WO2022062825A1

WO2022062825A1 - Vehicle control method, device, and vehicle

Info

Publication number: WO2022062825A1
Application number: PCT/CN2021/114741
Authority: WO
Inventors: 张晓毓
Original assignee: 华为技术有限公司
Priority date: 2020-09-23
Filing date: 2021-08-26
Publication date: 2022-03-31
Also published as: CN114248794A

Abstract

A self-driving method, comprises: acquiring the motion state of an obstacle within a preset range; respectively inputting the motion state of the obstacle into an artificial intelligence (AI) model and a potential energy function, and determining a first motion state and a second motion state of a vehicle, wherein the potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating a collision potential energy between the obstacle and the vehicle, wherein the greater the collision potential energy, the greater the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision, the first motion state being the movement speed and the moving direction of the vehicle calculated on the basis of the AI model, and the second motion state being the movement speed and the moving direction of the vehicle calculated on the basis of a potential energy function model; and determining a target motion state of the vehicle on the basis of the first motion state and/or the second motion state, the target motion state comprising a target movement speed and a target moving direction of the vehicle. Said method improves the safety of adjusting the movement speed and the moving direction of a vehicle.

Description

Vehicle control method, device and vehicle

This application claims the priority of the Chinese patent application with the application number 202011007408.3 and the application title "Control Method, Device and Vehicle for Vehicles" filed with the State Intellectual Property Office of China on September 23, 2020, the entire contents of which are incorporated by reference in in this application.

technical field

The present application relates to the field of autonomous driving, and more particularly, to a vehicle control method, device, and vehicle.

Background technique

Artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that responds in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theory.

Autopilot is a mainstream application in the field of artificial intelligence. Autopilot technology relies on the cooperation of computer vision, radar, monitoring devices and global positioning systems to allow motor vehicles to achieve autonomous driving without the need for human active operation. Autonomous vehicles use various computing systems to help transport passengers from one location to another. Some autonomous vehicles may require some initial or continuous input from an operator, such as a pilot, driver, or passenger. An autonomous vehicle permits the operator to switch from a manual mode of operation to a self-driving mode or a mode in between. Since automatic driving technology does not require humans to drive motor vehicles, it can theoretically effectively avoid human driving errors, reduce the occurrence of traffic accidents, and improve the efficiency of highway transportation. Therefore, autonomous driving technology is getting more and more attention.

At present, the motion state of obstacles around the vehicle can be input into the AI model to obtain the speed and direction of movement of the vehicle through the AI model, so as to adjust the speed and direction of movement of the vehicle to avoid collision between the vehicle and obstacles around the vehicle . However, in the above scheme of using the AI model to control the moving speed and direction of the vehicle, due to the uninterpretability and unpredictability of the AI model, the safety of the controlled vehicle cannot be satisfied.

SUMMARY OF THE INVENTION

The present application provides a vehicle control method, device and vehicle, so as to improve the safety of adjusting the target movement speed and target movement direction of the vehicle.

In a first aspect, a control scheme for a vehicle is provided, including: collecting the motion state of an obstacle within a preset range; inputting the motion state of the obstacle into an artificial intelligence AI model and a potential energy function, respectively, to determine the first motion of the vehicle. state and a second motion state, wherein the potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating the collision potential energy between the obstacle and the vehicle, the greater the collision potential energy, the more The higher the possibility of the collision, the smaller the potential energy of the collision, the smaller the possibility of the collision, the first motion state is the motion speed and direction of the vehicle calculated based on the AI model, and the first motion state is The second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model; based on the first motion state and/or the second motion state, a target motion state of the vehicle is determined, and the target motion state is The state includes the target movement speed and target movement direction of the vehicle.

In the embodiment of the present application, the motion state of the obstacle is input into the AI model and the potential energy function respectively to obtain the first motion state and the second motion state, and the target motion state of the vehicle is determined based on the first motion state and the second motion state , so as to avoid that the target motion state of the vehicle can only be determined based on the AI model in the prior art, which is beneficial to improve the safety of determining the target motion state of the vehicle.

On the other hand, in the embodiment of the present application, determining the second motion state of the vehicle based on the potential energy function is beneficial to improve the accuracy of determining the target motion state, and avoids the prior art from determining the vehicle's motion state only based on the relative distance between the vehicle and the obstacle. target motion state.

In a possible implementation manner, the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to the relative speed between the obstacle and the vehicle .

In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is inversely related to the relative distance between the obstacle and the vehicle, and is positively related to the relative speed between the obstacle and the vehicle, which is beneficial to measure the obstacle and the vehicle. the possibility of a collision between them.

Optionally, the above-mentioned relative speed may be a component of the relative speed between the obstacle and the vehicle in the direction of travel of the vehicle.

In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is positively correlated with the component of the relative speed in the traveling direction of the vehicle, which is beneficial to improve the accuracy of the possibility of collision between the obstacle and the vehicle.

In a possible implementation manner, the determining the target motion state of the vehicle based on the first motion state and/or the second motion state includes: if the relative relationship between the obstacle and the vehicle is If the distance is less than the first preset distance, and/or the relative speed between the obstacle and the vehicle is higher than the first preset speed, then the target motion state of the vehicle is determined based on the second motion state; and /or, if the relative distance between the obstacle and the vehicle is greater than the second preset distance, and/or the relative speed between the obstacle and the vehicle is lower than the second preset speed, based on The first motion state determines the target motion state of the vehicle, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed .

In this embodiment of the present application, if the relative distance between the obstacle and the vehicle is less than the first preset distance, and/or the relative speed between the obstacle and the vehicle is higher than the first preset speed, that is, in a more emergency situation In this case, the target motion state of the vehicle can be determined based on the second motion state determined by the potential energy function, which is beneficial to improve the safety of determining the target motion state of the vehicle.

If the relative distance between the obstacle and the vehicle is greater than the second preset distance, and/or the relative speed between the obstacle and the vehicle is lower than the second preset speed, that is, in a non-emergency situation, it can be determined based on the AI model The first motion state of the vehicle determines the target motion state of the vehicle, which is beneficial to reduce the time for determining the target motion state of the vehicle.

In a possible implementation manner, inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle includes: based on the first objective function min(Δf), and Δf=(ff ₀ ) <0, determine the second motion state of the vehicle, where f ₀ represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle .

In the embodiment of the present application, based on the first objective function min(Δf), and Δf=(ff ₀ )<0, the second motion state of the vehicle is determined, so that the change between the current collision potential energy and the adjusted collision potential energy is relatively small. Small, that is to say, the movement speed of the vehicle changes is smaller, and the movement angle of the vehicle changes is smaller, so as to improve the comfort of the passengers.

In a possible implementation manner, inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle includes: determining the vehicle based on a second objective function min(f<F ₁ ). The second motion state of , wherein F ₁ represents the preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

In the embodiment of the present application, based on the second objective function min (f<F ₁ ), the second motion state of the vehicle is determined, so that the adjusted collision potential energy is smaller than the preset first collision potential energy threshold, which is beneficial to improve the determination of the vehicle's The security of the target motion state.

In a possible implementation, the potential energy function is

Wherein, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

In a second aspect, a method for controlling a vehicle is provided, comprising: calculating a relative speed and a relative distance between an obstacle and a vehicle; calculating the relationship between the obstacle and the vehicle based on the relative speed, the relative distance, and a potential energy function Collision potential energy between vehicles, wherein the collision potential energy indicates the possibility of collision between the obstacle and the vehicle, the higher the collision potential energy is, the higher the collision probability is, and the smaller the collision potential energy is, the higher the collision potential energy is. The possibility of the collision is smaller; based on the first objective function min(Δf), and Δf=(ff ₀ )<0, determine the second motion state of the vehicle; or based on the second objective function min(f<F ₁ ), determine the second motion state of the vehicle, where f ₀ represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted, F ₁ represents a preset first collision potential energy threshold.

In the embodiment of the present application, determining the second motion state of the vehicle based on the potential energy function is beneficial to improve the accuracy of determining the target motion state, and avoids determining the target motion state of the vehicle only based on the relative distance between the vehicle and the obstacle in the prior art .

On the other hand, in the embodiment of the present application, based on the first objective function min(Δf), and Δf=(ff ₀ )<0, the second motion state of the vehicle is determined, so that there is a difference between the current collision potential energy and the adjusted collision potential energy The change of the vehicle is smaller, that is to say, the movement speed of the vehicle changes is smaller, and the movement angle of the vehicle change is smaller, so as to improve the comfort of the passengers. Based on the second objective function min (f<F ₁ ), the second motion state of the vehicle is determined so that the adjusted collision potential energy is smaller than the preset first collision potential energy threshold, which is beneficial to improve the safety of determining the target motion state of the vehicle.

In a possible implementation manner, the determining the second motion state of the vehicle based on the first objective function min(Δf) and Δf=(ff ₀ )<0 includes: in the comfort mode, based on the The first objective function min(Δf), and Δf=(ff ₀ )<0, determines the second motion state of the vehicle.

In the embodiment of the present application, in the comfort mode, based on the first objective function min(Δf), and Δf=(ff ₀ )<0, the second motion state of the vehicle is determined, so that the current collision potential energy and the adjusted collision potential energy are The change between them is small, that is to say, the movement speed changed by the vehicle is smaller, and the movement angle changed by the vehicle is smaller, so as to improve the comfort of the passengers.

It should be noted that the above-mentioned comfort mode may be input by the driver, or may be selected by the controller in the vehicle based on the current road conditions, which is not limited in this embodiment of the present application.

In a possible implementation manner, the method further includes: in a safe mode, based on a second objective function min(f<F ₁ ), determining a second motion state of the vehicle, wherein F ₁ represents a preset The first collision potential energy threshold of , f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

In the embodiment of the present application, in the safe mode, the second motion state of the vehicle is determined based on the second objective function min (f<F ₁ ), so that the adjusted collision potential energy is smaller than the preset first collision potential energy threshold, there are It is beneficial to improve the safety of determining the target motion state of the vehicle.

It should be noted that the above-mentioned safety mode may be input by the driver, or may be selected by the controller in the vehicle based on the current road conditions, which is not limited in this embodiment of the present application.

In a third aspect, a control device for a vehicle is provided, and the device has the function of implementing the device in the method design of the first aspect. These functions can be implemented by hardware or by executing corresponding software by hardware. The hardware or software includes one or more units corresponding to the above functions.

In a fourth aspect, a control device for a vehicle is provided, and the device has the function of implementing the device in the method design of the second aspect. These functions can be implemented by hardware or by executing corresponding software by hardware. The hardware or software includes one or more units corresponding to the above functions.

In a fifth aspect, a computing device is provided, including an input-output interface, a processor, and a memory. The processor is used to control the input and output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the method in the first aspect.

In a sixth aspect, a computing device is provided, including an input-output interface, a processor, and a memory. The processor is used to control the input and output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the method in the second aspect.

In a seventh aspect, a computer program product is provided, the computer program product comprising: computer program code, which, when the computer program code is run on a computer, causes the computer to perform the methods in the above aspects.

In an eighth aspect, a computer-readable medium is provided, and the computer-readable medium stores program codes, which, when executed on a computer, cause the computer to execute the methods in the above-mentioned aspects.

In a ninth aspect, a chip system is provided, the chip system includes a processor for a computing device to implement the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data involved in the above methods and/or or information. In a possible design, the chip system further includes a memory for storing necessary program instructions and data of the computing device. The chip system may be composed of chips, or may include chips and other discrete devices.

In a tenth aspect, a vehicle is provided, including an input-output interface, a processor and a memory. The processor is used to control the input and output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the methods in the above aspects.

Optionally, the above-mentioned vehicle may have an automatic driving function.

Description of drawings

FIG. 1 is a functional block diagram of a vehicle 100 to which the embodiments of the present application are applied.

FIG. 2 is a schematic diagram of an applicable automatic driving system according to an embodiment of the present application.

FIG. 3 is a flowchart of a vehicle control method according to an embodiment of the present application.

FIG. 4 is a schematic diagram of the architecture of an intelligent driving system according to another embodiment of the present application.

FIG. 5 is a flowchart of control of a vehicle according to another embodiment of the present application.

FIG. 6 is a schematic diagram of a relationship between a vehicle and an obstacle in a coordinate system according to an embodiment of the present application.

FIG. 7 is a schematic diagram of a motion state between a vehicle and an obstacle in a coordinate system according to an embodiment of the present application.

FIG. 8 is a schematic diagram of a collision risk level according to an embodiment of the present application.

FIG. 9 is a schematic diagram of an interaction system according to an embodiment of the present application.

FIG. 10 is a schematic diagram of a control device of a vehicle according to an embodiment of the present application.

FIG. 11 is a schematic diagram of a control device of a vehicle according to an embodiment of the present application.

FIG. 12 is a schematic block diagram of a computing device according to another embodiment of the present application.

detailed description

The technical solutions in the present application will be described below with reference to the accompanying drawings. For ease of understanding, the following describes a scenario to which the embodiments of the present application are applicable by taking a scenario of intelligent driving as an example with reference to FIG. 1 .

FIG. 1 is a functional block diagram of a vehicle 100 provided by an embodiment of the present application. In one embodiment, the vehicle 100 is configured in a fully or partially autonomous driving mode. For example, the vehicle 100 can control itself while in an autonomous driving mode, and can determine the current state of the vehicle and its surroundings through human manipulation, determine the likely behavior of at least one other vehicle in the surrounding environment, and determine the other vehicle The vehicle 100 is controlled based on the determined information with a confidence level corresponding to the likelihood of performing the possible behavior. When the vehicle 100 is in an autonomous driving mode, the vehicle 100 may be placed to operate without human interaction.

Vehicle 100 may include various subsystems, such as travel system 102 , sensor system 104 , control system 106 , one or more peripherals 108 and power supply 110 , computer system 112 , and user interface 116 . Alternatively, vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. Additionally, each of the subsystems and elements of the vehicle 100 may be interconnected by wire or wirelessly.

The travel system 102 may include components that provide powered motion for the vehicle 100 . In one embodiment, travel system 102 may include engine 118 , energy source 119 , transmission 120 , and wheels/tires 121 . The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a gasoline engine and electric motor hybrid engine, an internal combustion engine and an air compression engine hybrid engine. Engine 118 converts energy source 119 into mechanical energy.

Examples of energy sources 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 119 may also provide energy to other systems of the vehicle 100 .

Transmission 120 may transmit mechanical power from engine 118 to wheels 121 . Transmission 120 may include a gearbox, a differential, and a driveshaft. In one embodiment, transmission 120 may also include other devices, such as clutches. Among other things, the drive shaft may include one or more axles that may be coupled to one or more wheels 121 .

The sensor system 104 (also known as "collection device") may include several sensors that sense information about the environment surrounding the vehicle 100 . For example, the sensor system 104 may include a positioning system 122 (the positioning system may be a global positioning system (GPS) system, a Beidou system or other positioning systems), an inertial measurement unit (IMU) 124, Radar 126 , laser rangefinder 128 and camera 130 . The sensor system 104 may also include sensors of the internal systems of the vehicle 100 being monitored (eg, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, orientation, velocity, etc.). This detection and identification is a critical function for the safe operation of the autonomous vehicle 100 .

The positioning system 122 may be used to estimate the geographic location of the vehicle 100 . The IMU 124 is used to sense position and orientation changes of the vehicle 100 based on inertial acceleration. In one embodiment, IMU 124 may be a combination of an accelerometer and a gyroscope.

Radar 126 may utilize radio signals to sense objects within the surrounding environment of vehicle 100 . In some embodiments, in addition to sensing the target, the radar 126 may also be used to sense one or more of the target's speed, position, and heading.

The laser rangefinder 128 may utilize laser light to sense objects in the environment in which the vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.

Camera 130 may be used to capture multiple images of the surrounding environment of vehicle 100 . Camera 130 may be a still camera or a video camera.

The control system 106 controls the operation of the vehicle 100 and its components. Control system 106 may include various elements including steering system 132 , throttle 134 , braking unit 136 , computer vision system 140 , route control system 142 , and obstacle avoidance system 144 .

The steering system 132 is operable to adjust the heading of the vehicle 100 . For example, in one embodiment it may be a steering wheel system.

The throttle 134 is used to control the operating speed of the engine 118 and thus the speed of the vehicle 100 .

The braking unit 136 is used to control the deceleration of the vehicle 100 . The braking unit 136 may use friction to slow the wheels 121 . In other embodiments, the braking unit 136 may convert the kinetic energy of the wheels 121 into electrical current. The braking unit 136 may also take other forms to slow the wheels 121 to control the speed of the vehicle 100 .

Computer vision system 140 may be operable to process and analyze images captured by camera 130 in order to identify objects and/or features in the environment surrounding vehicle 100 . The objects and/or features may include traffic signals, road boundaries and obstacles. Computer vision system 140 may use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 140 may be used to map the environment, track objects, estimate the speed of objects, and the like.

The route control system 142 is used to determine the travel route of the vehicle 100 . In some embodiments, route control system 142 may combine data from sensors, GPS 122, and one or more predetermined maps to determine a driving route for vehicle 100.

The obstacle avoidance system 144 is used to identify, evaluate and avoid or otherwise overcome potential obstacles in the environment of the vehicle 100 .

Of course, in one example, the control system 106 may additionally or alternatively include components other than those shown and described. Alternatively, some of the components shown above may be reduced.

Vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripheral devices 108 . Peripherals 108 may include a wireless communication system 146 , an onboard computer 148 , a microphone 150 and/or a speaker 152 .

In some embodiments, peripherals 108 provide a means for a user of vehicle 100 to interact with user interface 116 . For example, the onboard computer 148 may provide information to the user of the vehicle 100 . User interface 116 may also operate on-board computer 148 to receive user input. The onboard computer 148 can be operated via a touch screen. In other cases, peripheral devices 108 may provide a means for vehicle 100 to communicate with other devices located within the vehicle. For example, microphone 150 may receive audio (eg, voice commands or other audio input) from a user of vehicle 100 . Similarly, speakers 152 may output audio to a user of vehicle 100 .

Wireless communication system 146 may wirelessly communicate with one or more devices, either directly or via a communication network. For example, wireless communication system 146 may use 3G cellular communications, such as code division multiple access (CDMA), Global System for Mobile Communications (GSM)/GPRS, or fourth generation (4th generation, 4G) communications such as LTE. Or the fifth generation (5th-Generation, 5G) communication. The wireless communication system 146 may communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system 146 may communicate directly with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, wireless communication system 146 may include one or more dedicated short range communications (DSRC) devices, which may include communication between vehicles and/or roadside stations public and/or private data communications.

The power supply 110 may provide power to various components of the vehicle 100 . In one embodiment, the power source 110 may be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such a battery may be configured as a power source to provide power to various components of the vehicle 100 . In some embodiments, power source 110 and energy source 119 may be implemented together, such as in some all-electric vehicles.

Some or all of the functions of the vehicle 100 are controlled by the computer system 112 . Computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer readable medium such as data memory 114 . Computer system 112 may also be multiple computing devices that control individual components or subsystems of vehicle 100 in a distributed fashion.

The processor 113 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor may be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Although FIG. 1 functionally illustrates the processor, memory, and other elements of the computer 110 in the same block, one of ordinary skill in the art will understand that the processor, computer, or memory may actually include a processor, a computer, or a memory that may or may not Multiple processors, computers, or memories stored within the same physical enclosure. For example, the memory may be a hard drive or other storage medium located within an enclosure other than computer 110 . Thus, reference to a processor or computer will be understood to include reference to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering and deceleration components may each have their own processor that only performs computations related to component-specific functions .

In various aspects described herein, a processor may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking steps necessary to perform a single maneuver.

In some embodiments, the memory 114 may contain instructions 115 (eg, program logic) executable by the processor 113 to perform various functions of the vehicle 100 , including those described above. Memory 114 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of travel system 102 , sensor system 104 , control system 106 , and peripherals 108 . instruction.

In addition to instructions 115, memory 114 may store data such as road maps, route information, vehicle location, direction, speed, and other such vehicle data, among other information. Such information may be used by the vehicle 100 and the computer system 112 during operation of the vehicle 100 in autonomous, semi-autonomous and/or manual modes.

In some embodiments, the above-mentioned processor 113 may also execute the planning scheme for the longitudinal motion parameters of the vehicle according to the embodiments of the present application, so as to help the vehicle to plan the longitudinal motion parameters. For the specific longitudinal motion parameter planning method, reference may be made to the introduction of FIG. 3 below. , and are not repeated here for brevity.

A user interface 116 for providing information to or receiving information from a user of the vehicle 100 . Optionally, user interface 116 may include one or more input/output devices within the set of peripheral devices 108 , such as wireless communication system 146 , onboard computer 148 , microphone 150 and speaker 152 .

Computer system 112 may control functions of vehicle 100 based on input received from various subsystems (eg, travel system 102 , sensor system 104 , and control system 106 ) and from user interface 116 . For example, computer system 112 may utilize input from control system 106 in order to control steering unit 132 to avoid obstacles detected by sensor system 104 and obstacle avoidance system 144 . In some embodiments, computer system 112 is operable to provide control of various aspects of vehicle 100 and its subsystems.

Alternatively, one or more of these components described above may be installed or associated with the vehicle 100 separately. For example, memory 114 may exist partially or completely separate from vehicle 100 . The above-described components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above component is just an example. In practical applications, components in each of the above modules may be added or deleted according to actual needs, and FIG. 1 should not be construed as a limitation on the embodiment of the present invention.

An autonomous vehicle traveling on a road, such as vehicle 100 above, can recognize objects within its surroundings to determine adjustments to current speed. The objects may be other vehicles, traffic control equipment, or other types of objects. In some examples, each identified object may be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, distance from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to adjust.

Optionally, autonomous vehicle 100 or a computing device associated with autonomous vehicle 100 (eg, computer system 112, computer vision system 140, memory 114 of FIG. For example, traffic, rain, ice on the road, etc.) to predict the behavior of the identified object. Optionally, each identified object is dependent on the behavior of the other, so it is also possible to predict the behavior of a single identified object by considering all identified objects together. The vehicle 100 can adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle can determine that the vehicle will need to adjust to a steady state (eg, accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors may also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and the like.

In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 100 so that the autonomous vehicle follows a given trajectory and/or maintains contact with objects in the vicinity of the autonomous vehicle (eg, , cars in adjacent lanes on the road) safe lateral and longitudinal distances.

The above-mentioned vehicle 100 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, construction equipment, a tram, a golf cart, a train, a cart, etc. The embodiments of the invention are not particularly limited.

The applicable scene of the embodiment of the present application is described above with reference to FIG. 1 , and the applicable automatic driving system for executing the embodiment of the present application is described below with reference to FIG. 2 .

FIG. 2 is a schematic diagram of a suitable automatic driving system according to an embodiment of the present application. The computer system 101 includes a processor 103 , and the processor 103 is coupled to a system bus 105 . The processor 103 may be one or more processors, each of which may include one or more processor cores. A video adapter 107, which can drive a display 109, is coupled to the system bus 105. The system bus 105 is coupled to an input/output (I/O) bus 113 through a bus bridge 111 . I/O interface 115 is coupled to the I/O bus. I/O interface 115 communicates with various I/O devices, such as input device 117 (eg, keyboard, mouse, touch screen, etc.), media tray 121, (eg, CD-ROM, multimedia interface, etc.). Transceiver 123 (which can transmit and/or receive radio communication signals), camera 155 (which can capture sceneries and dynamic digital video images) and external USB interface 125 . Wherein, optionally, the interface connected to the I/O interface 115 may be a USB interface.

The processor 103 may be any conventional processor, including a Reduced Instruction Set Computing (Reduced Instruction Set Computing, RISC) processor, a Complex Instruction Set Computing (Complex Instruction Set Computer, CISC) processor or a combination of the above. Alternatively, the processor may be a special purpose device such as an application specific integrated circuit ASIC. Optionally, the processor 103 may be a neural network processor or a combination of a neural network processor and the above-mentioned conventional processors.

Alternatively, in various embodiments described herein, computer system 101 may be located remotely from the autonomous vehicle and may communicate wirelessly with the autonomous vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the autonomous vehicle, others are performed by a remote processor, including taking actions required to perform a single maneuver.

Computer 101 may communicate with software deployment server 149 through network interface 129 . Network interface 129 is a hardware network interface, such as a network card. The network 127 may be an external network, such as the Internet, or an internal network, such as an Ethernet network or a virtual private network (Virtual Private Network, VPN). Optionally, the network 127 may also be a wireless network, such as a Wi-Fi network, a cellular network, and the like.

The hard disk drive interface is coupled to the system bus 105 . The hard drive interface is connected to the hard drive. System memory 135 is coupled to system bus 105 . Data running in system memory 135 may include operating system 137 and application programs 143 of computer 101 .

The operating system includes a shell 139 and a kernel 141 . Shell 139 is an interface between the user and the kernel of the operating system. Shell 139 is the outermost layer of the operating system. Shell 139 manages the interaction between the user and the operating system: waiting for user input, interpreting user input to the operating system, and processing various operating system outputs.

Kernel 141 consists of those parts of the operating system that manage memory, files, peripherals, and system resources. Interacting directly with hardware, the operating system kernel usually runs processes and provides inter-process communication, providing CPU time slice management, interrupts, memory management, IO management, and more.

Application 143 includes programs that control the autonomous driving of the vehicle, such as programs that manage the interaction between the autonomous vehicle and obstacles on the road, programs that control the route or speed of the autonomous vehicle, and programs that control the interaction between the autonomous vehicle and other autonomous vehicles on the road. . Application 143 also exists on the system of software deploying server 149 . In one embodiment, computer system 101 may download application 143 from software deploying server 149 when application 147 needs to be executed.

In some embodiments, the above-mentioned application program may further include an application program corresponding to the target object perception scheme provided by the embodiments of the present application, wherein the target object perception scheme of the embodiments of the present application will be described in detail below. For the sake of brevity, the This will not be repeated here.

Sensors 153 (also known as "collection devices") are associated with computer system 101 . The sensor 153 is used to detect the environment around the computer 101 . For example, the sensor 153 can detect objects, such as animals, vehicles, obstacles, etc., and further sensors can detect the surrounding environment of the above objects, such as: the environment around the animal, other animals appearing around the animal, weather conditions , the brightness of the surrounding environment, etc. Alternatively, if the computer 101 is located on a self-driving vehicle, the sensors may be lidars, cameras, infrared sensors, chemical detectors, microphones, and the like.

In the existing obstacle avoidance system, the AI model is usually used to plan the speed and direction of movement of the vehicle, so as to avoid the collision between the vehicle and the obstacles around the vehicle. However, in the above scheme of using the AI model to control the moving speed and direction of the vehicle, due to the uninterpretability and unpredictability of the AI model, the safety of the controlled vehicle cannot be satisfied.

In order to avoid the above problems, the present application provides a new vehicle control method, that is, on the basis of the above-mentioned AI model-based control of the vehicle's motion speed and motion direction, a new method based on the potential energy function to calculate the obstacle and the motion direction is added. The collision potential energy between vehicles is used to control the speed and direction of movement of the vehicle, so that in the process of controlling the speed and direction of movement of the vehicle, the high perception and decision-making performance of the AI model can be retained, and potential energy can be introduced. Interpretability and predictability of functions to improve safety in controlling the speed and direction of movement of a vehicle. The following describes the vehicle control method according to the embodiment of the present application with reference to FIG. 3 .

FIG. 3 is a flowchart of a vehicle control method according to an embodiment of the present application. It should be understood that the method shown in FIG. 3 may be performed by the obstacle avoidance system shown in FIG. 1 , or by the processor 103 shown in FIG. 2 . The method shown in FIG. 3 includes steps 310 to 330 .

310. Collect the motion state of the obstacle within the preset range.

Optionally, the motion state of the obstacle may include information such as the obstacle's running speed, the running direction, and the position of the obstacle.

320. Input the motion state of the obstacle into the artificial intelligence AI model and the potential energy function respectively, and determine the first motion state and the second motion state of the vehicle, wherein the first motion state is the motion speed and direction of the vehicle calculated based on the AI model , and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function.

The above potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating the collision potential energy between the obstacle and the vehicle.

Optionally, the collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

For example, the above potential energy function can be expressed as

For another example, the above potential energy function can also be expressed as

Wherein, k', α', β' represent constant coefficients, C' represents a constant, ν' represents the relative speed between the obstacle and the vehicle, d' represents the obstacle and the vehicle relative distance between.

For another example, the above-mentioned potential energy function can also be expressed as f"=f ₁ (v")+f ₂ (d")+C", wherein ν" represents the relative speed between the obstacle and the vehicle. magnitude, f ₁ (v") represents a function based on relative velocity, C" represents a constant, d' represents a relative distance between the obstacle and the vehicle, and f ₂ (d") represents a function based on relative distance.

330. Determine a target motion state of the vehicle based on the first motion state and/or the second motion state, where the target motion state includes a target motion speed and a target motion direction of the vehicle.

For ease of understanding, the following describes the intelligent driving system of the embodiment of the present application with reference to FIG. 4 . FIG. 4 is a schematic diagram of the architecture of an intelligent driving system according to another embodiment of the present application. Referring to FIG. 4 , the intelligent driving system 400 includes a controller 401 , a perception device 402 , an interaction system 403 and an execution system 404 .

The sensing device 402 is used to obtain information on obstacles such as vehicles, people and infrastructure around the vehicle through sensors, including images of obstacles and detection information, wherein the detection information may vary according to different types of sensing devices. For example, when the sensing device is a lidar, the lidar can transmit a detection signal (eg, a laser beam) to the target, and then compare the received signal reflected from the target (eg, target echo) with the transmitted signal, After proper processing, the relevant detection information of the target can be obtained, such as the target distance, azimuth, height, speed, attitude, and even the shape and other parameters. The information of the above-mentioned obstacles will be sent to the controller 401, and the controller 401 will further determine the driving trajectory of the vehicle to the destination according to the information of the obstacles, and then send a control command including the speed to the execution system 404, and the execution system 404 will control the vehicle to drive. . Among them, the speed is a vector, including size and direction, and the size of the speed can also be called the rate. In order to meet the high functional safety requirements for safe driving of the vehicle, the controller 401 can utilize a redundant dual-channel design to separately calculate the moving speed of the vehicle in the same segment.

It should be noted that the above-mentioned sensing device 402 can be understood as having the same function at least in part as the sensing system 104 in FIG. 1 , the above-mentioned execution system 404 can be understood as having the same function at least in part as the traveling system 102 shown in FIG. 1 , and the above-mentioned controller 401 can be understood to be at least partially functionally identical to the control system 106 shown in FIG. 1 .

The above-mentioned controller 401 may include a working channel and a safety channel, the working channel is used to plan the first movement speed and the first movement direction of the vehicle by using the AI model, and the safety channel uses the above-mentioned potential energy function to plan the second movement speed and the second movement direction of the vehicle. . The controller 401 can use the working channel and the safety channel to determine the speed and direction of movement of the vehicle in the same area of the running track, and then the controller 401 determines the target speed and direction to be selected according to preset conditions. The target movement speed and target movement direction selected by the controller 401 may also be referred to as the optimal speed.

The above-mentioned working channel 4011 is used for perception, decision-making and path planning by using the AI model, and outputs the first movement speed and first movement direction of the vehicle, so that the vehicle can meet the requirements of quality management (QM). The working channel 4011 includes a first perception module 40111 and a decision module 40112. The first perception module 40111 is used to collect information about obstacles around the smart vehicle collected by the sensing device, and process the obstacle information to obtain road condition information, such as obstacle type, speed, size, road and other infrastructure conditions (such as the current direction). number of lanes, traffic signs, etc.). The decision module 40112 is configured to further determine the direction and speed of driving in a section of the area according to the road condition information provided by the first perception module 40111 .

The above-mentioned safety channel 4012 includes a second perception module 40121 , a decision-making and anti-collision module 40122 . Among them, the decision-making and anti-collision module 40122 is used for the information of obstacles provided by the second perception module 40121, such as the relative distance and relative speed between the obstacle and the vehicle. The potential energy function is used to determine the second movement speed and the second movement direction of the vehicle, so that the vehicle can travel to meet the safety level and meet the requirements of the automotive safety integrity level ASIL D level. Among them, the ASIL level is the automotive safety integrity level, which is used to describe the probability of a component or system achieving a given safety goal. The ASIL level is determined by three basic elements, namely severity (Severity, S), exposure rate (exposure, E), and controllability (C). Severity, which is used to indicate the severity of the damage to the lives and property of people in the vehicle once the risk occurs; exposure rate, which is used to refer to the probability of damage to people or property; Controllability, which is used to describe the driver can The extent to which proactive measures are taken to prevent damage from occurring. The ASIL level can be divided into four levels from high to low: D, C, B, and A. The D level has the smallest safety risk and the A level has the largest safety risk. In addition to the four safety levels, there is also a quality management requirement. The directive management requires no safety requirements. For autonomous driving mode, the safety risk is greater than ASIL.

As a possible implementation manner, the first sensing module 40111 and the second sensing module 40112 in FIG. 4 may be combined into one sensing module, and the combined sensing module obtains obstacle information from the sensing device 402, and accordingly The information further calculates the road condition information such as the distance of the obstacle relative to the vehicle and the relative speed of the obstacle relative to the vehicle, and sends the required content to the decision module 40112 and the decision and anti-collision module 40122 according to the information required by the decision module 40112 and the decision and anti-collision module 40122 respectively.

The first sensing module 40111, the decision-making module 40112, the second sensing module 40121, the decision-making and anti-collision module 40122, and the arbitration module 405 in the controller shown in FIG. 4 can be implemented by hardware, software, or a combination of hardware and software. implement the corresponding function.

Optionally, the system 400 further includes an interaction system 403, the interaction system 403 is used to realize the message interaction between the vehicle and the driver, so that the driver can send an operation instruction to the vehicle through the interaction system 403, and learn about the vehicle through the interaction system 403. current state.

As a possible embodiment, the system 400 further includes an arbiter 405, and the arbiter 405 receives the first movement speed and the first movement direction planned by the working channel 4011 and the second movement speed and the second movement direction planned by the safety channel 4012, respectively. , and the arbiter 405 selects the target movement speed and the target movement direction according to preset conditions.

As a possible implementation manner, FIG. 4 is only a schematic diagram of the architecture of a vehicle provided in the present application, and the arbiter may be implemented by software or hardware in the controller. The arbiter can also function as a redundant channel selection by an independent processor.

Optionally, the above step 330 includes: if the relative distance between the obstacle and the vehicle is less than the first preset distance, or the relative speed between the obstacle and the vehicle is higher than the first preset speed, then based on the second motion state Determine the target motion state of the vehicle; and/or, if the relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, then based on the first The motion state determines the target motion state of the vehicle, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.

The relative distance between the obstacle and the vehicle is smaller than the first preset distance, or the relative speed between the obstacle and the vehicle is higher than the first preset speed, which can indicate that the possibility of collision between the obstacle and the vehicle is high , belongs to an emergency situation. In this case, in order to improve safety, the second motion state of the vehicle obtained based on the potential energy function can be used as the target motion state of the vehicle.

The relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, which can indicate that the possibility of collision between the obstacle and the vehicle is low. , belongs to a non-emergency situation. In this case, the first motion state of the vehicle obtained based on the AI model can be used as the target motion state of the vehicle.

There are many methods for calculating the second motion state of the vehicle based on the potential energy function. For example, the vertical direction of the collision potential energy between the obstacle and the vehicle can be used as the motion direction of the vehicle, and the vehicle can be determined based on the motion speed and relative distance of the obstacle. size of the movement speed. Of course, the second motion state of the vehicle determined by using the above method may cause a large change from the current motion state of the vehicle, that is to say, greater direction adjustment and speed adjustment are required to make the vehicle avoid the above obstacles , which will affect the user experience of passengers to a certain extent.

Therefore, the embodiments of the present application also provide a method for determining a second motion state of a vehicle based on a potential energy function, that is, calculating the relative speed and relative distance between the obstacle and the vehicle; calculating the relative speed, relative distance, and potential energy function based on collision potential energy between the obstacle and the vehicle; based on the first objective function min(Δf), and Δf=(ff ₀ )<0, determine the second motion state of the vehicle; or based on the second objective function min(f<F ₁ ) to determine the second motion state of the vehicle, where f ₀ represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle, and F ₁ represents the preset first collision potential energy threshold.

The above-mentioned first objective function min(Δf) can be understood as finding the optimal solution to make the movement speed and movement direction of the vehicle with the smallest change between the current collision potential energy and the adjusted collision potential energy. In this way, the adjusted movement speed and direction of movement. Therefore, the above-mentioned solution for solving the motion speed and motion direction of the vehicle based on the first objective function min(Δf) can be called “comfort mode”. The amount of change in the direction of movement and movement is small, and the comfort of the passengers in the vehicle is high.

The above-mentioned second objective function min (f<F ₁ ) can be understood as finding the optimal solution so that the adjusted collision potential energy is smaller than the preset collision potential energy threshold. In this way, based on the setting F ₁ of the above collision potential energy threshold, the vehicle can be Adjusted movement speed and movement direction for safety. Therefore, the above solution based on the second objective function min(f<F ₁ ) to solve the moving speed and moving direction of the vehicle can be called “safety mode”. In this mode, the adjusted moving speed and moving direction of the vehicle make The collision potential energy between the vehicle and the obstacle is smaller than the preset collision potential energy threshold, which is beneficial to improve the safety of vehicle driving.

For ease of understanding, the following describes the vehicle control method according to the embodiment of the present application with reference to FIG. 5 . FIG. 5 is a flowchart of the control of the vehicle according to the embodiment of the present application. The method shown in FIG. 5 includes steps 510 to 590 .

510. Determine the relative speed and relative distance between the obstacle and the vehicle within a preset range.

The obstacles within the above-mentioned preset range may include one or more obstacles.

A two-dimensional coordinate system can be established with the center of mass of the vehicle as the origin and the direction of the vehicle speed as the positive X axis, and the position of each obstacle in the preset range in the two-dimensional coordinate system can be determined, based on the obstacle in the two-dimensional coordinate system. The position of the determines the relative distance between the obstacle and the vehicle.

FIG. 6 is a schematic diagram showing the relationship between the vehicle and the obstacle in the coordinate system of the embodiment of the present application. Referring to the coordinate system shown in FIG. 6 , taking the center of mass of the vehicle 610 as the origin, and the direction of the vehicle 610 traveling speed is the positive direction of the X-axis, a two-dimensional coordinate system is established, and the coordinate system also includes obstacle 1, obstacle 2, and obstacle item 3.

The above-mentioned specific process of determining the relative distance and relative speed between the vehicle 610 and the obstacle can be divided into the following three steps. It should be understood that the following methods can be used to calculate the relative distance and relative speed between each obstacle in the multiple obstacles and the vehicle. For the sake of brevity, one of the obstacles (target obstacle) is used as an example to introduce the determination of the relative distance. and relative velocity methods.

Step 1: Collect the position of the target obstacle in the coordinate system at time T

Collect the position of the target obstacle in the coordinate system at time T'

Step 2: Calculate the current distance between the vehicle 610 and the target obstacle

Step 3: Calculate the relative speed between the vehicle 610 and the target obstacle, and the direction of the relative speed points to the vehicle 610 .

Referring to Figure 7, it is assumed that the target obstacle is from the position from time T to time T'

move to location

Among them, T'=T+Δt,

The speed of the vehicle 610 is

Then the distance that the target obstacle moves in Δt time is

The relative speed of the target obstacle and the vehicle 610 is

The projection of the target obstacle along the speed direction of the vehicle 610 is

It should be noted that the obstacle may collide with the vehicle only when the obstacle is in the same direction as the vehicle and the speed is close to the vehicle. Calculating the projection of the obstacle along the speed direction of the vehicle is to confirm the speed component of the possible collision between the obstacle and the vehicle. . In other words, the projection of the obstacle in the direction of the vehicle speed is used to indicate the tendency of the obstacle to collide with the ego vehicle caused by the obstacle moving in the direction of the ego vehicle speed. The projection of the obstacle along the vehicle speed direction is taken as the relative speed of the obstacle to the vehicle.

520. Calculate the collision potential energy between the obstacle and the vehicle based on the relative speed and the relative distance between the obstacle and the vehicle.

use

Calculate the collision potential energy of obstacles. The collision potential energy f(O) of the target obstacle O is used to describe the tendency of the target obstacle O to collide with the vehicle, or it is called the escape potential energy that the vehicle should have to avoid the collision of the target obstacle. For example, the closer the vehicle is to the target obstacle, the stronger the escaping trend is, and the faster the target obstacle is approaching, the stronger the escaping trend is. In the above formula, k, α, β are constant coefficients, C is a constant, and the value of C can be flexibly set according to the simulation results and actual experience. Because the speed ν is the speed of the target obstacle relative to the vehicle, it is a vector with both magnitude and direction. Therefore, f is also a vector with the same direction as v.

It is worth noting that when calculating the size of f(O), take the size of v and bring it into the above formula to calculate the collision potential energy of the obstacle. The projections of f in the x and y directions are

where v _x and v _y are the coordinates of ν on the X and Y axes, respectively.

Further, the decision-making and anti-collision module can determine the position of each surrounding vehicle in the coordinate system shown in FIG. 6 according to the relative positions of the surrounding vehicles and the own vehicle. Specifically, after the coordinate system with the own vehicle as the origin is established, the coordinate system is a two-dimensional coordinate system, and in the plane of the two-dimensional coordinate system, the projected position of the surrounding vehicles on the two-dimensional coordinate system is used as the surrounding vehicle's projection position. Location. Optionally, the method for determining the position of the surrounding vehicles in the own vehicle coordinate system also includes: converting the coordinates of the surrounding vehicles in the geodetic coordinate system into a two-dimensional coordinate system. Coordinate transformation in a coordinate system, which is not limited in this application.

It should be noted that if the above-mentioned obstacles are multiple, the collision potential energy between each obstacle and the target vehicle can be summed based on the solution method of the vector sum. At this time, the above f(O) can be understood as multiple The collision potential energy sum of the obstacles.

530. Determine whether there is an obstacle of an early warning level according to the collision potential energy. If there is an obstacle with an early warning level, step 540 is executed. If there is no obstacle with an early warning level, the calculation process can be ended, or the calculation process can be restarted.

In this embodiment of the present application, the collision risk between the obstacle and the vehicle 610 may be divided into three levels based on the collision potential energy: a safety level, an early warning level, and a dangerous level. When the obstacle is at the safety level, the vehicle is not likely to collide; when the obstacle is at the warning level, the vehicle may collide, and the controller can prompt the driver to manually operate through the interactive system to avoid obstacles; when the obstacle is at the dangerous level The controller can take over the control of the vehicle in an emergency, so as to avoid the collision between the self-vehicle and other vehicles in an emergency in the execution of other modules of the vehicle.

It is worth noting that when the obstacle is at a dangerous level, the controller actively takes over is limited to the process of calculation or data processing performed by other modules when the vehicle is in automatic driving mode. For manual driving mode, the operation of the vehicle is completely controlled by the driver, and the controller can not interfere with the driving process of the vehicle.

Referring to Fig. 8 , it is assumed that the collision risk level can be preset according to the vehicle's obstacle avoidance ability (such as performance and size), respectively, to preset the first collision potential energy threshold |F ₁ | and the second collision potential energy threshold |F ₂ |, when |F ₁ |≤ When |f|<|F ₂ |, the obstacle belongs to the warning level; when |f|≥|F ₂ |, the obstacle belongs to the dangerous level; when |f|<|F ₁ |, the obstacle belongs to the safety level, Among them, |F ₁ |<|F ₂ |, the obstacle 11 in the dangerous area belongs to the dangerous level, the obstacle 12, obstacle 14, and obstacle 13 in the early warning area belong to the early warning level, and the obstacle 15 in the safe area belongs to the early warning level. , Obstacle 16, and Obstacle 17 belong to the safety level.

540, alert the driver.

550. Determine whether there is an obstacle of a dangerous level according to the collision potential energy. If there is an obstacle of a dangerous level, step 560 is executed. If there is no obstacle of a dangerous level, the calculation process can be ended, or the calculation process can be restarted.

560, alerting the driver.

570, select Safe Mode or Comfort Mode. If the safe mode is selected, step 580 is performed, and if the comfortable mode is selected, step 590 is selected.

580. Determine a second motion state of the vehicle based on the second objective function min(f<F ₁ ).

It should be noted that there may be an infinite number of solutions (v, θ) in the process of finding feasible solutions. At this time, among all feasible solutions, the feasible solution with the smallest change relative to the current speed can be selected as the optimal solution, where , v represents the movement speed of the vehicle 610 after adjustment, and θ represents the movement direction of the vehicle 610 after adjustment. If there is no feasible solution (v, θ) that meets the conditions, at this time, the feasible solution (v, θ) with the smallest f can be selected as the optimal solution, and a warning is issued to the driver at the same time.

590 , based on the first objective function min(Δf), and Δf=(ff ₀ )<0, determine a second motion state of the vehicle.

It should be noted that there may be an infinite number of solutions (v, θ) in the process of finding feasible solutions. At this time, among all feasible solutions, the feasible solution with the smallest change relative to the current speed can be selected as the optimal solution, where , v represents the movement speed of the vehicle 610 after adjustment, and θ represents the movement direction of the vehicle 610 after adjustment. If there is no feasible solution (v, θ) that meets the conditions, at this time, the feasible solution (v, θ) with the smallest Δf can be selected as the optimal solution, and a warning is issued to the driver at the same time.

Optionally, "sending an alert to the driver" in the above steps may be implemented by the interactive system shown in FIG. 9 . As shown in Figure 9, the interactive system can prompt the driver to pay attention to obstacles in various forms, and the driver can take over the vehicle or send execution instructions to the vehicle to control the driving of the vehicle. For example, audio prompts, seat vibration prompts, interior flashing lights prompts. Human-computer interaction systems can also use different colors or backgrounds to identify different levels and areas.

Specifically, the human-computer interaction process between the vehicle and the driver can be realized in at least one of the following ways:

Mode 1: The vehicle's on-board display interface prompts the vehicle to have a collision risk with surrounding obstacles, as well as the first speed and the second speed through text. For example, Va and Vb in FIG. 9 are optional obstacle avoidance directions, and the driver can choose any one as the direction in which the vehicle travels. In addition, in addition to marking Va and Vb as optional obstacle avoidance directions, different signs can also be used to indicate the collision risk of driving in the direction of the obstacle. Prompt "dangerous".

Mode 2: In the vehicle, the vehicle is prompted to have a collision risk with the surrounding obstacles, the first speed and the second speed; the seat vibration is used in the vehicle to prompt the vehicle to have a collision risk with the surrounding obstacles.

Method 3: In the vehicle, the flashing lights of the lights indicate that there is a risk of collision between the vehicle and surrounding obstacles. For dangerous situations, the driver's attention can also be prompted by flashing lights quickly.

As a possible implementation, after the vehicle avoids obstacles according to the above method, the original driving trajectory determined by the decision-making module may be changed. It is necessary to further re-plan or adjust the original driving trajectory according to the road conditions of the vehicle at the current moment. Ensure that the vehicle arrives at the destination designated by the driver smoothly.

Optionally, in addition to using the above-mentioned controller to determine the speed, the vehicle can also receive the speed selected by the driver through the interface or voice, and after receiving the above-mentioned speed control command, the vehicle can be controlled to run at this speed.

Through the above human-computer interaction system, the driver's driving experience can be improved, and the driver can better take over and control the vehicle. On the other hand, through the human-computer interaction system, the driver can also understand the environment of the vehicle, reducing the driver's fear of not being able to know the driving area of the vehicle in an emergency. In an emergency, the driver can also decide whether to switch the driving mode to the manual driving mode through the situation displayed by the manual interaction system, and the driver will take over the control of the vehicle.

As a possible implementation, in addition to using the relative speed and relative distance between the obstacle and the vehicle to confirm the collision potential energy, and then confirm the collision risk between the obstacle and the vehicle, it can also be used according to the type of the obstacle. Different weights are added to the vehicle, and the setting of the specific weights can consider the damage degree of the collision between different types of obstacles and the vehicle. The optimal direction and speed of obstacle avoidance are further determined in combination with the above-mentioned collision damage degree.

As another possible implementation, in addition to relying on the sensing data of the vehicle in which the controller detects surrounding obstacles, the controller can also send information about other vehicles to the vehicle from other obstacles, including track information of other vehicles , the obstacle avoidance process of the vehicle's own vehicle can also be combined with the above vehicle information to realize the vehicle obstacle avoidance process. Among them, other obstacles can send information to the vehicle through the vehicle to everything (V2X) communication technology. When there are two or more obstacle avoidance directions, you can also confirm whether it collides with the own vehicle according to the type of the obstacle, the distance from the vehicle and the relative speed, and display the probability of avoiding the obstacle through the interface. The operator can select any feasible direction as the obstacle avoidance direction through the interface.

As another possible implementation manner, when the second speed confirmed by the safety channel has multiple directions, the safest direction may also be selected as the direction of the second speed according to the degree of collision risk with the obstacle, wherein the degree of collision risk is the direction of the second speed. It includes one or more of the factors such as the probability of collision with the obstacle, the degree of damage in the collision, etc. The degree of damage in the collision can be calibrated according to the size, relative speed and relative distance of the obstacle. The larger the obstacle, the higher the relative speed. The faster and the shorter the relative distance, the higher the damage in a collision. In the above manner, when there are multiple directions of the second speed, the optimal direction can be selected to avoid obstacles according to the degree of collision risk, thereby further improving the safety of automatic driving. Moreover, the above-mentioned collision risk degree can be displayed to the driver through the human-computer interaction interface, and the driver can select the direction of the final speed, and then control the vehicle to drive at the speed selected by the driver.

It is worth noting that, for the purpose of simple description, the above method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, and secondly, Those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the present application.

Other reasonable step combinations that those skilled in the art can think of based on the above description also fall within the protection scope of the present application. Secondly, those skilled in the art should also be familiar with that, the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the present application.

The methods of the embodiments of the present application are described above with reference to FIGS. 1 to 9 , and the apparatuses of the embodiments of the present application are described below with reference to FIGS. 10 to 12 . It should be understood that the apparatuses shown in FIG. 10 to FIG. 12 can implement each step in the above method, and for the sake of brevity, details are not repeated here.

FIG. 10 is a schematic diagram of a control device of a vehicle according to an embodiment of the present application. The apparatus 1000 shown in FIG. 10 includes: a collection unit 1010 and a processing unit 1020 .

a collection unit 1010, configured to collect the motion state of obstacles within a preset range;

The processing unit 1020 is configured to input the motion state of the obstacle into the artificial intelligence AI model and the potential energy function respectively, and determine the first motion state and the second motion state of the vehicle, wherein the potential energy function is calculated by calculating the obstacle and the potential energy function. The collision potential energy between the vehicles indicates the probability of the collision between the obstacle and the vehicle, the higher the collision potential energy, the higher the collision probability, and the smaller the collision potential energy, the higher the collision probability The smaller the value, the first motion state is the motion speed and direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model. direction;

The processing unit 1020 is further configured to determine a target motion state of the vehicle based on the first motion state and/or the second motion state, where the target motion state includes a target motion speed and a target motion direction of the vehicle .

Optionally, as an embodiment, the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to the relative speed between the obstacle and the vehicle. .

Optionally, as an embodiment, if the relative distance between the obstacle and the vehicle is less than the first preset distance, or the relative speed between the obstacle and the vehicle is higher than the first preset distance speed, the processing unit 1020 is further configured to determine the target motion state of the vehicle based on the second motion state; and/or,

If the relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, the processing unit 1020 further uses In determining the target motion state of the vehicle based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset distance Set speed.

Optionally, as an embodiment, the processing unit 1020 is further configured to: determine the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(ff ₀ )<0, wherein, f ₀ represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy to be adjusted between the vehicle and the obstacle.

Optionally, as an embodiment, the processing unit 1020 is further configured to: determine the second motion state of the vehicle based on the second objective function min (f<F ₁ ), where F ₁ represents a preset The first collision potential energy threshold, f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

Optionally, as an embodiment, the potential energy function is

FIG. 11 is a schematic diagram of a control device of a vehicle according to an embodiment of the present application. The apparatus 1100 shown in FIG. 11 includes: a collection unit 1110 and a processing unit 1120 , and the collection unit 1110 is configured to collect data required by the processing unit 1120 .

The processing unit 1120 is used to calculate the relative speed and relative distance between the obstacle and the vehicle;

The processing unit 1120 is further configured to calculate the collision potential energy between the obstacle and the vehicle based on the relative speed, the relative distance, and the potential energy function, wherein the collision potential energy indicates that the obstacle is different from the vehicle. The possibility of collision of the vehicle, the higher the collision potential energy, the higher the possibility of the collision, and the smaller the collision potential energy, the lower the possibility of the collision;

The processing unit 1120 is further configured to determine the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(ff ₀ )<0; or

The processing unit 1120 is further configured to determine the second motion state of the vehicle based on the second objective function min(f<F ₁ ),

Wherein, f ₀ represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy to be adjusted between the vehicle and the obstacle, and F ₁ represents a preset first collision potential energy threshold.

Optionally, as an embodiment, the processing unit is further configured to:

In the comfort mode, based on the first objective function min(Δf), and Δf=(ff ₀ )<0, a second motion state of the vehicle is determined.

Optionally, as an embodiment, the processing unit is further configured to:

In the safety mode, the second motion state of the vehicle is determined based on the second objective function min (f<F ₁ ), where F ₁ represents a preset first collision potential energy threshold, and f represents the relationship between the vehicle and the vehicle. The collision potential energy that needs to be adjusted between obstacles.

In an optional embodiment, the processing unit 1020 may be a processor 1220, the collection unit 1010 may be a communication interface 1230, and the communication device may further include a memory 1210, as shown in FIG. 12 .

In an optional embodiment, the processing unit 1120 may be a processor 1220, the collection unit 1110 may be a communication interface 1230, and the communication device may further include a memory 1210, as shown in FIG. 12 .

FIG. 12 is a schematic block diagram of a computing device according to another embodiment of the present application. The computing device 1200 shown in FIG. 12 may include: a memory 1210 , a processor 1220 , and a communication interface 1230 . Among them, the memory 1210, the processor 1220, and the communication interface 1230 are connected through an internal connection path, the memory 1210 is used to store instructions, and the processor 1220 is used to execute the instructions stored in the memory 1220 to control the input/output interface 1230 to receive/send At least part of the parameters of the second channel model. Optionally, the memory 1210 can be coupled with the processor 1220 through an interface, or can be integrated with the processor 1220 .

It should be noted that, the above-mentioned communication interface 1230 uses a transceiver such as but not limited to a transceiver to implement communication between the communication device 1200 and other devices or communication networks. The above-mentioned communication interface 1230 may also include an input/output interface.

In the implementation process, each step of the above-mentioned method may be completed by an integrated logic circuit of hardware in the processor 1220 or an instruction in the form of software. The methods disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware processor, or executed by a combination of hardware and software modules in the processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory 1210, and the processor 1220 reads the information in the memory 1210, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.

It should be understood that, in this embodiment of the present application, the processor may be a central processing unit (central processing unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (digital signal processors, DSP), dedicated integrated Circuit (application specific integrated circuit, ASIC), off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It should also be understood that, in this embodiment of the present application, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the processor may also include non-volatile random access memory. For example, the processor may also store device type information.

It should be understood that the term "and/or" in this document is only an association relationship to describe associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, and A and B exist at the same time , there are three cases of B alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

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 alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

A vehicle control method, comprising:

Collect the motion state of obstacles within a preset range;

The motion state of the obstacle is input into the artificial intelligence AI model and the potential energy function respectively, and the first motion state and the second motion state of the vehicle are determined, wherein the potential energy function is calculated by calculating the distance between the obstacle and the vehicle. The collision potential energy indicates the possibility of the collision between the obstacle and the vehicle, the higher the collision potential energy, the higher the collision possibility, the smaller the collision potential energy, the lower the collision possibility, and the first A motion state is the motion speed and direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model;

Based on the first motion state and/or the second motion state, a target motion state of the vehicle is determined, and the target motion state includes a target motion speed and a target motion direction of the vehicle.
The method of claim 1, wherein the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle. Relative velocity is positively correlated.
The method according to claim 1 or 2, wherein the determining the target motion state of the vehicle based on the first motion state and/or the second motion state comprises:

If the relative distance between the obstacle and the vehicle is less than the first preset distance, or the relative speed between the obstacle and the vehicle is higher than the first preset speed, then based on the second movement the state determines the target motion state of the vehicle; and/or,

If the relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, then based on the first motion The state determines the target motion state of the vehicle, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.
The method according to any one of claims 1-3, wherein the inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle comprises:

Based on the first objective function min(Δf), and Δf=(ff 0 )<0, determine the second motion state of the vehicle, where f 0 represents the current collision potential energy of the vehicle and the obstacle, and f represents The collision potential energy between the vehicle and the obstacle needs to be adjusted.
The method according to any one of claims 1-3, wherein the inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle comprises:

Determine the second motion state of the vehicle based on the second objective function min (f<F 1 ), where F 1 represents a preset first collision potential energy threshold, and f represents the distance between the vehicle and the obstacle The collision potential energy to be adjusted to.
The method according to any one of claims 1-5, wherein the potential energy function is
Wherein, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.
A vehicle control method, comprising:

Calculate the relative speed and relative distance between the obstacle and the vehicle;

A collision potential energy between the obstacle and the vehicle is calculated based on the relative velocity, the relative distance, and a potential energy function, wherein the collision potential energy is indicative of the likelihood of the obstacle colliding with the vehicle, The larger the collision potential energy is, the higher the possibility of the collision is, and the smaller the collision potential energy is, the smaller the possibility of the collision is;

determining a second motion state of the vehicle based on the first objective function min(Δf), and Δf=(ff 0 )<0; or

Based on the second objective function min(f<F 1 ), the second motion state of the vehicle is determined,

Wherein, f 0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy to be adjusted between the vehicle and the obstacle, and F 1 represents a preset first collision potential energy threshold.
The method according to claim 7, wherein the determining the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(ff 0 )<0, comprises:

In the comfort mode, based on the first objective function min(Δf), and Δf=(ff 0 )<0, a second motion state of the vehicle is determined.
The method of claim 8, wherein the method further comprises:

In the safety mode, the second motion state of the vehicle is determined based on the second objective function min (f<F 1 ), where F 1 represents a preset first collision potential energy threshold, and f represents the relationship between the vehicle and the vehicle. The collision potential energy that needs to be adjusted between obstacles.
The method according to any one of claims 7-9, wherein the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is inversely related to the obstacle and the vehicle. The relative speeds between the vehicles are positively correlated.
A control device for a vehicle, comprising:

a collection unit, used to collect the motion state of obstacles within a preset range;

The processing unit is configured to input the motion state of the obstacle into the artificial intelligence AI model and the potential energy function respectively, and determine the first motion state and the second motion state of the vehicle, wherein the potential energy function is calculated by calculating the obstacle and the potential energy function. The collision potential energy between the vehicles indicates the possibility of the collision between the obstacle and the vehicle, the higher the collision potential energy, the higher the collision possibility, the smaller the collision potential energy, the higher the collision possibility small, the first motion state is the motion speed and direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model ;

The processing unit is further configured to determine a target motion state of the vehicle based on the first motion state and/or the second motion state, where the target motion state includes a target motion speed and a target motion direction of the vehicle.
12. The apparatus of claim 11, wherein the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle. Relative velocity is positively correlated.
The device according to claim 11 or 12, wherein if the relative distance between the obstacle and the vehicle is less than a first preset distance, or the relative speed between the obstacle and the vehicle higher than the first preset speed, the processing unit is further configured to determine the target motion state of the vehicle based on the second motion state; and/or,

If the relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, the processing unit is further configured to: The target motion state of the vehicle is determined based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.
The device according to any one of claims 11-13, wherein the processing unit is further configured to:

Based on the first objective function min(Δf), and Δf=(ff 0 )<0, determine the second motion state of the vehicle, where f 0 represents the current collision potential energy of the vehicle and the obstacle, and f represents The collision potential energy between the vehicle and the obstacle needs to be adjusted.
The device according to any one of claims 11-13, wherein the processing unit is further configured to:

Determine the second motion state of the vehicle based on the second objective function min (f<F 1 ), where F 1 represents a preset first collision potential energy threshold, and f represents the distance between the vehicle and the obstacle The collision potential energy to be adjusted to.
The device according to any one of claims 11-15, wherein the potential energy function is
Wherein, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.
A control device for a vehicle, comprising:

A processing unit for calculating the relative speed and relative distance between the obstacle and the vehicle;

The processing unit is further configured to calculate a collision potential energy between the obstacle and the vehicle based on the relative speed, the relative distance, and a potential energy function, wherein the collision potential energy indicates that the obstacle is different from the vehicle. the possibility of collision of the vehicle, the higher the collision potential energy, the higher the possibility of the collision; the smaller the collision potential energy, the lower the possibility of the collision;

The processing unit is further configured to determine the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(ff 0 )<0; or

The processing unit is further configured to determine the second motion state of the vehicle based on the second objective function min(f<F 1 ),

Wherein, f 0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy to be adjusted between the vehicle and the obstacle, and F 1 represents a preset first collision potential energy threshold.
The apparatus of claim 17, wherein the processing unit is further configured to:

In the comfort mode, based on the first objective function min(Δf), and Δf=(ff 0 )<0, a second motion state of the vehicle is determined.
The apparatus of claim 18, wherein the processing unit is further configured to:

In the safety mode, the second motion state of the vehicle is determined based on the second objective function min (f<F 1 ), where F 1 represents a preset first collision potential energy threshold, and f represents the relationship between the vehicle and the vehicle. The collision potential energy that needs to be adjusted between obstacles.
The apparatus of any one of claims 17-19, wherein the collision potential energy is inversely related to the relative distance between the obstacle and the vehicle, and the collision potential energy is inversely related to the obstacle and the vehicle. The relative speeds between the vehicles are positively correlated.
A computing device, characterized in that it comprises: at least one processor and a memory, the at least one processor is coupled to the memory for reading and executing instructions in the memory, so as to execute the steps of claims 1- The method of any one of 10.
A computer-readable medium, characterized in that, the computer-readable medium stores program codes, which, when the computer program codes are executed on a computer, cause the computer to perform the method described in any one of claims 1-10. method described.
A chip, characterized by comprising: at least one processor and a memory, wherein the at least one processor is coupled to the memory for reading and executing instructions in the memory, so as to execute the instructions in claims 1-10 The method of any of the above.
An autonomous driving vehicle, characterized by comprising: at least one processor and a memory, wherein the at least one processor is coupled to the memory for reading and executing instructions in the memory, so as to execute the method as claimed in claim 1 - The method of any one of 10.