CN114248794A

CN114248794A - Vehicle control method and device and vehicle

Info

Publication number: CN114248794A
Application number: CN202011007408.3A
Authority: CN
Inventors: 张晓毓
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-09-23
Filing date: 2020-09-23
Publication date: 2022-03-29
Also published as: WO2022062825A1

Abstract

The application provides an automatic driving technology in the field of artificial intelligence to improve the safety of adjusting the moving speed and the moving direction of a vehicle. The method comprises the following steps: acquiring the motion state of an obstacle within a preset range; respectively inputting the motion states of the obstacles into an artificial intelligence AI model and a potential energy function, and determining a first motion state and a second motion state of the vehicle, wherein the potential energy function indicates the possibility of collision between the obstacles and the vehicle by calculating the collision potential energy between the obstacles and the vehicle, the collision potential energy is larger, the collision possibility is higher, the collision possibility is smaller, the first motion state is the motion speed and the direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and the direction of the vehicle calculated based on the potential energy function model; and determining a target motion state of the vehicle based on the first motion state and/or the second motion state, wherein the target motion state comprises a target motion speed and a target motion direction of the vehicle.

Description

Vehicle control method and device and vehicle

Technical Field

The present application relates to the field of automated driving, and more particularly, to a control method and apparatus for a vehicle, and a vehicle.

Background

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the 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 intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method 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 and reasoning, human-computer interaction, recommendation and search, AI basic theory, and the like.

Automatic driving is a mainstream application in the field of artificial intelligence, and the automatic driving technology depends on the cooperative cooperation of computer vision, radar, a monitoring device, a global positioning system and the like, so that the motor vehicle can realize automatic driving without the active operation of human beings. Autonomous vehicles use various computing systems to assist in transporting passengers from one location to another. Some autonomous vehicles may require some initial input or continuous input from an operator, such as a pilot, driver, or passenger. Autonomous vehicles permit an operator to switch from a manual mode of operation to an autonomous driving mode or an intermediate mode. Because the automatic driving technology does not need human to drive the motor vehicle, the driving error of human can be effectively avoided theoretically, the occurrence of traffic accidents is reduced, and the transportation efficiency of the road can be improved. Therefore, the automatic driving technique is increasingly emphasized.

At present, the motion state of the obstacle around the vehicle may be input into the AI model to obtain the motion speed and the motion direction of the vehicle through the AI model, so as to adjust the motion speed and the motion direction of the vehicle to avoid the collision of the vehicle with the obstacle around the vehicle. However, in the above scheme of controlling the moving speed and moving direction of the vehicle using the AI model, the safety of the controlled vehicle cannot be satisfied due to the inexplicability and unpredictability of the AI model.

Disclosure of Invention

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

In a first aspect, a control scheme for a vehicle is provided, comprising: 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 collision potential energy between the obstacle and the vehicle, the collision potential energy is larger, the possibility of collision is higher, the collision potential energy is smaller, the possibility of collision is smaller, the first motion state is the motion speed and the motion direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and the motion direction of the vehicle calculated based on the potential energy function model; determining a target motion state of the vehicle based on the first motion state and/or the second motion state, wherein the target motion state comprises a target motion speed and a target motion direction of the vehicle.

In the embodiment of the application, the motion states of the obstacle are respectively input into the AI model and the potential energy function 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 that the situation that in the prior art, the target motion state of the vehicle can only be determined based on the AI model is avoided, and the safety of determining the target motion state of the vehicle is improved.

On the other hand, in the embodiment of the application, the second motion state of the vehicle is determined based on the potential energy function, so that the accuracy of determining the target motion state is improved, and the condition that the target motion state of the vehicle is determined only based on the relative distance between the vehicle and the obstacle in the prior art is avoided.

In one possible implementation, 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 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, so that the possibility of collision between the obstacle and the vehicle is favorably measured.

Alternatively, the above-described relative speed may be a component of the relative speed between the obstacle and the vehicle in the vehicle traveling direction.

In the embodiment of the application, the collision potential energy between the obstacle and the vehicle is positively correlated with the component of the relative speed in the vehicle running direction, so that the accuracy of the possibility of collision between the obstacle and the vehicle is improved.

In one possible implementation, 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 distance between the obstacle and the vehicle is smaller than a first preset distance and/or the relative speed between the obstacle and the vehicle is higher than a first preset speed, determining a 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 a second preset distance, and/or the relative speed between the obstacle and the vehicle is lower than a second preset speed, determining the target motion state of the vehicle based on the first motion state, wherein 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 the embodiment of the application, if the relative distance between the obstacle and the vehicle is smaller 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 relatively emergency situation, 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 improving 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, namely in a non-emergency situation, the target motion state of the vehicle can be determined based on the first motion state determined by the AI model, which is beneficial to reducing the time for determining the target motion state of the vehicle.

In one possible implementation, the inputting the motion state of the obstacle into the potential energy function and determining the second motion state of the vehicle include: based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of said vehicle, wherein f₀Representing the current collision potential energy of the vehicle and the obstacle, and f representing the collision potential energy between the vehicle and the obstacle which needs to be adjusted.

In the embodiment of the present application, the first objective function min (Δ f) is based on, and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle such that a change between the current collision potential energy and the adjusted collision potential energy is small, that is, such that a changed motion speed of the vehicle is small and a changed motion angle of the vehicle is small, to improve comfort of passengers.

In one possible implementation, the inputting the motion state of the obstacle into the potential energy function and determining the second motion state of the vehicle include: based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein F₁Representing a preset first collision potential energy threshold value, and f representing the collision potential energy between the vehicle and the obstacle to be adjusted.

In the embodiment of the application, based on the second objective function min (F < F)₁) And determining a second motion state of the vehicle, so that the adjusted collision potential energy is smaller than a preset first collision potential energy threshold value, and the safety of determining the target motion state of the vehicle is improved.

In one possible implementation, the potential energy function is

Wherein k, α, β represent constant coefficients, C represents a constant, v represents a magnitude of a relative velocity between the obstacle and the vehicle, and d represents a relative velocity between the obstacle and the vehicleDistance.

In a second aspect, there is provided a control method of a vehicle, including: calculating the relative speed and the relative distance between the obstacle and the vehicle; calculating 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 a likelihood of the obstacle colliding with the vehicle, and the larger the collision potential energy, the higher the likelihood of the collision, and the smaller the likelihood of the collision; based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle; or based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein f₀Representing the current collision potential energy of the vehicle and the obstacle, F representing the collision potential energy between the vehicle and the obstacle to which adjustment is required, F₁Representing a preset first crash potential energy threshold.

In the embodiment of the application, the second motion state of the vehicle is determined based on the potential energy function, so that the accuracy of determining the target motion state is improved, and the condition that the target motion state of the vehicle is determined based on the relative distance between the vehicle and the obstacle in the prior art is avoided.

On the other hand, in the embodiment of the present application, the first objective function min (Δ f) is based, and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle such that a change between the current collision potential energy and the adjusted collision potential energy is small, that is, such that a changed motion speed of the vehicle is small and a changed motion angle of the vehicle is small, to improve comfort of passengers. Based on a second objective function min (F < F)₁) And determining a second motion state of the vehicle, so that the adjusted collision potential energy is smaller than a preset first collision potential energy threshold value, and the safety of determining the target motion state of the vehicle is improved.

In one possible implementation, the first objective function min (Δ f) is based on, and Δ f ═ f (f-f)₀) < 0, determining a second motion state of the vehicle, comprising: in comfort mode, based on the first objective function min (Δ f), and Δ f ═ f (f-f)₀) < 0, determining a second motion state of the vehicle.

In the embodiment of the present application, in the comfort mode, based on the first objective function min (Δ f), and Δ f ═ f (f-f)₀) < 0, determining a second motion state of the vehicle such that a change between the current collision potential energy and the adjusted collision potential energy is small, that is, such that a changed motion speed of the vehicle is small and a changed motion angle of the vehicle is small, to improve comfort of passengers.

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

In one possible implementation, the method further includes: in the safety mode, based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein F₁Representing a preset first collision potential energy threshold value, and f representing the collision potential energy between the vehicle and the obstacle to be adjusted.

In the embodiment of the present application, in the security mode, based on the second objective function min (f＜F₁) And determining a second motion state of the vehicle, so that the adjusted collision potential energy is smaller than a preset first collision potential energy threshold value, and the safety of determining the target motion state of the vehicle is improved.

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

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

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

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

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

In an eighth aspect, a computer-readable medium is provided, which stores program code, which, when run on a computer, causes the computer to perform the method in the above-mentioned aspects.

In a ninth aspect, a chip system is provided, the chip system comprising a processor for a computing device to perform the functions recited in the above aspects, e.g. to generate, receive, transmit, or process data and/or information recited in the above methods. In one possible design, the system-on-chip further includes a memory for storing program instructions and data necessary for the computing device. The chip system may be formed by a chip, or may include a chip and other discrete devices.

In a tenth aspect, a vehicle is provided that includes an input-output interface, a processor, and a memory. The processor is used for controlling the input and output interface to send and receive signals or information, the memory is used for storing a computer program, and the processor is used for calling and running the computer program from the memory so as to enable the computing device to execute the method in the aspects.

Alternatively, the vehicle may have an automatic driving function.

Drawings

Fig. 1 is a functional block diagram of a vehicle 100 to which the embodiment of the present application is 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 control method of a vehicle according to an embodiment of the present application.

Fig. 4 is a schematic diagram of an 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 view of a moving state between the vehicle and the obstacle in the coordinate system according to the embodiment of the present application.

Fig. 8 is a schematic diagram of collision risk classes of an embodiment of the present application.

Fig. 9 is a schematic diagram of an interactive 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 of another embodiment of the present application.

Detailed Description

The technical solution in the present application will be described below with reference to the accompanying drawings. For convenience of understanding, a scene of intelligent driving is taken as an example in conjunction with fig. 1, and a scene to which the embodiment of the present application is applied is described below.

Fig. 1 is a functional block diagram of a vehicle 100 provided in 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 may control itself while in the autonomous driving mode, and may determine a current state of the vehicle and its surroundings by human operation, determine a possible behavior of at least one other vehicle in the surroundings, and determine a confidence level corresponding to a likelihood that the other vehicle performs the possible behavior, controlling the vehicle 100 based on the determined information. While the vehicle 100 is in the autonomous driving mode, the vehicle 100 may be placed into operation without human interaction.

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

The travel system 102 may include components that provide powered motion to the vehicle 100. In one embodiment, the travel system 102 may include an engine 118, an energy source 119, a 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 hybrid engine of a gasoline engine and an electric motor, or a hybrid engine of an internal combustion engine and an air compression engine. The engine 118 converts the 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 electrical power. The energy source 119 may also provide energy to other systems of the vehicle 100.

The transmission 120 may transmit mechanical power from the engine 118 to the wheels 121. The transmission 120 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 120 may also include other devices, such as a clutch. Wherein the drive shaft may comprise one or more shafts that may be coupled to one or more wheels 121.

The sensor system 104 (also referred to as a "collection device") may include a number of sensors that sense information about the environment surrounding the vehicle 100. For example, the sensor system 104 may include a positioning system 122 (which may be a Global Positioning System (GPS) system, a Beidou system, or other positioning system), an Inertial Measurement Unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. The sensor system 104 may also include sensors of internal systems of the monitored vehicle 100 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors may be used to detect the object and its corresponding characteristics (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function of 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.

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

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.

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

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

The steering system 132 is operable to adjust the heading of the vehicle 100. For example, in one embodiment, 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 brake unit 136 is used to control the deceleration of the vehicle 100. The brake unit 136 may use friction to slow the wheel 121. In other embodiments, the brake unit 136 may convert the kinetic energy of the wheel 121 into an electric current. The brake unit 136 may take other forms to slow the rotational speed of the wheels 121 to control the speed of the vehicle 100.

The computer vision system 140 may be operable to process and analyze images captured by the camera 130 to identify objects and/or features in the environment surrounding the vehicle 100. The objects and/or features may include traffic signals, road boundaries, and obstacles. The 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 an environment, track objects, estimate the speed of objects, and so forth.

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

Obstacle avoidance system 144 is used to identify, assess, and avoid or otherwise negotiate potential obstacles in the environment of vehicle 100.

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

Vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripherals 108. The peripheral devices 108 may include a wireless communication system 146, an in-vehicle computer 148, a microphone 150, and/or speakers 152.

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

The wireless communication system 146 may communicate wirelessly with one or more devices, either directly or via a communication network. For example, the wireless communication System 146 may use 3G cellular communication such as Code Division Multiple Access (CDMA), Global System for Mobile Communications (GSM)/GPRS, or fourth generation (4G) communication such as LTE. Or a 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 utilize an infrared link, bluetooth, or ZigBee (ZigBee) to communicate directly with the device. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 146 may include one or more Dedicated Short Range Communications (DSRC) devices that may include public and/or private data communications between vehicles and/or roadside stations.

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

Some or all of the functionality of the vehicle 100 is controlled by the computer system 112. The computer system 112 may include at least one processor 113, the processor 113 executing instructions 115 stored in a non-transitory computer readable medium, such as data storage 114. The computer system 112 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner.

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 processors, memories, and other elements of the computer 110 in the same blocks, those of ordinary skill in the art will appreciate that the processors, computers, or memories may actually comprise multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard disk drive or other storage medium located in a different housing than the computer 110. Thus, references to a processor or computer are to be understood as including references to a collection of processors or computers or memories which 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 component and the retarding component, may each have their own processor that performs only computations related to the component-specific functions.

In various aspects described herein, the 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 executed on a processor disposed within the vehicle and others are executed by a remote processor, including taking the steps necessary to perform a single maneuver.

In some embodiments, the memory 114 may include instructions 115 (e.g., program logic), and the instructions 115 may be executed by the processor 113 to perform various functions of the vehicle 100, including those described above. The 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 the travel system 102, the sensor system 104, the control system 106, and the peripheral devices 108.

In addition to instructions 115, memory 114 may also store data such as road maps, route information, the location, direction, speed of the vehicle, 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 processor 113 may further execute the planning scheme for vehicle longitudinal motion parameters according to the embodiment of the present application to help the vehicle plan the longitudinal motion parameters, where the specific longitudinal motion parameter planning method may refer to the description of fig. 3 below, and for brevity, details are not described herein again.

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

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

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

Optionally, the above components are only an example, in an actual application, components in the above modules may be added or deleted according to an actual need, and fig. 1 should not be construed as limiting the embodiment of the present invention.

Autonomous vehicles traveling on a roadway, such as vehicle 100 above, may identify objects within their surrounding environment to determine an adjustment to the current speed. The object may be another vehicle, a traffic control device, or another type of object. In some examples, each identified object may be considered independently, and based on the respective characteristics of the object, such as its current speed, acceleration, separation from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to be adjusted.

Optionally, the autonomous vehicle 100 or a computing device associated with the autonomous vehicle 100 (e.g., the computer system 112, the computer vision system 140, the memory 114 of fig. 1) may predict behavior of the identified objects based on characteristics of the identified objects and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so it is also possible to predict the behavior of a single identified object taking all identified objects together into account. The vehicle 100 is able to adjust its speed based on the predicted behaviour of said identified object. In other words, the autonomous vehicle can determine that the vehicle will need to adjust to a steady state (e.g., 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 on which it is traveling, the curvature of the road, the proximity of static and dynamic objects, and so forth.

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 to cause the autonomous vehicle to follow a given trajectory and/or maintain a safe lateral and longitudinal distance from objects in the vicinity of the autonomous vehicle (e.g., cars in adjacent lanes on the road).

The vehicle 100 may be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, an amusement car, a playground vehicle, construction equipment, a trolley, a golf cart, a train, a trolley, etc., and the embodiment of the present invention is not particularly limited.

A scenario in which the embodiment of the present application is applied is described above with reference to fig. 1, and an automatic driving system in which the embodiment of the present application is applied is described below with reference to fig. 2.

FIG. 2 is a schematic diagram of an autopilot system suitable for use with an embodiment of the application, where computer system 101 includes a processor 103, and where processor 103 is coupled to a system bus 105. Processor 103 may be one or more processors, each of which may include one or more processor cores. A display adapter (video adapter)107, which may drive a display 109, the display 109 coupled with system bus 105. System bus 105 is coupled via a bus bridge 111 to an input/output (I/O) bus 113. The I/O interface 115 is coupled to an I/O bus. The I/O interface 115 communicates with various I/O devices, such as an input device 117 (e.g., keyboard, mouse, touch screen, etc.), a multimedia disk (media tray)121 (e.g., CD-ROM, multimedia interface, etc.). A transceiver 123 (which can send and/or receive radio communication signals), a camera 155 (which can capture scenic and motion digital video images), and an external USB interface 125. Wherein, optionally, the interface connected with the I/O interface 115 may be a USB interface.

The processor 103 may be any conventional processor, including a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, or a combination thereof. Alternatively, the processor may be a dedicated device such as an application specific integrated circuit, ASIC. Alternatively, the processor 103 may be a neural network processor or a combination of a neural network processor and a conventional processor as described above.

Optionally, 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 processes described herein are performed on a processor disposed within an autonomous vehicle, others being performed by a remote processor, including taking the actions required to perform a single maneuver.

Computer 101 may communicate with software deploying server 149 via network interface 129. The 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 or Virtual Private Network (VPN). Optionally, the network 127 may also be a wireless network, such as a Wi-Fi network, a cellular network, or the like.

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

The operating system includes a shell (shell)139 and a kernel (kernel) 141. The shell 139 is an interface between the user and the kernel of the operating system. The housing 139 is the outermost layer of the operating system. The shell 139 manages the interaction between the user and the operating system: waits for user input, interprets the user input to the operating system, and processes the output results of the various operating systems.

Kernel 141 is comprised of those portions of the operating system that are used to manage memory, files, peripherals, and system resources. Interacting directly with the hardware, the operating system kernel typically runs processes and provides inter-process communication, CPU slot management, interrupts, memory management, IO management, and the like.

Applications 143 include programs related to controlling the automatic driving of a vehicle, such as programs that manage the interaction of an automatically driven vehicle with obstacles on the road, programs that control the route or speed of an automatically driven vehicle, and programs that control the interaction of an automatically driven vehicle with other automatically driven vehicles on the road. The application program 143 also exists on the system of the software deploying server (deploying server) 149. In one embodiment, computer system 101 may download application program 143 from software deploying server (deploying server)149 when application program 147 needs to be executed.

In some embodiments, the application programs may further include an application program corresponding to a sensing scheme for the target object provided in the embodiments of the present application, where the sensing scheme for the target object in the embodiments of the present application will be specifically described below, and is not described herein again for brevity.

Sensor 153 (also referred to as a "collection device") is associated with computer system 101. The sensor 153 is used to detect the environment surrounding the computer 101. For example, the sensor 153 may detect an object, such as an animal, a vehicle, an obstacle, etc., and further the sensor may detect an environment around the object, such as: the environment surrounding the animal, other animals present around the animal, weather conditions, brightness of the surrounding environment, etc. Alternatively, if the computer 101 is located on an autonomous vehicle, the sensor may be a laser radar, camera, infrared sensor, chemical detector, microphone, or the like.

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

In order to avoid the problems, the application provides a new vehicle control method, namely, on the basis of controlling the movement speed and the movement direction of the vehicle based on the AI model, a scheme for calculating the collision potential energy between the obstacle and the vehicle based on a potential energy function to control the movement speed and the movement direction of the vehicle is added, so that in the process of controlling the movement speed and the movement direction of the vehicle, the higher perception and decision-making performance of the AI model can be kept, and the interpretability and the predictability of the potential energy function can be introduced to improve the safety of controlling the movement speed and the movement direction of the vehicle. A control method of a vehicle according to an embodiment of the present application will be described below with reference to fig. 3.

Fig. 3 is a flowchart of a control method of a vehicle 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.

And 310, acquiring the motion state of the obstacle within a preset range.

Alternatively, the movement state of the obstacle may include information such as a traveling speed, a traveling direction, and a position of the obstacle.

And 320, respectively inputting the motion states of the obstacle into the artificial intelligence AI model and the potential energy function, and determining a first motion state and a 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 potential energy function indicates the possibility of collision of the obstacle with the vehicle by calculating collision potential energy between the obstacle and the vehicle, and the larger the collision potential energy is, the higher the possibility of collision is, and the smaller the collision potential energy is, the lower the possibility of collision is.

Alternatively, 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.

For example, the potential energy function may be expressed as

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

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

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

For another example, the potential energy function may be expressed as f ═ f₁(v”)+f₂(d ") + C", where v "represents the magnitude of the relative velocity between the obstacle and the vehicle, f₁(v ") represents a function based on relative velocity, C" represents a constant, d' represents a relative distance between the obstacle and the vehicle, f₂(d ") is based on a function of relative distance.

And 330, determining a target motion state of the vehicle based on the first motion state and/or the second motion state, wherein the target motion state comprises a target motion speed and a target motion direction of the vehicle.

For ease of understanding, the intelligent driving system of the embodiment of the present application is described below with reference to fig. 4. Fig. 4 is a schematic diagram of an architecture of an intelligent driving system according to another embodiment of the present application. Referring to fig. 4, the smart driving system 400 includes a controller 401, a sensing device 402, an interaction system 403, and an execution system 404.

The sensing device 402 is used for acquiring information of obstacles, such as vehicles, people, infrastructure, and the like around the vehicle, including images of the obstacles and detection information, by using a sensor, wherein the detection information may be different according to the type of the sensing device. For example, when the sensing device is a lidar, the lidar may transmit a detection signal (e.g., a laser beam) to a target, and then compare the received signal reflected from the target (e.g., a target echo) with the transmission signal, and after appropriate processing, obtain detection information about the target, such as parameters of the target distance, orientation, altitude, speed, attitude, or even shape. The information of the obstacle is sent to the controller 401, the controller 401 further determines a traveling track of the vehicle to the destination according to the information of the obstacle, and then sends a control command including a speed to the execution system 404, and the execution system 404 controls the vehicle to travel. The velocity is a vector, and includes a magnitude and a direction, and the magnitude of the velocity may also be referred to as a velocity. In order to meet the high functional safety requirement of safe driving of the vehicle, the controller 401 can utilize a redundant dual-channel design to respectively calculate the moving speed of the vehicle in the same section area.

It should be noted that the sensing device 402 can be understood to be at least partially functionally identical to the sensing system 104 of fig. 1, the actuating system 404 can be understood to be at least partially functionally identical to the traveling system 102 of fig. 1, and the controller 401 can be understood to be at least partially functionally identical to the control system 106 of fig. 1.

The controller 401 may include a working channel for planning a first movement speed and a first movement direction of the vehicle using the AI model, and a safety channel for planning a second movement speed and a second movement direction of the vehicle using the potential energy function. The controller 401 may determine the moving speed and the moving direction of the vehicle in the same section of the driving track by using the working channel and the safety channel, and then the controller 401 determines the target moving speed and the target moving direction to be selected according to the preset conditions. Among them, the target moving speed and the target moving direction selected by the controller 401 may also be referred to as an optimal speed.

The working channel 4011 is configured to perform sensing, decision making, and path planning by using an AI model, and output a first movement speed and a first movement direction of the vehicle, so that the vehicle can meet Quality Management (QM) requirements. The working channel 4011 comprises a first sensing module 40111 and a decision module 40112. The first perception module 40111 is configured to collect information of obstacles around the smart vehicle, which is collected by the perception device, and process the information of the obstacles to obtain road condition information, such as types, speeds, sizes, and infrastructure conditions (e.g., the number of lanes in the current direction, traffic signs, etc.) of the obstacles. The decision module 40112 is configured to further determine a driving direction and speed in a section of area according to the road condition information provided by the first perception module 40111.

The secure channel 4012 includes a second perception module 40121 and a decision and collision avoidance module 40122. The decision and collision avoidance module 40122 is used to provide information about obstacles, such as the relative distance and relative speed of the obstacles to the vehicle, based on the information provided by the second perception module 40121. And determining a second movement speed and a second movement direction of the vehicle by adopting a potential energy function, so that the vehicle can meet the requirements of safety level and safety integrity level ASIL D level of the vehicle when running. The ASIL level is an automotive safety integrity level that describes the probability that a component or system will achieve a given safety objective. ASIL level is determined by three basic factors, namely severity (S), exposure (E), and controllability (C). Severity, which is used to indicate the severity of damage to the lives and properties of people in the vehicle once the risk occurs; exposure, used to refer to the probability that a person or property suffers damage; controllability, which describes how much initiative the driver can take to avoid damage when the risk becomes realistic. The ASIL level can be divided into D, C, B, A four levels from high to low, the security risk of the level D is minimum, and the security risk of the level A is maximum. There is also a quality management requirement beyond the four security levels, which is an order management requirement without security aspects, the security risk being greater for the autonomous driving mode compared to ASIL.

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

The first perception module 40111, the decision module 40112, the second perception module 40121, the decision and collision avoidance module 40122 and the arbitration module 405 in the controller shown in fig. 4 may be implemented by hardware, or may be implemented by software, or both hardware and software may implement the corresponding functions.

Optionally, an interactive system 403 is further included in the system 400, and the interactive system 403 is configured to implement message interaction between the vehicle and the driver, so that the driver can send an operation instruction to the vehicle through the interactive system 403, and know the current state of the vehicle through the interactive system 403.

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

As a possible implementation manner, fig. 4 is only an architecture diagram of a vehicle provided by the present application, and the arbiter can implement its functions by software or hardware in the controller. The arbiter may also implement the role of redundant channel selection by a separate processor.

Optionally, the step 330 includes: if the relative distance between the obstacle and the vehicle is smaller than a first preset distance or the relative speed between the obstacle and the vehicle is higher than a first preset speed, determining 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 a second preset distance, or the relative speed between the obstacle and the vehicle is lower than a second preset speed, determining the target motion state of the vehicle based on the first motion state, wherein 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 fact that 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 means that the possibility of collision between the obstacle and the vehicle is high, and the vehicle is in an emergency, and in this case, in order to improve safety, the second motion state of the vehicle obtained based on the potential energy function may be used as the target motion state of the vehicle.

The fact that 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 can indicate that the possibility of collision between the obstacle and the vehicle is low, and the vehicle is in a non-emergency situation, and 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.

The above-mentioned method for calculating the second motion state of the vehicle based on the potential energy function has many kinds, for example, the vertical direction of collision potential energy between the obstacle and the vehicle can be taken as the motion direction of the vehicle, and the motion speed of the vehicle can be determined based on the motion speed and the relative distance of the obstacle. Of course, the second motion state of the vehicle determined by the method may cause a large change from the current motion state of the vehicle, that is, a large direction adjustment and a large speed adjustment are required to enable the vehicle to avoid the obstacle, which may affect the user experience of the passengers to a certain extent.

Therefore, the embodiment of the application also provides a method for determining the second motion state of the vehicle based on the potential energy function, namely calculating the relative speed and the relative distance between the obstacle and the vehicle; calculating collision potential energy between the obstacle and the vehicle based on the relative speed, the relative distance, and the potential energy function; based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle; or based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein f₀Representing the current collision potential energy of the vehicle and the obstacle, F representing the collision potential energy between the vehicle and the obstacle to which adjustment is required, and F₁Representing a preset first crash potential energy threshold.

The first objective function min (Δ f) may be understood as a moving speed and a moving direction of the vehicle, which are obtained by solving an optimal solution to minimize the change between the current collision potential energy and the adjusted collision potential energy, so that the adjusted moving speed and moving direction of the vehicle may be obtained. Therefore, the solution for solving the moving speed and moving direction of the vehicle based on the first objective function min (Δ f) may be referred to as a "comfort mode" in which the comfort level of the passengers in the vehicle is high because the vehicle has a small amount of change between the adjusted moving speed and moving direction and the current moving speed and moving direction.

The second objective function min (F < F)₁) It is understood that the optimal solution is solved such that the adjusted collision potential is less than a predetermined collision potential threshold, such that the setting F based on the collision potential threshold is based on₁The safety of the adjusted moving speed and moving direction of the vehicle can be ensured. Thus, the above is based on the second objective function min (F < F)₁) The scheme for solving the movement speed and the movement direction of the vehicle can be called as a 'safety mode', and in the mode, the collision potential energy between the vehicle and the obstacle is smaller than a preset collision potential energy threshold value due to the movement speed and the movement direction of the vehicle after adjustment, so that the driving safety of the vehicle is improved.

For ease of understanding, a control method of a vehicle according to an embodiment of the present application is described below with reference to fig. 5. Fig. 5 is a flowchart of control of the vehicle according to the embodiment of the present application. The method shown in fig. 5 includes steps 510 through 590.

And 510, determining the relative speed and the relative distance between the obstacle and the vehicle within the preset range.

The obstacles within the predetermined range may include one or more obstacles.

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

Fig. 6 shows a schematic diagram of 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, a two-dimensional coordinate system is established with the centroid of the vehicle 610 as the origin and the traveling speed direction of the vehicle 610 as the positive X-axis direction, and further includes the obstacle 1, the obstacle 2, and the obstacle 3.

The above-described specific process of determining the relative distance and the relative speed between the vehicle 610 and the obstacle may be divided into the following 3 steps. It should be understood that the following method may be adopted as a method of calculating the relative distance and the relative speed between each obstacle of the plurality of obstacles and the vehicle, and for the sake of brevity, the method of determining the relative distance and the relative speed will be described below by taking one of the obstacles (target obstacle) as an example.

Step 1: acquiring the position of a target obstacle at the T moment in a coordinate system

Acquiring the position of a target obstacle at the T' moment in a coordinate system

Step 2: calculating the current distance of the vehicle 610 from the target obstacle

And step 3: the relative speed of the vehicle 610 and the target obstacle is calculated, the direction of which is directed towards the vehicle 610.

Referring to fig. 7, assume that the target obstacle is from position at time T to time T

Move to the position

Wherein T' ═ T + Δ T,

the speed of the vehicle 610 is

The target obstacle moves a distance of Δ t in time

The relative velocity of the target obstacle to the vehicle 610 is

Projection of target obstacle in the direction of vehicle 610 speedIs composed of

It should be noted that the obstacle may collide with the own vehicle only when the obstacle is in the same direction as the own vehicle and the speed is close to the speed, and the projection of the obstacle in the vehicle speed direction, that is, the speed component at which the obstacle may collide with the own vehicle is calculated. In other words, the projection of the obstacle in the vehicle speed direction is used to indicate a tendency of collision with the own vehicle caused by the obstacle moving in the own vehicle traveling speed direction. The projection of the obstacle in the vehicle speed direction is taken as the relative speed of the obstacle with respect to the vehicle.

And 520, calculating collision potential energy between the obstacle and the vehicle based on the relative speed and the relative distance between the obstacle and the vehicle.

By using

And calculating collision potential energy of the obstacles. The collision potential energy f (O) of the target obstacle O is used to describe the tendency of the target obstacle O to possibly collide with the vehicle, or is referred to as the escape potential energy that the vehicle should have in order to avoid the collision of the target obstacle. For example, the closer the vehicle is to the target obstacle, the stronger the tendency to escape, and the faster the target obstacle is approaching. In the formula, k, alpha and beta are constant coefficients, C is a constant, and the value of C can be flexibly set according to a simulation result and actual experience. Because the velocity v is the velocity of the target obstacle relative to the vehicle, it is a vector that has both magnitude and direction. Therefore, f is also a vector and has the same direction as v.

It is worth to be noted that, when the magnitude of f (o) is calculated, the magnitude of v is substituted into the above formula to calculate and obtain the collision potential energy of the obstacle. The projections of f in the x and y directions are respectively

Wherein v is_xAnd v_yAre the coordinates of v in the X and Y axes, respectively.

Further, the decision and collision avoidance module may determine the location of each surrounding vehicle in the coordinate system shown in fig. 6 based on the relative locations of the surrounding vehicles and the host vehicle. Specifically, after a coordinate system with the own vehicle as an origin is established, the coordinate system is a two-dimensional coordinate system, and in a plane of the two-dimensional coordinate system, a projection position of the surrounding vehicle on the two-dimensional coordinate system is taken as a position of the surrounding vehicle. Optionally, the method for determining the position of the surrounding vehicle in the own vehicle coordinate system further comprises: the coordinates of the surrounding vehicle in the geodetic coordinate system are converted into a two-dimensional coordinate system, and the coordinates of the surrounding vehicle in the two coordinate systems can be converted by adopting a method of the conventional technology in specific implementation, which is not limited in the present application.

It should be noted that if there are a plurality of obstacles, the collision potential energy between each obstacle and the target vehicle may be summed based on the solution of the vector sum, and in this case, f (o) may be understood as the collision potential energy sum of the plurality of obstacles.

And 530, judging whether the obstacle at the early warning level exists or not according to the collision potential energy. If there is an obstacle with an early warning level, step 540 is executed, and if there is no obstacle with an early warning level, the calculation process may be ended, or the calculation process may be restarted.

The risk of collision between the obstacle and the vehicle 610 may be classified into three classes based on collision potential energy in the embodiment of the present application: security level, early warning level, and danger level. The host vehicle has no possibility of collision when the obstacle is at a safe level; when the obstacle is at the early warning level, the vehicle is likely to collide, and the controller can prompt a driver to perform manual operation through the interactive system, so that obstacle avoidance is realized; when the barrier is in a dangerous level, the controller can take over the control right of the vehicle in an emergency, and the collision between the vehicle and other vehicles in the emergency occurring in the execution of other modules of the vehicle is avoided.

It is worth mentioning that the situation that the controller takes over actively when the obstacle is in a dangerous level is limited to the process of performing calculations or data processing by other modules when the vehicle is in the autonomous driving mode. For the manual driving mode, the operation of the vehicle is fully controlled by the driver, and the controller may not intervene in the driving process of the vehicle.

Referring to fig. 8, it is assumed that the collision risk level may preset a first collision potential energy threshold | F according to the obstacle avoidance capability (e.g., performance and size) of the vehicle, respectively₁Second collision potential energy threshold | F₂I, when | F₁|≤|f|<|F₂When | the obstacle belongs to the early warning level; when | F | ≧ F |₂When | the obstacle belongs to a dangerous level; when | f |<|F₁| where the obstacle belongs to a security level, where | F₁|<|F₂If yes, the obstacle 11 in the danger area belongs to the danger level, the obstacles 12, 14 and 13 in the early warning area belong to the early warning level, and the obstacles 15, 16 and 17 in the safety area belong to the safety level.

540, an alert is issued to the driver.

And 550, judging whether the obstacle with the dangerous level exists according to the collision potential energy. If there is an obstacle with a danger level, step 560 is executed, and if there is no obstacle with a danger level, the calculation process may be ended or restarted.

560, issuing an alert to the driver.

570, select safe mode or comfort mode. If the safe mode is selected, step 580 is executed, and if the comfort mode selection step 590 is selected.

580, based on a second objective function min (F < F)₁) And determining 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 solving the feasible solutions, and in this case, the feasible solution with the smallest change with respect to the current speed may be selected as the optimal solution from all the feasible solutions, where v represents the moving speed of the adjusted vehicle 610 and θ represents the moving direction of the adjusted vehicle 610. If there is no feasible solution (v, θ) that satisfies the condition, at this time, the feasible solution (v, θ) that minimizes f may be selected as the optimal solution, while warning the driver.

590 based on the first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining 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 solving the feasible solutions, and in this case, the feasible solution with the smallest change with respect to the current speed may be selected as the optimal solution from all the feasible solutions, where v represents the moving speed of the adjusted vehicle 610 and θ represents the moving direction of the adjusted vehicle 610. If there is no feasible solution (v, θ) that satisfies the condition, at this time, the feasible solution (v, θ) that minimizes Δ f may be selected as the optimal solution, while warning the driver.

Alternatively, the step of "alerting the driver" may be implemented by an interactive system as shown in fig. 9. As shown in fig. 9, the interactive system may prompt the driver to pay attention to the obstacle situation in various forms, and the driver takes over the vehicle or sends an execution instruction to the vehicle to control the vehicle to run. Such as audio prompts, seat shake prompts, in-vehicle flashing light prompts. The human-computer interaction system may also identify different levels and regions with different colors or backgrounds.

Specifically, the man-machine interaction process of the vehicle and the driver can be realized by at least one of the following modes:

mode 1: and prompting that the vehicle has collision risk with surrounding obstacles, a first speed and a second speed through characters on an on-board display interface of the vehicle. For example, Va and Vb in fig. 9 are optional obstacle avoidance directions, and the driver can select any one of the directions in which the vehicle travels. In addition, in addition to the labels Va and Vb as optional obstacle avoidance directions, the collision risk of driving toward the obstacle direction can be indicated by using different signs, for example, in the directions of the obstacles O1 and O2 in fig. 9, the "danger" is indicated by using a five-pointed star sign and characters.

Mode 2: prompting the vehicle to have collision risk, a first speed and a second speed with the surrounding obstacles in the vehicle through voice; the risk of collision of a vehicle with surrounding obstacles is indicated in the vehicle by the vibration of the seat.

Mode 3: the vehicle is informed of the collision risk with surrounding obstacles through the lamp flashing in the vehicle. For dangerous situations, the attention of the driver can be prompted by flashing the light quickly.

As a possible implementation manner, after the vehicle avoids the obstacle according to the above method, the original driving track determined by the decision module may be changed, and the original driving track needs to be re-planned or adjusted further in combination with the road condition of the vehicle at the current time, so as to ensure that the vehicle smoothly reaches the destination specified by the driver.

Alternatively, the vehicle may receive a speed selected by the driver through an interface or voice, in addition to the speed determined by the controller, and may control the vehicle to run at the speed after receiving the speed control command.

Through the human-computer interaction system, the driving experience of a driver can be improved, and the driver is helped to take over and control the vehicle better. On the other hand, the driver can also know the environment condition of the vehicle through the human-computer interaction system, and the fear that the driver cannot know the driving area of the vehicle in an emergency is reduced. In an emergency, the driver can also decide whether to switch the driving mode to the manual driving mode according to the condition displayed by the manual interaction system, and the driver takes over the control right of the vehicle.

As a possible implementation manner, in addition to determining collision potential energy by using the relative speed and the relative distance between the obstacle and the vehicle, and further determining the collision risk between the obstacle and the vehicle, different weights may be added to different types of vehicles according to the type of the obstacle, and the setting of the specific weight may consider the damage degree of collision between the obstacles of different types and the vehicle. And further determining the optimal direction and speed of obstacle avoidance by combining the collision damage degree.

As another possible implementation manner, the controller may send information of other vehicles to the vehicle from other obstacles, including track information of other vehicles, in addition to sensing data of surrounding obstacles detected by the sensing device of the vehicle in which the controller is located, and the obstacle avoidance process of the vehicle may also be implemented by combining the information of the vehicle. Other obstacles may transmit information to the vehicle via vehicle to outside network (V2X) communication technology. When two or more obstacle avoidance directions exist, the collision with the self-vehicle can be confirmed to be changed according to the type of the obstacle, the distance from the self-vehicle and the relative speed, the probability of avoiding the obstacle is displayed through the interface, and a driver can select any one feasible direction as the obstacle avoidance direction through the interface.

As another possible implementation manner, when the second speed confirmed by the safe passage has multiple directions, the safest direction may be selected as the direction of the second speed according to the collision risk degree with the obstacle, wherein the collision risk degree includes one or more of the probability of collision with the obstacle, the damage degree of the collision, and the like, the damage degree of the collision may be calibrated according to the size, the relative speed, and the relative distance of the obstacle, and the larger the obstacle, the faster the relative speed, and the shorter the relative distance, the higher the damage degree of the collision. Through the mode, when a plurality of directions of the second speed exist, the optimal direction can be selected according to the collision risk degree to avoid the obstacle, and the safety of automatic driving is further improved. And the collision risk degree can be displayed to the driver through a human-computer interaction interface, the direction of the final speed is selected by the driver, and then the vehicle is controlled to run according to the speed selected by the driver.

It should be noted that, for simplicity of description, the above method embodiments are described as a series of action combinations, but those skilled in the art should understand that the present application is not limited by the described action sequence, and those skilled in the art should understand that the embodiments described in the specification belong to the preferred embodiments, and the mentioned actions are not necessarily required by the present application.

Other reasonable combinations of steps that can be conceived by one skilled in the art from the above description are also within the scope of the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

The method of the embodiment of the present application is described above with reference to fig. 1 to 9, and the apparatus of the embodiment of the present application is described below with reference to fig. 10 to 12. It should be understood that the apparatus shown in fig. 10 to 12 can implement the steps of the above method, and for brevity, the description is omitted 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 comprises: an acquisition unit 1010 and a processing unit 1020.

The acquisition unit 1010 is used for acquiring the motion state of the barrier within a preset range;

a processing unit 1020, configured to input motion states of the obstacle into an Artificial Intelligence (AI) model and a potential energy function, respectively, and determine a first motion state and a second motion state of a vehicle, where the potential energy function indicates a possibility of collision between the obstacle and the vehicle by calculating collision potential energy between the obstacle and the vehicle, the collision potential energy is greater, the collision potential energy is smaller, the collision is higher, the first motion state is a motion speed and a motion direction of the vehicle calculated based on the AI model, and the second motion state is a motion speed and a motion direction of the vehicle calculated based on the potential energy function model;

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 a relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to a relative speed between the obstacle and the vehicle.

Optionally, as an embodiment, if the relative distance between the obstacle and the vehicle is smaller than a first preset distance, or the relative speed between the obstacle and the vehicle is higher than a first preset 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 the presence of a gas in the gas,

if the relative distance between the obstacle and the vehicle is greater than a second preset distance, or the relative speed between the obstacle and the vehicle is lower than a second preset speed, the processing unit 1020 is further configured to determine a target motion state of the vehicle based on the first motion state, where 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.

Optionally, as an embodiment, the processing unit 1020 is further configured to: based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of said vehicle, wherein f₀Representing the current collision potential energy of the vehicle and the obstacle, and f representing the collision potential energy between the vehicle and the obstacle which needs to be adjusted.

Optionally, as an embodiment, the processing unit 1020 is further configured to: based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein F₁Representing a preset first collision potential energy threshold value, and f representing the collision potential energy between the vehicle and the obstacle to be adjusted.

Optionally, as an embodiment, the potential energy function is

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

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: the device comprises an acquisition unit 1110 and a processing unit 1120, wherein the acquisition unit 1110 is used for acquiring data required by the processing unit 1120.

A processing unit 1120 for calculating a relative speed and a relative distance between the obstacle and the vehicle;

the processing unit 1120 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 a possibility of collision between the obstacle and the vehicle, and the larger the collision potential energy, the higher the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision;

the processing unit 1120 is further configured to obtain an objective function min (Δ f) based on the first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle; or

The processing unit 1120 is further configured to perform a second objective function min (F < F)₁) Determining a second state of motion of the vehicle,

wherein f is₀Representing the current collision potential energy of the vehicle and the obstacle, F representing the collision potential energy between the vehicle and the obstacle to which adjustment is required, F₁Representing a preset first crash potential energy threshold.

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

in comfort mode, based on the first objective function min (Δ f), and Δ f ═ f (f-f)₀) < 0, determining a second motion state of the vehicle.

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

in the safety mode, based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein F₁Representing a preset first collision potential energy threshold value, and f representing the collision potential energy between the vehicle and the obstacle to be adjusted.

In an alternative embodiment, the processing unit 1020 may be the processor 1220, the acquisition unit 1010 may be the communication interface 1230, and the communication device may further include the memory 1210, as shown in fig. 12 in particular.

In an alternative embodiment, the processing unit 1120 may be the processor 1220, the acquisition unit 1110 may be the communication interface 1230, and the communication device may further include the memory 1210, as shown in fig. 12 in particular.

FIG. 12 is a schematic block diagram of a computing device of another embodiment of the present application. The computing device 1200 shown in fig. 12 may include: memory 1210, processor 1220, and communications interface 1230. Wherein, the memory 1210, the processor 1220 and the communication interface 1230 are connected via an internal connection path, the memory 1210 is used for storing instructions, and the processor 1220 is used for executing the instructions stored by the memory 1220 to control the input/output interface 1230 to receive/transmit at least part of the parameters of the second channel model. Alternatively, the memory 1210 may be coupled to the processor 1220 via an interface, or may be integrated with the processor 1220.

It is noted that the communication interface 1230 described above uses a transceiver device, such as but not limited to a transceiver, to enable communication between the communication device 1200 and other devices or communication networks. The communication interface 1230 may also include an input/output interface (I/O interface).

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 1220. The method disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 1210, and the processor 1220 reads the information in the memory 1210 and performs the steps of the above method in combination with the hardware thereof. To avoid repetition, it is not described in detail here.

It should be understood that in the embodiments of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will also be appreciated that in embodiments of the present application, the memory may comprise both read-only memory and random access memory, and may 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 information of the device type.

It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A control method of a vehicle, characterized by comprising:

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 collision potential energy between the obstacle and the vehicle, the collision potential energy is larger, the possibility of collision is higher, the collision potential energy is smaller, the possibility of collision is smaller, the first motion state is the motion speed and the motion direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and the motion direction of the vehicle calculated based on the potential energy function model;

determining a target motion state of the vehicle based on the first motion state and/or the second motion state, wherein the target motion state comprises a target motion speed and a target motion direction of the vehicle.

2. The method of claim 1, wherein the collision potential energy is inversely related to a relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to a relative speed between the obstacle and the vehicle.

3. The method of claim 1 or 2, wherein 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 smaller than a first preset distance, or the relative speed between the obstacle and the vehicle is higher than a first preset speed, determining a target motion state of the vehicle based on the second motion state; and/or the presence of a gas in the gas,

if the relative distance between the obstacle and the vehicle is larger than a second preset distance, or the relative speed between the obstacle and the vehicle is lower than a second preset speed, determining a target motion state of the vehicle based on the first motion state, wherein the first preset distance is smaller than or equal to the second preset distance, and the first preset speed is larger than or equal to the second preset speed.

4. The method of any one of claims 1-3, wherein said inputting the state of motion of the obstacle into a potential energy function, determining a second state of motion of the vehicle, comprises:

based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of said vehicle, wherein f₀Representing the current collision potential energy of the vehicle and the obstacle, and f representing the collision potential energy between the vehicle and the obstacle which needs to be adjusted.

5. The method of any one of claims 1-3, wherein said inputting the state of motion of the obstacle into a potential energy function, determining a second state of motion of the vehicle, comprises:

based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle, wherein F₁Representing a preset first collision potential energy threshold value, and f representing the collision potential energy between the vehicle and the obstacle to be adjusted.

6. The method of any one of claims 1-5, wherein the potential energy function is

7. A control method of a vehicle, characterized by comprising:

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

calculating 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 a likelihood of the obstacle colliding with the vehicle, and the larger the collision potential energy, the higher the likelihood of the collision, and the smaller the likelihood of the collision;

based on a first objective function min (Δ f), and Δ f ═ f-f₀) < 0, determining a second motion state of the vehicle; or

Based on a second objective function min (F < F)₁) Determining a second state of motion of the vehicle,

8. The method of claim 7, wherein the first objective function min (Δ f) is based, and Δ f ═ f (f-f)₀) < 0, determining a second motion state of the vehicle, comprising:

9. The method of claim 8, wherein the method further comprises:

10. The method of any of claims 7-9, wherein the collision potential energy is inversely related to a relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to a relative speed between the obstacle and the vehicle.

11. A control apparatus of a vehicle, characterized by comprising:

the acquisition unit is used for acquiring the motion state of the barrier within a preset range;

the processing unit is used for 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 collision potential energy between the obstacle and the vehicle, the collision potential energy is higher, the collision potential energy is lower, the possibility of collision is lower, the first motion state is the motion speed and the motion direction of the vehicle calculated based on the AI model, and the second motion state is the motion speed and the motion 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 a relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to a relative speed between the obstacle and the vehicle.

13. The apparatus according to claim 11 or 12, wherein the processing unit is further configured to determine a target motion state of the vehicle based on the second motion state 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 is higher than a first preset speed; and/or the presence of a gas in the gas,

if the relative distance between the obstacle and the vehicle is greater than a second preset distance, or the relative speed between the obstacle and the vehicle is lower than a second preset speed, the processing unit is further configured to determine a 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 speed.

14. The apparatus of any of claims 11-13, wherein the processing unit is further to:

15. The apparatus of any of claims 11-13, wherein the processing unit is further to:

16. The apparatus of any one of claims 11-15, wherein the potential energy function is

17. A control apparatus of a vehicle, characterized by comprising:

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

the processing unit is further configured to calculate 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 a possibility of collision between the obstacle and the vehicle, and the larger the collision potential energy, the higher the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision;

the processing unit is further configured to determine a second objective function min (Δ f) based on the first objective function min (Δ f), where Δ f is (f-f)₀) < 0, determining a second motion state of the vehicle; or

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

18. The apparatus as recited in claim 17, said processing unit to further:

19. The apparatus as recited in claim 18, said processing unit to further:

20. The apparatus of any one of claims 17-19, wherein the collision potential energy is inversely related to a relative distance between the obstacle and the vehicle, and the collision potential energy is positively related to a relative speed between the obstacle and the vehicle.

21. A computing device, comprising: at least one processor and memory, the at least one processor coupled with the memory to read and execute instructions in the memory to perform the method of any of claims 1-10.

22. A computer-readable medium, characterized in that the computer-readable medium has stored program code which, when run on a computer, causes the computer to perform the method according to any one of claims 1-10.

23. A chip, comprising: at least one processor and memory, the at least one processor coupled with the memory to read and execute instructions in the memory to perform the method of any of claims 1-10.

24. An autonomous vehicle, comprising: at least one processor and memory, the at least one processor coupled with the memory to read and execute instructions in the memory to perform the method of any of claims 1-10.