Vehicle control method, vehicle and storage medium

Abstract

The invention provides a vehicle control method, a vehicle and a storage medium, relates to the technical field of vehicles, and can solve the problems that a current trajectory planning scheme cannot plan a vehicle trajectory according to specific requirements and is poor in applicability. The method comprises the following steps: determining a target driving track in at least one driving track based on a target driving demand; the at least one driving track is a driving track of the vehicle in the road, which is predicted through a vehicle motion prediction model based on road parameters; the vehicle motion prediction model is used for predicting a driving track of the vehicle in a motion state; and controlling the vehicle to run according to the target running track. According to the invention, the applicability of the vehicle driving track scheme can be improved, and the vehicle can be driven based on the target driving demand.

Description

Technical Field

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

[0002] With the rapid and continuous updating and iteration of science and technology in my country, autonomous driving technology has become a research hotspot in the current automotive industry. The key to vehicle autonomous driving lies in the planning of autonomous driving trajectories at future moments. Therefore, trajectory planning technology in the field of autonomous driving is crucial to achieving safe, efficient, and natural driving behavior.

[0003] Among the related technologies, the trajectory planning technologies currently used in the field of autonomous driving mostly plan the vehicle's trajectory based on curvature, path distance or based on some constraints, such as planning the path with minimum curvature or planning the trajectory with the shortest distance.

[0004] However, this type of trajectory planning scheme cannot plan vehicle trajectories according to specific requirements and has poor applicability. For example, for the moose test condition, it is impossible to plan the fastest vehicle trajectory. Summary of the invention

[0005] The present application provides a vehicle control method, a vehicle and a storage medium, which solves the problem that the current trajectory planning scheme cannot plan the vehicle trajectory according to specific requirements and has poor applicability, can improve the applicability of the vehicle driving trajectory scheme, and enable the vehicle to travel based on target driving requirements.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a vehicle control method, the method comprising: determining a target driving trajectory in at least one driving trajectory based on a target driving demand; at least one driving trajectory is a driving trajectory of the vehicle on the road predicted based on road parameters through a vehicle motion prediction model; the vehicle motion prediction model is used to predict the driving trajectory of the vehicle under a moving state; and controlling the vehicle to drive according to the target driving trajectory.

[0008] In combination with the above-mentioned first aspect, in a possible implementation method, the target driving demand is one or more of the preset driving demands; the preset driving demand includes at least one of the following: the vehicle's driving speed is the highest, the driving time is the shortest, the driving distance is the shortest, the curvature of the driving trajectory is the smallest, the vehicle's driving state during driving is the most stable, and the similarity with the original driving trajectory is the highest at the preset position.

[0009] In combination with the first aspect above, in a possible implementation method, based on a preset driving requirement, determining a target driving trajectory in at least one driving trajectory includes: for each driving trajectory, determining a matching degree of the driving trajectory to the preset driving requirement; and determining the driving trajectory with the greatest matching degree as the target driving trajectory.

[0010] In combination with the first aspect above, in a possible implementation manner, the method further includes: constructing a vehicle motion prediction model based on the vehicle's driving state parameters and motion control parameters.

[0011] In combination with the first aspect above, in a possible implementation method, a vehicle motion prediction model is constructed based on the vehicle's driving state parameters and motion control parameters, including: constructing a vehicle dynamics model of the vehicle based on the driving state parameters; constructing a vehicle motion prediction model based on the vehicle dynamics model and motion control parameters.

[0012] In combination with the first aspect above, in a possible implementation method, the step of determining at least one driving trajectory includes: determining the drivable range of the vehicle on the road based on road parameters; constraining the vehicle motion prediction model based on the drivable range and trajectory constraints to determine at least one driving trajectory.

[0013] In combination with the first aspect above, in a possible implementation method, controlling a vehicle to travel along a target driving trajectory includes: determining a target control amount of each actuator in the vehicle based on the target driving trajectory; the target control amount is the control amount executed by the actuator when the vehicle travels along the target driving trajectory; sending the target control amount to each actuator in the vehicle through the vehicle's chassis wire control execution system, so that each actuator executes the target control amount to control the vehicle to travel along the target driving trajectory.

[0014] In combination with the first aspect above, in a possible implementation method, based on the target driving trajectory, the target control amount of each actuator in the vehicle is determined, including: obtaining the real-time motion state of the vehicle; based on the real-time motion state, correcting the target driving trajectory through a vehicle motion prediction model to obtain a corrected target driving trajectory; based on the corrected target driving trajectory, determining the target control amount of each actuator in the vehicle.

[0015] In combination with the first aspect above, in a possible implementation manner, the motion control parameter includes at least one of the following: a motor torque of the vehicle and a wheel angle of the vehicle.

[0016] In combination with the above-mentioned first aspect, in a possible implementation, when the vehicle is equipped with a first motor, a second motor, a third motor, a first steering device and a second steering device, the motion control parameters specifically include at least one of the following: the vehicle's first motor torque, the second motor torque, the third motor torque, the front wheel equivalent angle, the left rear wheel angle and the right rear wheel angle; wherein the first motor is used to drive the vehicle's left front wheel and right front wheel, the second motor is used to drive the vehicle's left rear wheel, and the third motor is used to drive the vehicle's right rear wheel; the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0017] In combination with the above-mentioned first aspect, in a possible implementation method, the driving state parameters include at least one of the following: vehicle position information, longitudinal speed, lateral speed, vehicle heading angle, yaw angular velocity, mass, longitudinal component of force received by each wheel in the ground coordinate system, lateral component of force received by each wheel in the ground coordinate system, vehicle moment of inertia, distance from the center of mass of the vehicle to the front axle, distance from the center of mass of the vehicle to the rear axle, front wheel track, rear wheel track, wheel turning angle, longitudinal force received by the wheel in the wheel coordinate system, and lateral force received by the wheel in the wheel coordinate system.

[0018] In combination with the above first aspect, in a possible implementation manner, the road parameter includes at least one of the following: a position of a center point of the road, a road width at the center point, and position information of obstacles in the road.

[0019] In combination with the above first aspect, in a possible implementation manner, the trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

[0020] In a second aspect, the present application provides a vehicle control device, which includes: a processing unit; a processing unit for determining a target driving trajectory in at least one driving trajectory based on a target driving demand; at least one driving trajectory is a driving trajectory of a vehicle on a road predicted based on road parameters through a vehicle motion prediction model; the vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state; the processing unit is also used to control the vehicle to drive according to the target driving trajectory.

[0021] In combination with the above-mentioned second aspect, in a possible implementation method, the target driving demand is one or more of the preset driving demands; the preset driving demand includes at least one of the following: the vehicle's driving speed is the highest, the driving time is the shortest, the driving distance is the shortest, the curvature of the driving trajectory is the smallest, the vehicle's driving state during driving is the most stable, and the similarity with the original driving trajectory is the highest at the preset position.

[0022] In conjunction with the above second aspect, in a possible implementation, the processing unit is specifically used to: for each driving trajectory, determine the matching degree of the driving trajectory to the preset driving demand; and determine the driving trajectory with the greatest matching degree as the target driving trajectory. In conjunction with the above second aspect, in a possible implementation, the processing unit is further used to: construct a vehicle motion prediction model based on the vehicle's driving state parameters and motion control parameters.

[0023] In combination with the above-mentioned second aspect, in a possible implementation method, the processing unit is specifically used to: construct a vehicle dynamics model of the vehicle based on the driving state parameters; and construct a vehicle motion prediction model based on the vehicle dynamics model and motion control parameters.

[0024] In combination with the above-mentioned second aspect, in a possible implementation method, the processing unit is specifically used to: determine the drivable range of the vehicle on the road based on road parameters; constrain the vehicle motion prediction model based on the drivable range and trajectory constraints to determine at least one driving trajectory.

[0025] In combination with the above-mentioned second aspect, in a possible implementation method, the device also includes a communication unit; the processing unit is also used to determine the target control amount of each actuator in the vehicle based on the target driving trajectory; the target control amount is the control amount executed by the actuator when the vehicle is driving according to the target driving trajectory; the communication unit is used to send the target control amount to each actuator in the vehicle through the vehicle's chassis wire control execution system, so that each actuator executes the target control amount to control the vehicle to drive according to the target driving trajectory.

[0026] In combination with the above-mentioned second aspect, in a possible implementation method, the processing unit is specifically used to: instruct the communication unit to obtain the real-time motion state of the vehicle; based on the real-time motion state, correct the target driving trajectory through the vehicle motion prediction model to obtain a corrected target driving trajectory; based on the corrected target driving trajectory, determine the target control amount of each actuator in the vehicle.

[0027] In combination with the above second aspect, in a possible implementation manner, the motion control parameter includes at least one of the following: a motor torque of the vehicle and a wheel angle of the vehicle.

[0028] In combination with the above-mentioned second aspect, in a possible implementation method, when the vehicle is equipped with a first motor, a second motor, a third motor, a first steering device and a second steering device, the motion control parameters specifically include at least one of the following: the vehicle's first motor torque, the second motor torque, the third motor torque, the front wheel equivalent angle, the left rear wheel angle and the right rear wheel angle; wherein the first motor is used to drive the vehicle's left front wheel and right front wheel, the second motor is used to drive the vehicle's left rear wheel, and the third motor is used to drive the vehicle's right rear wheel; the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0029] In combination with the above-mentioned second aspect, in a possible implementation method, the driving state parameters include at least one of the following: vehicle position information, longitudinal speed, lateral speed, vehicle heading angle, yaw angular velocity, mass, longitudinal component of force received by each wheel in the ground coordinate system, lateral component of force received by each wheel in the ground coordinate system, vehicle moment of inertia, distance from the center of mass of the vehicle to the front axle, distance from the center of mass of the vehicle to the rear axle, front wheel track, rear wheel track, wheel turning angle, longitudinal force received by the wheel in the wheel coordinate system, and lateral force received by the wheel in the wheel coordinate system.

[0030] In combination with the above second aspect, in a possible implementation manner, the road parameter includes at least one of the following: the position of the center point of the road, the road width at the center point, and the position information of obstacles in the road.

[0031] In combination with the above second aspect, in a possible implementation manner, the trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

[0032] In combination with the above-mentioned second aspect, in a possible implementation method, the preset driving requirements also include at least one of the following: shortest driving time, shortest driving distance, smallest curvature of the driving trajectory, the most stable driving state of the vehicle during driving, and the highest similarity to the original driving trajectory.

[0033] In a third aspect, the present application provides a vehicle, the device comprising: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement a vehicle control method as described in the first aspect and any possible implementation method of the first aspect.

[0034] In combination with the above-mentioned third aspect, in a possible implementation, the vehicle also includes: a first motor, a second motor, and a third motor; wherein the first motor is used to drive the left front wheel and the right front wheel of the vehicle, the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle.

[0035] In combination with the third aspect above, in a possible implementation, the vehicle also includes a first steering device and a second steering device; wherein the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a terminal, the terminal executes a vehicle control method as described in the first aspect and any possible implementation of the first aspect.

[0037] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute a vehicle control method as described in the first aspect and any possible implementation of the first aspect.

[0038] In a sixth aspect, the present application provides a chip, the chip comprising a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement a vehicle control method as described in the first aspect and any possible implementation method of the first aspect.

[0039] Specifically, the chip provided in the present application also includes a memory for storing computer programs or instructions.

[0040] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor of the device, or may be packaged separately from the processor of the device, which is not limited in this application.

[0041] In a seventh aspect, the present application provides a control system for a vehicle, comprising: a vehicle and a data server, wherein the data server is used to execute a control method for the vehicle as described in the first aspect and any possible implementation manner of the first aspect.

[0042] The descriptions of the second to seventh aspects of the present application can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second to seventh aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0043] In this application, the name of the vehicle control device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0044] These and other aspects of the present application will become more apparent from the following description.

[0045] The above scheme brings at least the following beneficial effects: Based on the above technical scheme, the vehicle control method provided by the present application determines the target driving trajectory in at least one driving trajectory based on the target driving demand. Among them, at least one driving trajectory is a driving trajectory of the vehicle on the road predicted based on road parameters through a vehicle motion prediction model, and the vehicle motion prediction model is used to predict the driving trajectory of the vehicle under the motion state. And the vehicle is controlled to drive according to the target driving trajectory. The above technical scheme can determine the appropriate driving trajectory according to different target driving needs, which solves the problem that the current trajectory planning scheme can only plan the vehicle's driving trajectory based on curvature, path distance or based on some constraints, but cannot plan the vehicle trajectory according to specific needs, and has poor applicability, thereby improving the applicability of the vehicle driving trajectory scheme and allowing the vehicle to drive according to the target driving demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a method for optimizing a racing car's driving trajectory provided in an embodiment of the present application;

[0047] Figure 2 A flow chart of a trajectory tracking control method provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of the architecture of a vehicle control system provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of a vehicle motor provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of a vehicle steering device provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of a vehicle control method provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram of the hardware structure of a vehicle provided in an embodiment of the present application;

[0053] Figure 8 A flow chart of a vehicle control method provided in an embodiment of the present application;

[0054] Fig. 9 A flowchart of another vehicle control method provided in an embodiment of the present application;

[0055] Fig.10 A flowchart of another vehicle control method provided in an embodiment of the present application;

[0056] Fig.11A flowchart of another vehicle control method provided in an embodiment of the present application;

[0057] Fig.12 A schematic diagram of another vehicle control method provided in an embodiment of the present application;

[0058] Fig.13 A schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0061] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0062] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0063] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0064] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0065] With the rapid and continuous updating and iteration of science and technology in my country, autonomous driving technology has become a research hotspot in the current automotive industry. The key to vehicle autonomous driving lies in the planning of autonomous driving trajectories at future moments. Therefore, trajectory planning technology in the field of autonomous driving is crucial to achieving safe, efficient, and natural driving behavior.

[0066] Among the related technologies, the trajectory planning technologies currently used in the field of autonomous driving mostly plan the vehicle's trajectory based on curvature, path distance or based on some constraints, such as planning the path with minimum curvature or planning the trajectory with the shortest distance.

[0067] For example, Figure 1 As shown, in a racing car driving trajectory optimization method, the shortest speed curve is determined according to the initial racing car path and the tire adhesion limit constraint; and the path with the minimum curvature is determined according to the shortest speed curve, the tire adhesion limit constraint and the vehicle motion state.

[0068] However, the limitation of this type of trajectory planning scheme is that its trajectory planning process first determines the speed curve with the shortest time consumption, and then determines the path with the minimum curvature based on this, and it is impossible to plan the vehicle trajectory according to specific needs. For example, for the moose test condition, this type of trajectory planning scheme cannot plan the fastest vehicle driving trajectory; or for the emergency lane change condition, this type of trajectory planning scheme also cannot plan the vehicle driving trajectory of other targets.

[0069] Moreover, the relevant trajectory planning and tracking technologies only consider single power drive systems and single front-wheel steering systems. The upper limit of the vehicle state change speed limits the optimization of trajectory planning, making it difficult for the obtained trajectory to meet actual needs. Moreover, the tracking effect is not good for trajectories with faster system state changes, and large deviations are prone to occur.

[0070] For example, Figure 2 As shown, in a trajectory tracking control method, the vehicle's turning angle increment weight at the current moment is determined according to the current lateral speed and the current longitudinal speed of the vehicle at the current moment, and the expected lateral speed and the expected longitudinal speed of the vehicle at the next moment; the vehicle's trajectory tracking error is determined with the turning angle increment as a parameter according to the expected lateral displacement of the vehicle, the expected heading angle of the vehicle, the vehicle's operating state parameters at the current moment, and the turning angle increment weight; the turning angle increment is calculated with the goal of minimizing the trajectory tracking error; the current vehicle turning angle is added to the turning angle increment to obtain the target wheel turning angle of the vehicle at the next moment, so that the vehicle travels according to the target wheel turning angle.

[0071] In the above scheme, the control quantity is only the front wheel angle, and the rear wheel angle control problem is not considered. It is not suitable for vehicles with independent rear wheel control systems.

[0072] In view of this, the vehicle control method provided by the present application determines a target driving trajectory in at least one driving trajectory based on a target driving demand. At least one driving trajectory is a driving trajectory of a vehicle on a road predicted based on road parameters through a vehicle motion prediction model, and the vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state. And, the vehicle is controlled to travel according to the target driving trajectory. The above technical scheme can determine a suitable driving trajectory according to different target driving demands, which solves the problem that the current trajectory planning scheme can only plan the vehicle's driving trajectory based on curvature, path distance or based on some constraints, but cannot plan the vehicle trajectory according to specific needs, and has poor applicability, thereby improving the applicability of the vehicle driving trajectory scheme and allowing the vehicle to travel according to the target driving demand.

[0073] The implementation of the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

[0074] Figure 3 The following is a schematic diagram of the architecture of a vehicle control system provided in an embodiment of the present application. Figure 3 As shown, the control system of the vehicle includes: a vehicle 301 and a data server 302 .

[0075] The vehicle 301 and the data server 302 are connected via a communication link, which may be a wired communication link or a wireless communication link, which is not limited in the present application.

[0076] The following is a detailed introduction to the vehicle 301.

[0077] In one possible implementation, vehicle 301 is used to send location information collected by multiple on-board sensors in vehicle 301 to data server 302, and send location information collected by multiple on-board sensors to data server 302, so that data server 302 can determine the relative position of vehicle 301 on the road based on the location information collected by multiple on-board sensors.

[0078] Optionally, the vehicle 301 is equipped with on-board sensors such as cameras, laser radars, millimeter-wave radars, ultrasonic radars, and inertial measurement units (IMUs). By fusing the information collected by multiple on-board sensors, the relative position of the vehicle 301 on the road can be obtained. Of course, the above is only an exemplary method for obtaining the relative position of the vehicle 301 on the road. The present application can also obtain the relative position of the vehicle 301 on the road in other ways, such as through the data collected by any one or more devices such as the attitude and heading reference system (AHRS), the vertical reference unit (VRU), the global navigation satellite system (GNSS), the global positioning system (GPS), the Beidou navigation satellite system (BDS), or the inertial navigation system (INS), to determine the relative position of the vehicle 301 on the road, and the present application does not limit this.

[0079] Optional, such as Figure 4 As shown, a first motor, a second motor, and a third motor are configured in the vehicle 301. The first motor is used to drive the left front wheel and the right front wheel of the vehicle 301, the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle.

[0080] Optional, such as Figure 5 As shown, a first steering device and a second steering device are also configured in the vehicle 301. The first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0081] In addition, a third steering device is also configured in the vehicle 301. The third steering device is used to control the left front wheel and the right front wheel of the vehicle 301 to turn.

[0082] It should be noted that the independent rear-wheel steering system in the present application can increase the change speed of the vehicle 301, increase the optimization potential of trajectory planning, and make the obtained trajectory more in line with actual needs. At the same time, different lateral forces and yaw moments are applied to the vehicle 301 through the rear-wheel steering device to improve the control effect of the vehicle; and based on the three-motor independent drive system, by notifying the torque of the left rear wheel and the right rear wheel, the vehicle can generate additional yaw moments to further improve the control effect.

[0083] Optionally, the vehicle 301 in the present application is not limited to three drive modes, but may also be a four drive mode or a dual drive mode. Of course, the above is only an exemplary description of the vehicle drive mode. As the number of vehicle wheels increases, the vehicle 301 may also be a five drive mode, a six drive mode, etc. The vehicle drive mode may be proportional to the number of vehicle wheels, and the present application does not limit this.

[0084] The following is a detailed introduction to the vehicle 301 in four-wheel drive modes.

[0085] In a possible implementation, the vehicle 301 in the present application may also be in a four-drive mode. The vehicle 301 may be configured with four motors, for example, a fourth motor, a fifth motor, a second motor, and a third motor.

[0086] Among them, the fourth motor is used to drive the left front wheel of the vehicle 301; the fifth motor is used to drive the right front wheel of the vehicle 301; the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle.

[0087] The following is a detailed introduction to the dual-drive mode vehicle 301 .

[0088] In addition, the vehicle 301 in the present application may also be in a dual-drive mode. The vehicle 301 may also be configured with two motors, for example, a first motor and a sixth motor.

[0089] The first motor is used to drive the left front wheel and the right front wheel of the vehicle 301 , and the sixth motor is used to drive the left rear wheel and the right rear wheel of the vehicle 301 .

[0090] Optionally, the vehicle 301 in the present application is not limited to the three-steering mode, but may also be a four-steering mode or a two-steering mode. Of course, the above is only an exemplary description of the vehicle's steering mode. As the number of vehicle wheels increases, the vehicle 301 may also be a five-steering mode, a six-steering mode, etc. The vehicle's steering mode may be proportional to the number of vehicle wheels, and the present application does not limit this.

[0091] The following is a detailed introduction to the vehicle 301 with four turning modes.

[0092] In a possible implementation, the vehicle 301 in the present application may also be configured with a fourth steering device, a fifth steering device, a first steering device and a second steering device.

[0093] Among them, the fourth steering device is used to control the steering of the left front wheel of the vehicle 301, the fifth steering device is used to control the steering of the right front wheel of the vehicle 301, the first steering device is used to control the steering of the left rear wheel of the vehicle 301, and the second steering device is used to control the steering of the right rear wheel of the vehicle 301.

[0094] The following is a detailed introduction to the dual-steering mode vehicle 301.

[0095] In a possible implementation, the vehicle 301 in the present application may also be configured with a third steering device and a sixth steering device.

[0096] Among them, the third steering device is used to control the steering of the left front wheel and the right front wheel of the vehicle 301; the sixth steering device is used to control the steering of the left rear wheel and the right rear wheel of the vehicle 301.

[0097] The data server 302 is described in detail below.

[0098] In one possible implementation, Figure 6 As shown, the data server 302 is used to receive position information collected by multiple on-board sensors in the vehicle 301, and based on the position information collected by the multiple on-board sensors, determine the relative position of the vehicle 301 on the road through positioning perception technology, and respond to the tester's input operation on vehicle size information and road parameters (for example, test road boundaries) to determine the vehicle's center of mass drivable area.

[0099] Furthermore, the data server 302 is also used to build a vehicle dynamics model based on the vehicle's driving state parameters, and to build a vehicle motion prediction model based on motion control parameters (for example, the vehicle's first motor torque, second motor torque, third motor torque, front wheel equivalent steering angle, left rear wheel angle, and right rear wheel angle, etc.).

[0100] Furthermore, based on the vehicle's centroid drivable area, trajectory constraints, and preset driving requirements, the vehicle motion prediction model is constrained to determine the target driving trajectory. Further, based on the target trajectory, the target control amount of each actuator in the vehicle 301 is determined.

[0101] Optionally, the road parameters (eg, road boundary information, road center point location, road width at the center point, and location information of obstacles in the road, etc.) may also be information preconfigured in the data server 302 .

[0102] When implemented by hardware, each module in the data server 302 can be integrated into Figure 7 The hardware structure of the vehicle shown is implemented. Specifically, Figure 7 As shown, the basic hardware structure of the vehicle is introduced.

[0103] Figure 7 A schematic diagram of the hardware structure of a vehicle provided in an embodiment of the present application. Figure 7As shown, the vehicle includes at least one processor 701, a communication line 702, and at least one communication interface 704, and may also include a memory 703. The processor 701, the memory 703 and the communication interface 704 may be connected via the communication line 702.

[0104] Processor 701 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as: one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA).

[0105] The communication link 702 may include a pathway for transmitting information between the above-mentioned components.

[0106] The communication interface 704 is used to communicate with other devices or communication networks, and can use any transceiver-like device, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0107] The memory 703 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0108] In one possible design, the memory 703 can exist independently of the processor 701, that is, the memory 703 can be a memory outside the processor 701. In this case, the memory 703 can be connected to the processor 701 through the communication line 702, and is used to store execution instructions or application code, and is controlled by the processor 701 to implement the vehicle control method provided in the following embodiment of the present application. In another possible design, the memory 703 can also be integrated with the processor 701, that is, the memory 703 can be the internal memory of the processor 701, for example, the memory 703 is a high-speed cache, which can be used to temporarily store some data and instruction information.

[0109] As a possible implementation, the processor 701 may include one or more CPUs, for example Figure 7 As another possible implementation, the vehicle may include multiple processors, such as Figure 7 As another possible implementation, the vehicle may further include an output device 705 and an input device 706.

[0110] It should be pointed out that the various embodiments of the present application can learn from or refer to each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can all refer to each other without limitation.

[0111] Figure 8 A flow chart of a vehicle control method provided in an embodiment of the present application, which can be applied to Figure 7 In the vehicle shown. Figure 8 As shown, the method includes the following S801-S802.

[0112] S801. Determine a target driving trajectory in at least one driving trajectory based on a target driving demand.

[0113] Optionally, the preset driving requirement may include at least one of the following: the vehicle has the highest driving speed, the shortest driving time, the shortest driving distance, the smallest curvature of the driving trajectory, and the highest average speed at the preset location. Of course, the above is only an exemplary description of the preset driving requirements, and the preset driving requirements may also include other requirements, such as the vehicle's driving state is the most stable during driving and the similarity with the original driving trajectory is the highest, which is not limited in this application.

[0114] It should be noted that the target driving requirement is one or more of the preset driving requirements, which is not limited in this application. For example, the target driving requirement may be that the vehicle's driving speed is the highest at the preset location, the target driving requirement may be that the driving time is the shortest and the driving distance is the shortest, or the target driving requirement may be that the vehicle's driving speed is the highest, the driving time is the shortest, the driving distance is the shortest, and the curvature of the driving track is the smallest at the preset location.

[0115] In a possible implementation, for each driving trajectory, a matching degree of the driving trajectory to the target driving demand is determined; and the driving trajectory with the greatest matching degree is determined as the target driving trajectory.

[0116] The following describes the process of determining the target driving trajectory in different situations through Examples 1 to 3. Example 1: When the target driving requirement is that the vehicle's driving speed is the maximum at a preset position, the target driving trajectory is determined; Example 2: When the target driving requirement is that the average speed is the maximum, the target driving trajectory is determined; Example 3: When the target driving requirement is that the vehicle's driving speed is the maximum at a preset position and the vehicle's driving state is the most stable during driving, the target driving trajectory is determined.

[0117] Example 1: When the target driving demand is that the vehicle's driving speed is the maximum at a preset position, the degree of matching of the driving trajectory to the preset driving demand is the vehicle's driving speed at the preset position; the implementation method of S801 is: for each driving trajectory, determine the vehicle's driving speed at the preset position; determine the driving trajectory with the maximum driving speed as the target driving trajectory.

[0118] For example, in the unmanned elk speed test scenario, for each driving track, determine the 0 ) has the largest speed, that is, maxv(x=x 0 ). Traverse all driving trajectories offline and determine the driving trajectory with the largest driving speed as the target driving trajectory, denoted as ξ r (k).

[0119] For example, the driving trajectory includes driving trajectory A, driving trajectory B, and driving trajectory C. The speed a of the vehicle at the preset position in driving trajectory A, the speed b of the vehicle at the preset position in driving trajectory B, and the speed c of the vehicle at the preset position in driving trajectory C are determined respectively. If speed a>speed b, and speed a>speed c, then the driving trajectory A corresponding to speed a is determined to be the target driving trajectory.

[0120] Example 2: When the target driving demand is the maximum average speed, the matching degree of the driving trajectory to the preset driving demand is the average driving speed of the vehicle during the driving process; the implementation method of S801 is: for each driving trajectory, determine the average driving speed of the vehicle in each driving trajectory. Determine the driving trajectory with the maximum average driving speed as the target driving trajectory.

[0121] Example 3: When the target driving demand is that the vehicle's driving speed is the highest at a preset position and the vehicle's driving state is the most stable during driving, the degree of matching of the driving trajectory to the preset driving demand is the vehicle's driving speed at the preset position and the vehicle's driving state during driving; the implementation method of S801 is: for each driving trajectory, determine the vehicle's driving speed at the preset position and the vehicle's driving state during driving; determine the driving trajectory with the highest driving speed and the most stable driving state at the preset position as the target driving trajectory.

[0122] Optionally, before S801, at least one driving trajectory of the vehicle on the road is predicted based on the road parameters through a vehicle motion prediction model.

[0123] The road parameters include at least one of the following: the position of the center point of the road, the width of the road at the center point, and the position information of obstacles in the road. The vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state.

[0124] In a possible implementation, the process of determining at least one driving trajectory may include: determining a drivable range of the vehicle on the road based on road parameters; constraining the vehicle motion prediction model based on the drivable range and trajectory constraints to determine at least one driving trajectory.

[0125] The trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

[0126] Specifically, the process of determining at least one driving trajectory is described with reference to the embodiment shown in S1003-S1004, which will not be described in detail here.

[0127] S802: Control the vehicle to travel along the target driving trajectory.

[0128] In one possible implementation, based on the target driving trajectory, the target control amount of each actuator in the vehicle is determined, and the target control amount is sent to each actuator in the vehicle through the vehicle's chassis wire control execution system, so that each actuator executes the target control amount to control the vehicle to travel according to the target driving trajectory.

[0129] Specifically, the process of controlling the vehicle to travel along the target driving trajectory refers to the embodiment shown in S1101-S1102, which will not be repeated here.

[0130] Based on the above technical solution, the vehicle control method provided by the present application determines a target driving trajectory in at least one driving trajectory based on the target driving demand. Among them, at least one driving trajectory is a driving trajectory of the vehicle on the road predicted based on road parameters through a vehicle motion prediction model, and the vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state. And, the vehicle is controlled to drive according to the target driving trajectory. The above technical solution can determine a suitable driving trajectory according to different target driving needs, which solves the problem that the current trajectory planning scheme can only plan the vehicle's driving trajectory based on curvature, path distance or based on some constraints, but cannot plan the vehicle trajectory according to specific needs, and has poor applicability, thereby improving the applicability of the vehicle driving trajectory scheme and allowing the vehicle to drive according to the target driving demand.

[0131] As a possible embodiment of the present application, Figure 8 ,like Fig. 9 As shown, before the above S801, the implementation method of building a vehicle motion prediction model can be implemented through the following S901.

[0132] S901: construct a vehicle motion prediction model based on the vehicle's driving state parameters and motion control parameters.

[0133] Among them, the vehicle motion prediction model is used to predict the driving trajectory of the vehicle under the motion state.

[0134] Optionally, the driving state parameters include at least one of the following: vehicle position information, longitudinal speed, lateral speed, body heading angle, yaw angular velocity, mass, longitudinal component of force received by each wheel in the ground coordinate system, lateral component of force received by each wheel in the ground coordinate system, vehicle rotational inertia, distance from the vehicle center of mass to the front axle, distance from the vehicle center of mass to the rear axle, front wheel track, rear wheel track, wheel turning angle, longitudinal force received by the wheel in the wheel coordinate system, and lateral force received by the wheel in the wheel coordinate system.

[0135] Optionally, the motion control parameter includes at least one of the following: a motor torque of the vehicle and a wheel angle of the vehicle.

[0136] Optionally, when the vehicle is configured with a first motor, a second motor, a third motor, a first steering device and a second steering device, the motion control parameters specifically include at least one of the following: the vehicle's first motor torque, second motor torque, third motor torque, front wheel equivalent steering angle, left rear wheel angle and right rear wheel angle.

[0137] Among them, the first motor is used to drive the left front wheel and the right front wheel of the vehicle, the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle; the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0138] In a possible implementation, S901 may be implemented by: constructing a vehicle dynamics model of the vehicle based on the driving state parameters, and constructing a vehicle motion prediction model based on the vehicle dynamics model and motion control parameters.

[0139] Specifically, the specific implementation process of the above S901 refers to the embodiment shown in S1001-S1002, which will not be repeated here.

[0140] It should be noted that the independent rear-wheel steering system in this application can increase the speed of vehicle change, increase the optimization potential of trajectory planning, and make the obtained trajectory more in line with actual needs. At the same time, different lateral forces and yaw moments are applied to the vehicle through the rear-wheel steering device to improve the control effect of the vehicle. Moreover, based on the three-motor independent drive system, the torque of the left rear wheel and the right rear wheel driven by the independent motor can generate additional yaw moment of the vehicle, further improving the control effect.

[0141] Therefore, based on the steering system and independent drive system of the vehicle in the present application, the obtained vehicle motion prediction model can have a higher optimization potential when planning the driving trajectory, thereby making the obtained trajectory more in line with actual needs.

[0142] As a possible embodiment of the present application, Fig. 9 ,like Fig.10 As shown, the implementation method of constructing the vehicle motion prediction model in the above S901 can be implemented through the following S1001-S1002.

[0143] S1001. Construct a vehicle dynamics model of the vehicle based on driving state parameters.

[0144] In one possible implementation, when the vehicle has a single front and dual rear drive (i.e., the vehicle is driven by a first motor, a second motor, and a third motor), and the steering system includes a front-wheel steering device, a first steering device, and a second steering device, a vehicle dynamics model is established based on the vehicle's driving and steering system.

[0145] Optionally, the vehicle dynamics model satisfies the following formula:

[0146]

[0147] Where X is the horizontal coordinate of the vehicle in the ground coordinate system, Y is the vertical coordinate of the vehicle in the ground coordinate system, and v y is the longitudinal velocity of the vehicle, v x is the lateral velocity of the vehicle, φ is the heading angle of the vehicle, ω is the yaw rate, m is the mass of the vehicle, X ij is the longitudinal force on the wheel in the ground coordinate system, X fl is the longitudinal force on the left front wheel in the ground coordinate system, X fr is the longitudinal force on the right front wheel in the ground coordinate system, X rl is the longitudinal force on the left rear wheel in the ground coordinate system, X rr is the longitudinal force on the right rear wheel in the ground coordinate system, y ij is the lateral force on the wheel in the ground coordinate system, Y fl is the lateral force on the left front wheel in the ground coordinate system, Y fr is the lateral force on the right front wheel in the ground coordinate system, Y rl is the lateral force on the left rear wheel in the ground coordinate system, Y rr is the lateral force on the right rear wheel in the ground coordinate system, I z is the vehicle's moment of inertia, a is the distance from the vehicle's center of mass to the front axle, b is the distance from the vehicle's center of mass to the rear axle, t f is the front wheel track, t r is the rear wheel track, δ ij is the wheel angle, F xij The longitudinal force on the wheel in the wheel coordinate system, F yij is the lateral force on the wheel in the wheel coordinate system.

[0148] S1002: construct a vehicle motion prediction model based on the vehicle dynamics model and motion control parameters.

[0149] In one example, motion control parameters are determined based on a drive system of the vehicle and a steering system of the vehicle.

[0150] Among them, the motion control parameters include the first motor torque, the second motor torque, the third motor torque, the front wheel equivalent turning angle, the left rear wheel turning angle and the right rear wheel turning angle of the vehicle.

[0151] Optionally, the motion control parameter u may be expressed as: u = [T f ,T rl ,T rr ,δ f ,δ rl ,δ rr ] T .

[0152] Among them, T fis the first motor torque of the vehicle, T rl is the second motor torque, T rr is the third motor torque, δ f is the front wheel equivalent turning angle, δ rl is the left rear wheel turning angle, δ rr is the right rear wheel turning angle.

[0153] In one example, the vehicle dynamics model in S901 is represented as

[0154] Where x=[X,Y,φ,v x ,v y ,ω] T , represents the driving state variable; Represents the derivative with respect to x.

[0155] Furthermore, the vehicle dynamics model is linearized and discretized, and the motion control parameter u is converted into a wheel control increment to obtain a vehicle motion prediction model.

[0156] For example, the vehicle motion prediction model satisfies the following formula:

[0157]

[0158] Among them, ξ is the vehicle state; is the coefficient matrix, is a constant matrix; Δu is the wheel control increment; k is the moment.

[0159] Optionally, the longitudinal force of the front wheel is determined based on the first motor torque of the vehicle and the front wheel equivalent radius. The front wheel turning angle can be converted to the front wheel equivalent turning angle according to the vehicle design. The tire lateral force can be converted to the side slip angle through the equivalent tire model. The following examples 1-3 introduce that the relationship between the wheel longitudinal force and the motor torque can be established according to the equivalent tire model.

[0160] Specifically, the following Example 1 mainly introduces the process of determining the longitudinal force of the front wheel; Example 2 mainly introduces the process of determining the longitudinal force of the left rear wheel; and Example 3 mainly introduces the process of determining the longitudinal force of the right rear wheel.

[0161] Example 1: Determine the longitudinal force of the front wheels based on the first motor torque of the vehicle and the front wheel equivalent radius.

[0162] It should be noted that, since the left front wheel and the right front wheel are controlled by the same first motor, and the equivalent radius of the left front wheel and the right front wheel is the same, the longitudinal force of the left front wheel is the same as the longitudinal force of the right first motor, and both can be determined by the equivalent radius of the front wheel and the torque of the first motor.

[0163] For example, the front wheel F xf The longitudinal force satisfies the following formula:

[0164]

[0165] Among them, T f is the first motor torque of the vehicle, R f is the equivalent radius of the front wheel.

[0166] Example 2: Determine the longitudinal force of the left rear wheel based on the torque of the second motor of the vehicle and the equivalent radius of the left rear wheel.

[0167] For example, the longitudinal force F of the left rear wheel xrl Satisfies the following formula:

[0168]

[0169] Among them, T rl is the second motor torque, R r is the equivalent radius of the rear wheel.

[0170] Example 3: Determine the longitudinal force of the right rear wheel based on the torque of the vehicle's third motor and the equivalent radius of the right rear wheel.

[0171] For example, the longitudinal force F of the right rear wheel xrr Satisfies the following formula:

[0172]

[0173] Among them, T rr is the third motor torque, R r is the equivalent radius of the rear wheel.

[0174] Based on the above technical solution, a vehicle dynamics model of the vehicle is constructed based on the driving state parameters. Based on the vehicle dynamics model and the motion control parameters, a vehicle motion prediction model is constructed. Based on the above technical solution, a vehicle motion prediction model can be established for the vehicle's drive system and the vehicle's steering system, which is convenient for subsequent planning of the driving trajectory based on the vehicle motion prediction model.

[0175] As a possible embodiment of the present application, Fig.10 As shown, the above-mentioned method for determining at least one driving trajectory can be implemented through the following S1003-S1004.

[0176] S1003. Determine a drivable range of the vehicle on the road based on road parameters.

[0177] In a possible implementation, when the road is a road in a road area where obstacles have been removed, S903 may be implemented as follows: obtaining the position of the center of mass of the vehicle. Establishing a plane rectangular coordinate system with the position of the center of mass of the vehicle as the origin, and performing corresponding coordinate transformation on the road parameters to determine the road parameters in the plane rectangular coordinate system. Based on the road parameters in the plane rectangular coordinate system and the maximum distance between the vehicle boundary and the center of mass in the Y direction when the vehicle heading angle is φ, determine the drivable range of the center of mass of the vehicle in the road.

[0178] Among them, the road parameter can be T t =[X t ,Y t ,W t ].

[0179] Specifically, X t ,Y t Indicates the location of the center point of the road. t is the horizontal coordinate position of the center point of the road, X t ={x t1 ,x t2 ,...,x tn}; Y t Y is the ordinate position of the center point of the road. t ={y t1 ,y t2 ,...,yt n} ; W t Indicates the road width at the center point of the road, W t ={w t1 ,w t2 ,...,w tn}.

[0180] In one example, the drivable range P of the vehicle on the road t Satisfies the following formula:

[0181]

[0182] It should be noted that D((x,y),(x ti ,y ti ),(x ti+1 ,y ti+1 )) means the distance from point (x, y) to line segment (x ti ,y ti ), (x ti+1 ,y ti+1 ); B is the maximum distance between the vehicle boundary and the center of mass in the Y direction when the vehicle heading angle is φ.

[0183] In another possible implementation, S1003 can also be implemented as follows: obtaining the position of the center of mass of the vehicle. A plane rectangular coordinate system is established with the position of the center of mass of the vehicle as the origin, and the road parameters are subjected to corresponding coordinate transformation to determine the position coordinates of each center point in the road and the position coordinates of obstacles in the road in the plane rectangular coordinate system. Based on the position coordinates of each center point in the road and the position coordinates of obstacles in the road in the plane rectangular coordinate system, and the maximum distance of the vehicle boundary relative to the center of mass in the Y direction when the vehicle heading angle is φ, the drivable range of the center of mass of the vehicle in the road is determined.

[0184] S1004: Based on the drivable range and trajectory constraints, constrain the vehicle motion prediction model to determine at least one driving trajectory.

[0185] Specifically, the drivable range is: {x, y, φ}∈P t .

[0186] Among them, x is the horizontal coordinate of the vehicle in the ground coordinate system, y is the vertical coordinate of the vehicle in the ground coordinate system, and φ is the vehicle heading angle.

[0187] Specifically, the trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

[0188] In the following, the vehicle motion state constraint condition is specifically introduced through condition 1; and the motion control parameter increment constraint condition is specifically introduced through condition 2.

[0189] Condition 1: The vehicle motion state constraint can be expressed by stability constraint and control quantity limit constraint. The vehicle motion state constraint can be expressed by the following formula:

[0190] ξ min ≤ξ(k)≤ξ max

[0191] Among them, ξ min is the minimum value of each parameter in the preset vehicle state quantity, ξ max It is the maximum value of each parameter in the preset vehicle state quantity.

[0192] It should be noted that when each parameter in the vehicle state quantity reaches the minimum value of each parameter in the preset vehicle state quantity, the vehicle meets the stability constraint and is in a stable driving state. When each parameter in the vehicle state quantity reaches the maximum value of each parameter in the preset vehicle state quantity, the vehicle meets the control quantity limit constraint and is in a driving limit state; if the maximum value of each parameter is exceeded, the vehicle is at risk of losing control.

[0193] Condition 2: The motion control parameter increment constraint condition is mainly determined by the increment limit constraint. For example:

[0194] ΔU min ≤ΔU(k)≤ΔU max

[0195] Among them, ΔU min is the minimum value of the motion control parameter increment, ΔU max The maximum value of the motion control parameter increment.

[0196] Based on the above technical solution, the drivable range of the vehicle on the road is determined based on the road parameters. Based on the drivable range and the trajectory constraint conditions, the vehicle motion prediction model is constrained to determine at least one driving trajectory. The above technical solution can determine at least one driving trajectory through road constraints and trajectory constraints.

[0197] As a possible embodiment of the present application, Figure 8 ,like Fig.11 As shown, the process of controlling the vehicle to travel according to the target driving trajectory in the above S802 can be implemented through the following S1101-S1102.

[0198] S1101. Determine a target control amount of each actuator in the vehicle based on a target driving trajectory.

[0199] The target control amount is the control amount performed by the actuator when the vehicle is traveling according to the target driving trajectory.

[0200] In a possible implementation, the implementation process of S1101 can be implemented by following steps 1 to 3.

[0201] Step 1: Get the real-time motion status of the vehicle.

[0202] In one example, based on vehicle information collected by various on-board sensors configured in the vehicle (for example, inertial measurement unit, on-board camera, millimeter-wave radar, ultrasonic radar, lidar, etc.), the real-time motion state of the vehicle is determined through multi-sensor information fusion and a vehicle state estimation module.

[0203] It should be noted that during the actual driving process of the vehicle, the vehicle may deviate from the target signal driving trajectory. Therefore, based on the real-time motion state of the vehicle, it is possible to monitor whether the vehicle deviates from the target driving trajectory.

[0204] Step 2: Based on the real-time motion state, the target driving trajectory is corrected through the vehicle motion prediction model to obtain a corrected target driving trajectory.

[0205] In one example, the target driving trajectory and the motion state are sent to a vehicle motion prediction model so that the vehicle motion prediction model can correct the target driving trajectory based on the real-time motion state to improve the accuracy of the vehicle during driving.

[0206] It should be noted that, based on the real-time motion state, the target driving trajectory can be corrected in a timely manner when the vehicle deviates from the target driving trajectory.

[0207] Step 3: Based on the corrected target driving trajectory, determine the target control amount of each actuator in the vehicle.

[0208] In one example, based on the objective function and the corrected target driving trajectory, the target control amount of each actuator in the vehicle is determined through optimal quadratic programming.

[0209] Optionally, the objective function is determined by the trajectory tracking accuracy and the control amount cost. For example, the objective function J can be:

[0210]

[0211] Among them, N p is the prediction step length, Q and R are weight coefficients.

[0212] S1102. Sending a target control amount to each actuator in the vehicle through the chassis wire control execution system of the vehicle, so that each actuator executes the target control amount to control the vehicle to travel according to the target driving trajectory.

[0213] In one example, a target control quantity is sent to the vehicle's chassis wire-controlled execution system, and the chassis control system sends corresponding target control quantities to each actuator of the vehicle (e.g., each motor or each steering device) so that each actuator can control the vehicle based on the target control quantity.

[0214] Further, S1101-S1102 are repeatedly executed until the vehicle reaches the test end point.

[0215] For example, Fig.12 As shown, based on the target driving trajectory and the real-time motion state of the vehicle fed back by the vehicle state estimation module in real time, the vehicle motion prediction model updates and corrects the target control amount executed by each actuator, and sends the updated and corrected target control amount to the chassis wire control execution system of the vehicle, so that the chassis wire control execution system of the vehicle (first motor control module, second motor control module, third motor control module, third steering device control module, first steering device control module and second steering device control module) sends the updated and corrected target control amount to each actuator in the vehicle, so that each actuator can control the vehicle based on the target control amount.

[0216] Among them, the vehicle state estimation module also provides real-time feedback of the vehicle's real-time motion state to the vehicle's chassis wire control execution system.

[0217] Based on the above technical solution, the target control amount of each actuator in the vehicle is determined based on the target driving trajectory. The target control amount is sent to each actuator in the vehicle through the chassis wire control execution system of the vehicle, so that each actuator executes the target control amount. The above technical solution can make each actuator execute the target control amount and control the vehicle to travel along the target driving trajectory.

[0218] The embodiment of the present application can divide the vehicle into functional modules or functional units according to the above method example. For example, each functional module or functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic, which is only a logical function division. There may be other division methods in actual implementation.

[0219] like Fig.13 , which is a schematic diagram of the structure of a vehicle control device 130 provided in an embodiment of the present application, the vehicle control device 130 includes: a communication unit 1301 and a processing unit 1302 .

[0220] The processing unit 1302 is used to predict at least one driving trajectory of the vehicle on the road based on the road parameters through a vehicle motion prediction model; the vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state.

[0221] The processing unit 1302 is further configured to determine a target driving trajectory in at least one driving trajectory based on a preset driving requirement.

[0222] In one possible implementation, the preset driving requirements include at least one of the following: the vehicle's driving speed is the highest, the driving time is the shortest, the driving distance is the shortest, the curvature of the driving trajectory is the smallest, the vehicle's driving state during driving is the most stable, and the similarity with the original driving trajectory is the highest at the preset position.

[0223] In one possible implementation, when the preset driving requirement is that the vehicle's driving speed is the maximum at a preset position, the processing unit 1302 is specifically used to: determine the vehicle's driving speed at the preset position for each driving trajectory; and determine the driving trajectory with the maximum driving speed as the target driving trajectory.

[0224] In a possible implementation, the processing unit 1302 is further configured to construct a vehicle motion prediction model based on the vehicle's driving state parameters and motion control parameters.

[0225] In a possible implementation, the processing unit 1302 is specifically configured to: construct a vehicle dynamics model of the vehicle based on the driving state parameters; and construct a vehicle motion prediction model based on the vehicle dynamics model and the motion control parameters.

[0226] In a possible implementation, the processing unit 1302 is specifically used to: determine the drivable range of the vehicle on the road based on the road parameters; constrain the vehicle motion prediction model based on the drivable range and trajectory constraints to determine at least one driving trajectory.

[0227] In one possible implementation, the processing unit 1302 is also used to determine the target control amount of each actuator in the vehicle based on the target driving trajectory; the target control amount is the control amount performed by the actuator when the vehicle is traveling according to the target driving trajectory; the communication unit 1301 is used to send the target control amount to each actuator in the vehicle through the vehicle's chassis wire control execution system, so that each actuator executes the target control amount.

[0228] In one possible implementation, the processing unit 1302 is specifically used to: instruct the communication unit to obtain the real-time motion state of the vehicle; based on the real-time motion state, correct the target driving trajectory through the vehicle motion prediction model to obtain a corrected target driving trajectory; based on the corrected target driving trajectory, determine the target control amount of each actuator in the vehicle.

[0229] In a possible implementation manner, the motion control parameter includes at least one of the following: a motor torque of the vehicle and a wheel angle of the vehicle.

[0230] In one possible implementation, when a vehicle is configured with a first motor, a second motor, a third motor, a first steering device and a second steering device, the motion control parameters specifically include at least one of the following: the vehicle's first motor torque, the second motor torque, the third motor torque, the front wheel equivalent angle, the left rear wheel angle and the right rear wheel angle; wherein the first motor is used to drive the vehicle's left front wheel and right front wheel, the second motor is used to drive the vehicle's left rear wheel, and the third motor is used to drive the vehicle's right rear wheel; the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

[0231] In a possible implementation, the driving state parameters include at least one of the following: vehicle position information, longitudinal speed, lateral speed, vehicle heading angle, yaw angular velocity, mass, longitudinal component of force received by each wheel in the ground coordinate system, lateral component of force received by each wheel in the ground coordinate system, vehicle moment of inertia, distance from the center of mass of the vehicle to the front axle, distance from the center of mass of the vehicle to the rear axle, front wheel track, rear wheel track, wheel turning angle, longitudinal force received by the wheel in the wheel coordinate system, and lateral force received by the wheel in the wheel coordinate system.

[0232] In a possible implementation manner, the road parameter includes at least one of the following: the position of the center point of the road, the road width at the center point, and the position information of obstacles in the road.

[0233] In a possible implementation manner, the trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

[0234] In a possible implementation, the vehicle control device 130 may further include a storage unit 1303 ( Fig.13 The storage unit 1303 stores a program or instruction. When the processing unit 1302 executes the program or instruction, the control device 130 of the vehicle can execute the vehicle control method described in the above method embodiment.

[0235] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0236] An embodiment of the present application provides a computer program product including instructions. When the computer program product is run on a computer, the computer is enabled to execute the vehicle control method in the above method embodiment.

[0237] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the vehicle control method in the method flow shown in the above method embodiment.

[0238] Among them, the computer readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0239] Since the vehicle, computer-readable storage medium, and computer program product in the embodiments of the present application can be applied to the above-mentioned method, the technical effects that can be obtained can also refer to the above-mentioned method embodiments, and the embodiments of the present application will not be repeated here.

[0240] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0241] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0242] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0243] 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 multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] 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.

[0245] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: Based on the target driving demand, determining a target driving trajectory in at least one driving trajectory; the at least one driving trajectory is a driving trajectory of the vehicle on the road predicted based on road parameters by a vehicle motion prediction model; the vehicle motion prediction model is used to predict the driving trajectory of the vehicle in a moving state; The vehicle is controlled to travel according to the target driving trajectory.

2. The method according to claim 1, characterized in that The target driving requirement is one or more of the preset driving requirements; the preset driving requirement includes at least one of the following: At the preset position, the vehicle has the highest driving speed, the shortest driving time, the shortest driving distance, the smallest curvature of the driving trajectory, the most stable driving state during driving, and the highest similarity to the original driving trajectory.

3. The method according to claim 1, characterized in that The determining, based on the target driving demand, a target driving trajectory in the at least one driving trajectory comprises: For each driving trajectory, determining a matching degree of the driving trajectory to the target driving demand; The driving trajectory with the greatest matching degree is determined as the target driving trajectory.

4. The method according to claim 1, characterized in that: The method further comprises: The vehicle motion prediction model is constructed based on the vehicle's driving state parameters and motion control parameters.

5. The method according to claim 4, characterized in that The vehicle motion prediction model is constructed based on the vehicle's driving state parameters and motion control parameters, including: constructing a vehicle dynamics model of the vehicle based on the driving state parameters; The vehicle motion prediction model is constructed based on the vehicle dynamics model and the motion control parameters.

6. The method according to claim 1, characterized in that The step of determining the at least one driving trajectory comprises: Based on the road parameters, determining a drivable range of the vehicle on the road; Based on the drivable range and trajectory constraints, the vehicle motion prediction model is constrained to determine the at least one driving trajectory.

7. The method according to claim 1, characterized in that The controlling the vehicle to travel according to the target travel trajectory includes: Based on the target driving trajectory, determining a target control amount of each actuator in the vehicle; the target control amount is a control amount performed by the actuator when the vehicle travels according to the target driving trajectory; The target control amount is sent to each actuator in the vehicle through the chassis wire control execution system of the vehicle, so that each actuator executes the target control amount to control the vehicle to travel according to the target driving trajectory.

8. The method according to claim 7, characterized in that The step of determining a target control amount of each actuator in the vehicle based on the target driving trajectory includes: Acquiring the real-time motion status of the vehicle; Based on the real-time motion state, the target driving trajectory is corrected by the vehicle motion prediction model to obtain a corrected target driving trajectory; Based on the corrected target driving trajectory, a target control amount of each actuator in the vehicle is determined.

9. The method according to any one of claims 1 to 8, characterized in that The motion control parameter includes at least one of the following: The motor torque of the vehicle and the wheel angle of the vehicle.

10. The method according to claim 9, characterized in that In the case where the vehicle is equipped with a first motor, a second motor, a third motor, a first steering device, and a second steering device, the motion control parameter specifically includes at least one of the following: a first motor torque, a second motor torque, a third motor torque, a front wheel equivalent turning angle, a left rear wheel turning angle, and a right rear wheel turning angle of the vehicle; Among them, the first motor is used to drive the left front wheel and the right front wheel of the vehicle, the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle; the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

11. The method according to any one of claims 1 to 8, characterized in that The driving state parameter includes at least one of the following: The vehicle's position information, longitudinal speed, lateral speed, body heading angle, yaw angular velocity, mass, longitudinal component of force received by each wheel in the ground coordinate system, lateral component of force received by each wheel in the ground coordinate system, vehicle moment of inertia, distance from the vehicle's center of mass to the front axle, distance from the vehicle's center of mass to the rear axle, front wheel track, rear wheel track, wheel turning angle, longitudinal force received by the wheel in the wheel coordinate system, and lateral force received by the wheel in the wheel coordinate system.

12. The method according to any one of claims 1 to 8, characterized in that The road parameters include at least one of the following: The position of the center point of the road, the width of the road at the center point, and the position information of obstacles in the road.

13. The method according to claim 6, characterized in that The trajectory constraint condition includes at least one of the following: a vehicle motion state constraint condition and a motion control parameter increment constraint condition.

14. A vehicle, characterized in that: include: A processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the vehicle control method as described in any one of claims 1-13.

15. The vehicle according to claim 14, characterized in that The vehicle further comprises: a first motor, a second motor, and a third motor; The first motor is used to drive the left front wheel and the right front wheel of the vehicle, the second motor is used to drive the left rear wheel of the vehicle, and the third motor is used to drive the right rear wheel of the vehicle.

16. The vehicle according to claim 14 or 15, characterized in that The vehicle also includes a first steering device and a second steering device; Wherein, the first steering device is used to control the steering of the left rear wheel, and the second steering device is used to control the steering of the right rear wheel.

17. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions. When a computer executes the instructions, the computer executes the vehicle control method according to any one of claims 1 to 13.

18. A computer program product, characterized in that The computer program product comprises computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the vehicle control method according to any one of claims 1 to 13.

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