering mode of the filter includes a normal filtering mode, a fast filtering mode, and / or a reset filtering mode.

9. A vehicle control device, characterized in that: The vehicle control device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the vehicle control method as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the vehicle control method according to any one of claims 1 to 8.

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