Vehicle control method, device and vehicle

Abstract

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

Description

Technical Field

[0001] The present application relates to the field of autonomous driving, and more specifically, to a vehicle control method, device and vehicle. Background Art

[0002] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is also the study of the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, basic AI theory, etc.

[0003] Autonomous driving is a mainstream application in the field of artificial intelligence. Autonomous driving technology relies on the collaboration of computer vision, radar, monitoring devices, and global positioning systems to enable motor vehicles to achieve autonomous driving without the need for active human operation. Autonomous vehicles use various computing systems to help transport passengers from one location to another. Some autonomous vehicles may require some initial or continuous input from operators (such as navigators, drivers, or passengers). Autonomous vehicles allow operators to switch from manual mode to autonomous driving mode or a mode in between. Since autonomous driving technology does not require humans to drive motor vehicles, it can theoretically effectively avoid human driving errors, reduce the occurrence of traffic accidents, and improve highway transportation efficiency. Therefore, autonomous driving technology is receiving more and more attention.

[0004] At present, the motion state of obstacles around the vehicle can be input into the AI ​​model to obtain the vehicle's motion speed and direction through the AI ​​model, so as to adjust the vehicle's motion speed and direction to avoid collision between the vehicle and obstacles around the vehicle. However, in the above-mentioned scheme of using the AI ​​model to control the vehicle's motion speed and direction, the safety of the controlled vehicle cannot be met due to the unexplainability and unpredictability of the AI ​​model. Summary of the invention

[0005] The present application provides a vehicle control method, device and vehicle to improve the safety of adjusting the target movement speed and target movement direction of the vehicle.

[0006] In a first aspect, a vehicle control scheme is provided, comprising: collecting the motion state of an obstacle within a preset range; inputting the motion state of the obstacle into an artificial intelligence (AI) model and a potential energy function respectively to determine a first motion state and a second motion state of the vehicle, wherein the potential energy function indicates the possibility of a collision between the obstacle and the vehicle by calculating a collision potential energy between the obstacle and the vehicle, the greater the collision potential energy, the higher the possibility of the collision, and the smaller the collision potential energy, the smaller the possibility of the collision, the first motion state is the motion speed and direction of the vehicle calculated based on the AI ​​model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model; based on the first motion state and / or the second motion state, determining a target motion state of the vehicle, the target motion state includes a target motion speed and a target motion direction of the vehicle.

[0007] In an embodiment of the present application, the motion state of the obstacle is respectively input into the AI ​​model and the potential energy function to obtain a first motion state and a second motion state, and the target motion state of the vehicle is determined based on the first motion state and the second motion state, so as to avoid the situation in the prior art where the target motion state of the vehicle can only be determined based on the AI ​​model, which is beneficial to improving the safety of determining the target motion state of the vehicle.

[0008] On the other hand, in the embodiment of the present application, determining the second motion state of the vehicle based on the potential energy function is conducive to improving the accuracy of determining the target motion state, and avoids the prior art of determining the target motion state of the vehicle based only on the relative distance between the vehicle and the obstacle.

[0009] In a possible implementation manner, the collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

[0010] In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is negatively correlated with the relative distance between the obstacle and the vehicle, and positively correlated with the relative speed between the obstacle and the vehicle, which is helpful in measuring the possibility of a collision between the obstacle and the vehicle.

[0011] Optionally, the relative speed may be a component of the relative speed between the obstacle and the vehicle in the direction of travel of the vehicle.

[0012] In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is positively correlated with the component of the relative speed in the direction of vehicle travel, which is beneficial to improving the accuracy of the possibility of a collision between the obstacle and the vehicle.

[0013] In a possible implementation, 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 less than a first preset distance, and / 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, 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, 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.

[0014] In an embodiment of the present application, if the relative distance between the obstacle and the vehicle is less than the first preset distance, and / or the relative speed between the obstacle and the vehicle is higher than the first preset speed, that is, in a more urgent 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.

[0015] If the relative distance between the obstacle and the vehicle is greater than the second preset distance, and / or the relative speed between the obstacle and the vehicle is lower than the second preset speed, that is, in non-emergency situations, the target motion state of the vehicle can be determined based on the first motion state determined by the AI ​​model, which is conducive to reducing the time to determine the target motion state of the vehicle.

[0016] In one possible implementation, inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle includes: determining the second motion state of the vehicle based on a first objective function min(Δf), and Δf=(f-f0)<0, wherein f0 represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

[0017] In an embodiment of the present application, based on the first objective function min(Δf), and Δf=(f-f0)<0, the second motion state of the vehicle is determined so that the change between the current collision potential energy and the adjusted collision potential energy is smaller, that is, the change in the vehicle's motion speed is smaller, and the change in the vehicle's motion angle is smaller, so as to improve the comfort of the passengers.

[0018] In one possible implementation, inputting the motion state of the obstacle into a potential energy function to determine the second motion state of the vehicle includes: determining the second motion state of the vehicle based on a second objective function min(f<F1), wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

[0019] In an embodiment of the present application, based on the second objective function min(f<F1), the second motion state of the vehicle is determined so that the adjusted collision potential energy is less than the preset first collision potential energy threshold, which is beneficial to improving the safety of determining the target motion state of the vehicle.

[0020] In a possible implementation, the potential energy function is Among them, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

[0021] In a second aspect, a vehicle control method is provided, comprising: calculating a relative speed and a relative distance between an obstacle and the vehicle; calculating 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 a collision between the obstacle and the vehicle, and the greater the collision potential energy, the higher the possibility of the collision, and the smaller the collision potential energy, the smaller the possibility of the collision; determining a second motion state of the vehicle based on a first objective function min(Δf), and Δf=(f-f0)<0; or determining a second motion state of the vehicle based on a second objective function min(f<F1), wherein f0 represents a current collision potential energy between the vehicle and the obstacle, f represents a collision potential energy to be adjusted between the vehicle and the obstacle, and F1 represents a preset first collision potential energy threshold.

[0022] In the embodiment of the present application, determining the second motion state of the vehicle based on the potential energy function is conducive to improving the accuracy of determining the target motion state, and avoids the prior art of determining the target motion state of the vehicle based only on the relative distance between the vehicle and the obstacle.

[0023] On the other hand, in the embodiment of the present application, based on the first objective function min(Δf), and Δf=(f-f0)<0, the second motion state of the vehicle is determined so that the change between the current collision potential energy and the adjusted collision potential energy is smaller, that is, the change in the vehicle's motion speed is smaller, and the change in the vehicle's motion angle is smaller, so as to improve the comfort of the passengers. Based on the second objective function min(f<F1), the second motion state of the vehicle is determined so that the adjusted collision potential energy is less than the preset first collision potential energy threshold, which is conducive to improving the safety of determining the target motion state of the vehicle.

[0024] In a possible implementation manner, the collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

[0025] In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is negatively correlated with the relative distance between the obstacle and the vehicle, and positively correlated with the relative speed between the obstacle and the vehicle, which is helpful in measuring the possibility of a collision between the obstacle and the vehicle.

[0026] Optionally, the relative speed may be a component of the relative speed between the obstacle and the vehicle in the direction of travel of the vehicle.

[0027] In the embodiment of the present application, the collision potential energy between the obstacle and the vehicle is positively correlated with the component of the relative speed in the direction of vehicle travel, which is beneficial to improving the accuracy of the possibility of a collision between the obstacle and the vehicle.

[0028] In one possible implementation, the determining the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(f-f0)<0, includes: in comfort mode, determining the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(f-f0)<0.

[0029] In an embodiment of the present application, in the comfort mode, based on the first objective function min(Δf), and Δf=(f-f0)<0, the second motion state of the vehicle is determined so that the change between the current collision potential energy and the adjusted collision potential energy is smaller, that is, the change in the vehicle's motion speed is smaller, and the change in the vehicle's motion angle is smaller, so as to improve the comfort of the passengers.

[0030] It should be noted that the above-mentioned comfort mode can be input by the driver or selected by the controller in the vehicle based on the current road conditions, and the embodiments of the present application are not limited to this.

[0031] In a possible implementation, the method further includes: in a safety mode, based on a second objective function min(f<F1), determining a second motion state of the vehicle, wherein F1 represents a preset first collision potential energy threshold, and f represents a collision potential energy that needs to be adjusted between the vehicle and the obstacle.

[0032] In an embodiment of the present application, in the safety mode, based on the second objective function min(f<F1), the second motion state of the vehicle is determined so that the adjusted collision potential energy is less than the preset first collision potential energy threshold, which is beneficial to improving the safety of determining the target motion state of the vehicle.

[0033] It should be noted that the above safety mode can be input by the driver or selected by the controller in the vehicle based on the current road conditions, and the embodiments of the present application are not limited to this.

[0034] In a third aspect, a vehicle control device is provided, the device having the functions of implementing the device in the method design of the first aspect. These functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions.

[0035] In a fourth aspect, a vehicle control device is provided, the device having the functions of implementing the device in the method design of the second aspect. These functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions.

[0036] In a fifth aspect, a computing device is provided, comprising an input / output interface, a processor, and a memory. The processor is used to control the input / output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the method in the first aspect.

[0037] In a sixth aspect, a computing device is provided, comprising an input / output interface, a processor, and a memory. The processor is used to control the input / output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the method in the second aspect.

[0038] In a seventh aspect, a computer program product is provided, the computer program product comprising: a computer program code, when the computer program code is run on a computer, the computer executes the methods in the above aspects.

[0039] In an eighth aspect, a computer-readable medium is provided, wherein the computer-readable medium stores a program code, and when the computer program code runs on a computer, the computer executes the methods in the above aspects.

[0040] In a ninth aspect, a chip system is provided, which includes a processor for a computing device to implement the functions involved in the above aspects, for example, to generate, receive, send, or process the data and / or information involved in the above methods. In a possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for the computing device. The chip system can be composed of a chip, or it can include a chip and other discrete devices.

[0041] In a tenth aspect, a vehicle is provided, comprising an input / output interface, a processor and a memory. The processor is used to control the input / output interface to send and receive signals or information, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computing device executes the methods in the above aspects.

[0042] Optionally, the above-mentioned vehicle may have an autonomous driving function. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a functional block diagram of a vehicle 100 to which an embodiment of the present application is applicable.

[0044] Figure 2 It is a schematic diagram of an automatic driving system applicable to an embodiment of the present application.

[0045] Figure 3 It is a flow chart of a vehicle control method according to an embodiment of the present application.

[0046] Figure 4 It is a schematic diagram of the architecture of an intelligent driving system according to another embodiment of the present application.

[0047] Figure 5 This is a flow chart of vehicle control according to another embodiment of the present application.

[0048] Figure 6 It is a schematic diagram of the relationship between the vehicle and the obstacle in the coordinate system of an embodiment of the present application.

[0049] Figure 7 It is a schematic diagram of the motion state between a vehicle and an obstacle in the coordinate system of an embodiment of the present application.

[0050] Figure 8 It is a schematic diagram of the collision risk level of an embodiment of the present application.

[0051] Fig. 9 It is a schematic diagram of an interactive system according to an embodiment of the present application.

[0052] Fig.10 It is a schematic diagram of a control device for a vehicle according to an embodiment of the present application.

[0053] Fig.11 It is a schematic diagram of a control device for a vehicle according to an embodiment of the present application.

[0054] Fig.12 is a schematic block diagram of a computing device according to another embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will describe the technical solution in this application in conjunction with the accompanying drawings. Figure 1, taking the scenario of intelligent driving as an example, the scenarios to which the embodiments of the present application are applicable are introduced.

[0056] Figure 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 to be in a fully or partially autonomous driving mode. For example, the vehicle 100 can control itself while in the autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human operation, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the possibility of the other vehicle performing the possible behavior, and control the vehicle 100 based on the determined information. When the vehicle 100 is in the autonomous driving mode, the vehicle 100 can be set to operate without human interaction.

[0057] The vehicle 100 may include various subsystems, such as a travel system 102, a sensor system 104, a control system 106, one or more peripheral devices 108, and a power source 110, a computer system 112, and a user interface 116. Optionally, the vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. In addition, each subsystem and element of the vehicle 100 may be interconnected by wire or wirelessly.

[0058] The travel system 102 may include components that provide powered movement for 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 consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 118 converts the energy source 119 into mechanical energy.

[0059] Examples of energy source 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 119 may also provide energy to other systems of vehicle 100.

[0060] The transmission 120 can 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. Among them, the drive shaft may include one or more shafts that can be coupled to one or more wheels 121.

[0061] The sensor system 104 (also referred to as a "collection device") may include several sensors that sense information about the environment surrounding the vehicle 100. For example, the sensor system 104 may include a positioning system 122 (the positioning system may be a global positioning system (GPS) system, or 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 the 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 objects and their corresponding characteristics (position, shape, direction, speed, etc.). Such detection and recognition are key functions for the safe operation of the autonomous vehicle 100.

[0062] Positioning system 122 may be used to estimate the geographic location of vehicle 100. IMU 124 is used to sense position and orientation changes of vehicle 100 based on inertial acceleration. In one embodiment, IMU 124 may be a combination of an accelerometer and a gyroscope.

[0063] The radar 126 may utilize radio signals to sense objects in the surrounding environment of the vehicle 100. In some embodiments, in addition to sensing the target object, the radar 126 may also be used to sense one or more states of the target object's speed, position, and heading.

[0064] 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, a laser scanner, and one or more detectors, among other system components.

[0065] 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.

[0066] The control system 106 controls the operation of the vehicle 100 and its components. The control system 106 may include various elements, including a steering system 132 , a throttle 134 , a brake unit 136 , a computer vision system 140 , a path control system 142 , and an obstacle avoidance system 144 .

[0067] The steering system 132 is operable to adjust the forward direction of the vehicle 100. For example, in one embodiment, it may be a steering wheel system.

[0068] The throttle 134 is used to control the operating speed of the engine 118 and, in turn, the speed of the vehicle 100 .

[0069] The brake unit 136 is used to control the deceleration of the vehicle 100. The brake unit 136 can use friction to slow down the wheel 121. In other embodiments, the brake unit 136 can convert the kinetic energy of the wheel 121 into electric current. The brake unit 136 can also take other forms to slow down the rotation speed of the wheel 121 to control the speed of the vehicle 100.

[0070] The computer vision system 140 can be operated to process and analyze the images captured by the camera 130 in order to identify objects and / or features in the environment surrounding the vehicle 100. The objects and / or features may include traffic signs, 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 can be used to map the environment, track objects, estimate the speed of objects, and the like.

[0071] The route control system 142 is used to determine the driving route of the vehicle 100. In some embodiments, the route control system 142 may combine data from sensors, GPS 122, and one or more predetermined maps to determine the driving route for the vehicle 100.

[0072] The obstacle avoidance system 144 is used to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the environment of the vehicle 100 .

[0073] Of course, in one example, the control system 106 may include additional or alternative components other than those shown and described, or may also reduce some of the components shown above.

[0074] The vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripherals 108. The peripherals 108 may include a wireless communication system 146, an onboard computer 148, a microphone 150, and / or a speaker 152.

[0075] In some embodiments, the peripheral device 108 provides a means for the user of the vehicle 100 to interact with the user interface 116. For example, the onboard computer 148 can provide information to the user of the vehicle 100. The user interface 116 can also operate the onboard computer 148 to receive input from the user. The onboard computer 148 can be operated via a touch screen. In other cases, the peripheral device 108 can provide a means for the vehicle 100 to communicate with other devices located in the vehicle. For example, the microphone 150 can receive audio (e.g., voice commands or other audio input) from the user of the vehicle 100. Similarly, the speaker 152 can output audio to the user of the vehicle 100.

[0076] The wireless communication system 146 can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system 146 can use 3G cellular communications, such as code division multiple access (CDMA), Global System for Mobile Communications (GSM) / GPRS, or fourth generation (4G) communications, such as LTE. Or fifth generation (5G) communications. The wireless communication system 146 can communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system 146 can communicate directly with the device using an infrared link, Bluetooth or ZigBee. 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, which may include public and / or private data communications between vehicles and / or roadside stations.

[0077] The power source 110 can provide power to various components of the vehicle 100. In one embodiment, the power source 110 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries can 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 can be implemented together, such as in some all-electric vehicles.

[0078] Some or all functions of the vehicle 100 are controlled by a computer system 112. The computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable medium such as a 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.

[0079] 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. Figure 1The processor, memory, and other elements of the computer 110 in the same block are functionally illustrated, but it will be appreciated by those of ordinary skill in the art that the processor, computer, or memory may actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, the memory may be a hard drive or other storage medium located in a housing different from the computer 110. Therefore, references to a processor or computer will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering assembly and the deceleration assembly may each have their own processor that performs only calculations related to the functions specific to the component.

[0080] In various aspects described herein, the processor may be located remote from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle and others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0081] In some embodiments, the memory 114 may contain instructions 115 (e.g., program logic) that 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 peripherals 108.

[0082] In addition to instructions 115, memory 114 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, and other information. Such information may be used by vehicle 100 and computer system 112 during operation of vehicle 100 in autonomous, semi-autonomous, and / or manual modes.

[0083] In some embodiments, the processor 113 may also execute the vehicle longitudinal motion parameter planning scheme of the embodiment of the present application to help the vehicle plan the longitudinal motion parameters, wherein the specific longitudinal motion parameter planning method may refer to the following Figure 3 For the sake of brevity, I will not go into details here.

[0084] The user interface 116 is used to provide information to or receive information from a user of the vehicle 100. Optionally, the user interface 116 may include one or more input / output devices within the set of peripherals 108, such as a wireless communication system 146, an onboard computer 148, a microphone 150, and a speaker 152.

[0085] The computer system 112 may control functions of the vehicle 100 based on input 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 may be operable to provide control over many aspects of the vehicle 100 and its subsystems.

[0086] Alternatively, one or more of the above-mentioned components may be installed or associated separately from the vehicle 100. For example, the memory 114 may exist partially or completely separately from the vehicle 100. The above-mentioned components may be communicatively coupled together in a wired and / or wireless manner.

[0087] Optionally, the above components are only examples. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 1 It should not be understood as limiting the embodiments of the present invention.

[0088] An autonomous vehicle traveling on a road, such as vehicle 100 above, can identify objects in its surrounding environment to determine adjustments to the current speed. The objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, spacing from the vehicle, etc., can be used to determine the speed to be adjusted for the autonomous vehicle.

[0089] Optionally, the autonomous vehicle 100 or a computing device associated with the autonomous vehicle 100 (eg, Figure 1 The computer system 112, computer vision system 140, memory 114) can predict the behavior of the identified object based on the characteristics of the identified object 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 all the identified objects can also be considered together to predict the behavior of a single identified object. The vehicle 100 can adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous driving vehicle can determine that the vehicle will need to adjust to a stable state (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors can also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road it is traveling on, the curvature of the road, the proximity of static and dynamic objects, etc.

[0090] 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 vehicle 100 so that the autonomous vehicle follows a given trajectory and / or maintains a safe lateral and longitudinal distance from objects near the autonomous vehicle (e.g., cars in adjacent lanes on the road).

[0091] The vehicle 100 may be a car, truck, motorcycle, bus, ship, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, and cart, etc., and the embodiments of the present invention do not make any particular limitation.

[0092] Combination of the above Figure 1 The following describes the applicable scenarios of the embodiments of the present application. Figure 2 An automatic driving system suitable for executing an embodiment of the present application is introduced.

[0093] Figure 2 1 is a schematic diagram of an automatic driving system applicable to an embodiment of the present application, wherein a computer system 101 includes a processor 103, and the processor 103 is coupled to a system bus 105. The processor 103 may be one or more processors, each of which may include one or more processor cores. A display adapter (video adapter) 107, which may drive a display 109, is coupled to the system bus 105. The system bus 105 is coupled to an input / output (I / O) bus 113 via a bus bridge 111. An I / O interface 115 is coupled to the I / O bus. The I / O interface 115 communicates with a variety of I / O devices, such as an input device 117 (e.g., keyboard, mouse, touch screen, etc.), a multimedia tray 121 (e.g., CD-ROM, multimedia interface, etc.). A transceiver 123 (which may send and / or receive radio communication signals), a camera 155 (which may capture scene fields and dynamic digital video images), and an external USB interface 125. Optionally, the interface connected to the I / O interface 115 may be a USB interface.

[0094] 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 the conventional processors described above.

[0095] Alternatively, in various embodiments described herein, the computer system 101 may be located remotely from the autonomous vehicle and may communicate wirelessly with the autonomous vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the autonomous vehicle, and others are performed by a remote processor, including taking actions required to perform a single maneuver.

[0096] Computer 101 can communicate with software deployment server 149 through network interface 129. Network interface 129 is a hardware network interface, such as a network card. Network 127 can be an external network, such as the Internet, or an internal network, such as Ethernet or a virtual private network (VPN). Optionally, network 127 can also be a wireless network, such as a Wi-Fi network, a cellular network, etc.

[0097] The hard disk drive interface is coupled to the system bus 105. The hard disk drive interface is connected to the hard disk drive. The system memory 135 is coupled to the system bus 105. The data running in the system memory 135 may include the operating system 137 and application programs 143 of the computer 101.

[0098] The operating system includes a shell 139 and a kernel 141. The shell 139 is an interface between the user and the kernel of the operating system. The shell 139 is the outermost layer of the operating system. The shell 139 manages the interaction between the user and the operating system: waiting for user input, interpreting user input to the operating system, and processing various operating system output results.

[0099] The kernel 141 consists of those parts of the operating system that manage memory, files, peripherals, and system resources. Interacting directly with the hardware, the operating system kernel usually runs processes and provides communication between processes, provides CPU time slice management, interrupts, memory management, IO management, etc.

[0100] Application 143 includes programs related to controlling the vehicle's autonomous driving, such as a program for managing the interaction between the autonomous driving vehicle and obstacles on the road, a program for controlling the route or speed of the autonomous driving vehicle, and a program for controlling the interaction between the autonomous driving vehicle and other autonomous driving vehicles on the road. Application 143 also exists on the system of software deployment server 149. In one embodiment, when application 147 needs to be executed, computer system 101 can download application 143 from software deployment server 149.

[0101] In some embodiments, the above-mentioned application may also include an application corresponding to the perception scheme of the target object provided in the embodiment of the present application, wherein the perception scheme of the target object in the embodiment of the present application will be specifically introduced below and will not be repeated here for the sake of brevity.

[0102] Sensor 153 (also called "collection device") is associated with computer system 101. Sensor 153 is used to detect the environment around computer 101. For example, sensor 153 can detect targets, such as animals, vehicles, obstacles, etc., and further, the sensor can detect the environment around the above targets, such as: the environment around the animal, other animals around the animal, weather conditions, brightness of the surrounding environment, etc. Optionally, if computer 101 is located in an autonomous vehicle, the sensor can be a laser radar, a camera, an infrared sensor, a chemical detector, a microphone, etc.

[0103] In existing obstacle avoidance systems, AI models are usually used to plan the speed and direction of a vehicle to avoid collisions between the vehicle and obstacles around it. However, in the above-mentioned scheme of using AI models to control the speed and direction of a vehicle, the safety of the controlled vehicle cannot be met due to the unexplainability and unpredictability of the AI ​​models.

[0104] In order to avoid the above problems, the present application provides a new vehicle control method, that is, based on the above AI model-based control of the vehicle's speed and direction, a new method is added to calculate the collision potential energy between the obstacle and the vehicle based on the potential energy function to control the vehicle's speed and direction. In the process of controlling the vehicle's speed and direction, the high perception and decision-making performance of the AI ​​model can be retained, and the interpretability and predictability of the potential energy function can be introduced to improve the safety of controlling the vehicle's speed and direction. Figure 3 A vehicle control method according to an embodiment of the present application is introduced.

[0105] Figure 3 is a flow chart of a vehicle control method according to an embodiment of the present application. It should be understood that Figure 3 The method shown can be Figure 1 The obstacle avoidance system in Figure 2 The processor 103 is shown executing. Figure 3 The method shown includes steps 310 to 330 .

[0106] 310, collecting the motion status of obstacles within a preset range.

[0107] Optionally, the movement state of the obstacle may include information such as the travel speed, travel direction, and position of the obstacle.

[0108] 320, the motion state of the obstacle is input into the artificial intelligence AI model and the potential energy function respectively to determine the first motion state and the second motion state of the vehicle, wherein the first motion state is the motion speed and direction of the vehicle calculated based on the AI ​​model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function.

[0109] The above potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating the collision potential energy between the obstacle and the vehicle. The greater the collision potential energy, the higher the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision.

[0110] Optionally, the collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

[0111] For example, the above potential energy function can be expressed as Among them, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

[0112] For another example, the above potential energy function can also be expressed as Wherein, k', α', β' represent constant coefficients, C' represents a constant, ν' represents the magnitude of the relative speed between the obstacle and the vehicle, and d' represents the relative distance between the obstacle and the vehicle.

[0113] For another example, the potential energy function can also be expressed as f”=f1(v”)+f2(d”)+C”, wherein ν” represents the magnitude of the relative speed between the obstacle and the vehicle, f1(v”) represents a function based on the relative speed, C” represents a constant, d’ represents the relative distance between the obstacle and the vehicle, and f2(d”) is a function based on the relative distance.

[0114] 330 , based on the first motion state and / or the second motion state, determine a target motion state of the vehicle, where the target motion state includes a target motion speed and a target motion direction of the vehicle.

[0115] For ease of understanding, the following Figure 4 An intelligent driving system according to an embodiment of the present application is introduced. Figure 4 FIG. 1 is a schematic diagram of the architecture of an intelligent driving system according to another embodiment of the present invention. Figure 4 , the intelligent driving system 400 includes a controller 401, a perception device 402, an interaction system 403 and an execution system 404.

[0116] The sensing device 402 is used to obtain information about obstacles such as vehicles, people and infrastructure around the vehicle through sensors, including images and detection information of the obstacles, wherein the detection information may be different depending on the type of sensing device. For example, when the sensing device is a laser radar, the laser radar can transmit a detection signal (e.g., a laser beam) to the target, and then compare the received signal reflected from the target (e.g., the target echo) with the transmitted signal, and after appropriate processing, the relevant detection information of the target can be obtained, such as the target distance, direction, height, speed, attitude, and even shape parameters. The above obstacle information will be sent to the controller 401, and the controller 401 will further determine the driving trajectory of the vehicle to the destination based on the obstacle information, and then send a control instruction including speed to the execution system 404, and the execution system 404 will control the vehicle to drive. Among them, the speed is a vector, including magnitude and direction, and the magnitude of the speed can also be called rate. In order to meet the high functional safety requirements for safe driving of the vehicle, the controller 401 can use a redundant dual-channel design to calculate the movement speed of the vehicle in the same area.

[0117] It should be noted that the above-mentioned sensing device 402 can be understood as Figure 1 The sensing system 104 in the embodiment has at least some functions in common, and the execution system 404 can be understood as Figure 1 The travel system 102 shown in FIG. 1 has at least some functions in common, and the controller 401 described above can be understood as Figure 1 The control system 106 shown is at least partially functionally identical.

[0118] The controller 401 may include a working channel and a safety channel. The working channel is used to plan the first movement speed and first movement direction of the vehicle using the AI ​​model, and the safety channel plans the second movement speed and second movement direction of the vehicle using the potential energy function. The controller 401 may use the working channel and the safety channel to respectively determine the movement speed and movement direction of the vehicle in the same section of the driving trajectory, and then the controller 401 determines the target movement speed and target movement direction to be selected according to preset conditions. Among them, the target movement speed and target movement direction selected by the controller 401 may also be referred to as the optimal speed.

[0119] The above-mentioned working channel 4011 is used to use the AI ​​model for perception, decision-making and path planning, and output the first movement speed and first movement direction of the vehicle, so that the vehicle can meet the quality management (QM) requirements. The working channel 4011 includes a first perception module 40111 and a decision module 40112. The first perception module 40111 is used to collect information about obstacles around the intelligent vehicle collected by the perception device, and process the obstacle information to obtain road condition information, such as obstacle type, speed, size, road and other infrastructure conditions (such as the number of lanes in the current direction, traffic signs, etc.). The decision module 40112 is used to further determine the direction and speed of travel in a certain area based on the road condition information provided by the first perception module 40111.

[0120] The above-mentioned safety channel 4012 includes a second perception module 40121 and a decision and collision avoidance module 40122. Among them, the decision and collision avoidance module 40122 is used to determine the second movement speed and the second movement direction of the vehicle according to the information of the obstacle provided by the second perception module 40121, such as the relative distance and relative speed between the obstacle and the vehicle. The potential energy function is used to determine the second movement speed and the second movement direction of the vehicle, so that the vehicle driving can meet the safety level and meet the requirements of the automotive safety integrity level ASIL D level. Among them, the ASIL level is the automotive safety integrity level, which is used to describe the probability of a component or system achieving a given safety goal. The ASIL level is determined by three basic elements, namely severity (S), exposure rate (E), and controllability (C). Severity is used to indicate the severity of the damage to the life and property of the people in the car once the risk occurs; exposure rate is used to refer to the probability of damage to people or property; controllability is used to describe the extent to which the driver can take active measures to avoid damage when the risk becomes a reality. ASIL levels can be divided into four levels from high to low: D, C, B, and A. Level D has the lowest safety risk, while level A has the highest safety risk. In addition to the four safety levels, there is also a quality management requirement. The directive management requirements have no safety requirements. For the autonomous driving mode, the safety risk is greater than ASIL.

[0121] As a possible implementation, Figure 4 The first perception module 40111 and the second perception module 40112 can be combined into one perception module, which obtains obstacle information from the sensor device 402, and further calculates road condition information such as the distance of the obstacle relative to the vehicle and the relative speed of the obstacle relative to the vehicle based on this information, and sends the required content to the decision module 40112 and the decision and collision avoidance module 40122 respectively according to the information required by them.

[0122] Figure 4The first perception module 40111, decision module 40112, second perception module 40121, decision and collision avoidance module 40122 and arbitration module 405 in the controller shown can be implemented by hardware or software, or the corresponding functions can be implemented by both hardware and software.

[0123] Optionally, the system 400 further includes an interactive system 403, which is used to implement message interaction between the vehicle and the driver, so that the driver can send operation instructions to the vehicle through the interactive system 403 and understand the current status of the vehicle through the interactive system 403.

[0124] As a possible embodiment, the system 400 also includes an arbitrator 405, which receives the first movement speed and first movement direction planned by the working channel 4011 and the second movement speed and second movement direction planned by the safety channel 4012, and the arbitrator 405 selects the target movement speed and target movement direction according to preset conditions.

[0125] As a possible implementation, Figure 4 This is only a schematic diagram of a vehicle architecture provided by this application. The arbitrator can realize its functions by software or hardware in the controller. The arbitrator can also realize the function of redundant channel selection by an independent processor.

[0126] Optionally, the above step 330 includes: 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 the 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 the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, determining the target motion state of the vehicle based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.

[0127] If the relative distance between the obstacle and the vehicle is less than the first preset distance, or the relative speed between the obstacle and the vehicle is higher than the first preset speed, it can be said that the possibility of a collision between the obstacle and the vehicle is high, which is an emergency situation. In this case, in order to improve safety, the second motion state of the vehicle obtained based on the potential energy function can be used as the target motion state of the vehicle.

[0128] If the relative distance between the obstacle and the vehicle is greater than the second preset distance, or the relative speed between the obstacle and the vehicle is lower than the second preset speed, it can be said that the possibility of a collision between the obstacle and the vehicle is low and it is a non-emergency situation. In this case, the first motion state of the vehicle obtained based on the AI ​​model can be used as the target motion state of the vehicle.

[0129] There are many methods for calculating the second motion state of the vehicle based on the potential energy function. For example, the vertical direction of the collision potential energy between the obstacle and the vehicle can be used as the motion direction of the vehicle, and the motion speed of the vehicle can be determined based on the motion speed and relative distance of the obstacle. Of course, the second motion state of the vehicle determined by the above method may result in a large change from the current motion state of the vehicle, that is, a large direction adjustment and speed adjustment are required to enable the vehicle to avoid the above obstacles, which will affect the user experience of passengers to a certain extent.

[0130] Therefore, an embodiment of the present application also provides a method for determining the second motion state of a vehicle based on a potential energy function, namely, calculating the relative speed and relative distance between the obstacle and the vehicle; calculating the collision potential energy between the obstacle and the vehicle based on the relative speed, relative distance, and potential energy function; determining the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(f-f0)<0; or determining the second motion state of the vehicle based on the second objective function min(f<F1), wherein f0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle, and F1 represents a preset first collision potential energy threshold.

[0131] The first objective function min(Δf) can be understood as finding the optimal solution to minimize the change between the current collision potential energy and the adjusted collision potential energy in the vehicle's speed and direction of movement. In this way, the vehicle's adjusted speed and direction of movement can be obtained. Therefore, the solution for solving the vehicle's speed and direction of movement based on the first objective function min(Δf) can be called a "comfort mode". In this mode, since the change between the vehicle's adjusted speed and direction of movement and the current speed and direction of movement is small, the comfort level of the passengers in the vehicle is high.

[0132] The above second objective function min(f<F1) can be understood as finding the optimal solution to make the adjusted collision potential energy less than the preset collision potential energy threshold. In this way, based on the setting F1 of the above collision potential energy threshold, the safety of the vehicle's adjusted movement speed and movement direction can be achieved. Therefore, the above solution for solving the vehicle's movement speed and movement direction based on the second objective function min(f<F1) can be called a "safety mode". In this mode, the collision potential energy between the vehicle and the obstacle is less than the preset collision potential energy threshold due to the vehicle's adjusted movement speed and movement direction, which is conducive to improving the safety of vehicle driving.

[0133] For ease of understanding, the following Figure 5 A vehicle control method according to an embodiment of the present application is introduced. Figure 5 It is a flow chart of the control of the vehicle according to the embodiment of the present application. Figure 5The method shown includes steps 510 to 590 .

[0134] 510, determining the relative speed and relative distance between the obstacle and the vehicle within a preset range.

[0135] The obstacles within the above-mentioned preset range may include one or more obstacles.

[0136] A two-dimensional coordinate system can be established with the center of mass of the vehicle as the origin and the direction of the vehicle's movement speed as the positive direction of the X-axis, and the position of each obstacle in the two-dimensional coordinate system within a preset range can be determined, and the relative distance between the obstacle and the vehicle can be determined based on the position of the obstacle in the two-dimensional coordinate system.

[0137] Figure 6 A schematic diagram showing the relationship between a vehicle and an obstacle in a coordinate system of an embodiment of the present application is shown. Figure 6 The coordinate system shown takes the center of mass of vehicle 610 as the origin and the direction of the vehicle 610's travel speed as the positive direction of the X-axis to establish a two-dimensional coordinate system, which also includes obstacles 1, 2, and 3.

[0138] The specific process of determining the relative distance and relative speed between the vehicle 610 and the obstacle can be divided into the following three steps. It should be understood that the method of calculating the relative distance and relative speed between each obstacle in the plurality of obstacles and the vehicle can adopt the following method. For the sake of simplicity, the following takes one obstacle (target obstacle) as an example to introduce the method of determining the relative distance and relative speed.

[0139] Step 1: Collect the position of the target obstacle in the coordinate system at time T Collect the position of the target obstacle in the coordinate system at time T'

[0140] Step 2: Calculate the current distance between the vehicle 610 and the target obstacle

[0141] Step 3: Calculate the relative speed between the vehicle 610 and the target obstacle, where the direction of the relative speed points to the vehicle 610 .

[0142] See also Figure 7 , assuming that the target obstacle moves from position Move to location Where, T'=T+Δt, The speed of vehicle 610 is Then the distance that the target obstacle moves in time Δt is The relative speed between the target obstacle and the vehicle 610 is The projection of the target obstacle along the speed direction of vehicle 610 is

[0143] It should be noted that an obstacle is likely to collide with the vehicle only when it is in the same direction as the vehicle and has a similar speed. Calculating the projection of the obstacle along the vehicle speed direction is to determine the speed component at which the obstacle and the vehicle may collide. In other words, the projection of the obstacle along the vehicle speed direction is used to indicate the tendency of the obstacle to collide with the vehicle due to movement in the direction of the vehicle's speed. The projection of the obstacle along the vehicle speed direction is taken as the relative speed of the obstacle relative to the vehicle.

[0144] At 520 , based on the relative speed and relative distance between the obstacle and the vehicle, a collision potential energy between the obstacle and the vehicle is calculated.

[0145] use Calculate the collision potential energy of the obstacle. The collision potential energy f(O) of the target obstacle O is used to describe the tendency of the target obstacle O to collide with the vehicle, or is called the escape potential energy that the vehicle should have to avoid the collision with 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 approaches, the stronger the tendency to escape. In the above formula, k, α, β are constant coefficients, and C is a constant. The value of C can be flexibly set according to simulation results and actual experience. Because the speed ν is the speed of the target obstacle relative to the vehicle, it is a vector with both magnitude and direction. Therefore, f is also a vector and its direction is the same as that of v.

[0146] It is worth noting that when calculating the size of f(O), the size of v is substituted into the above formula to calculate the collision potential energy of the obstacle. The projections of f in the x and y directions are respectively Among them, v x and v y are the coordinates of ν on the X-axis and Y-axis respectively.

[0147] Furthermore, the decision and collision avoidance module can determine the relative position of each surrounding vehicle to the vehicle. Figure 6 The position in the coordinate system shown. Specifically, when the coordinate system with the vehicle as the origin is established, the coordinate system is a two-dimensional coordinate system. In the plane of the two-dimensional coordinate system, the projection position of the surrounding vehicles in the two-dimensional coordinate system is taken as the position of the surrounding vehicles. Optionally, the method for determining the position of the surrounding vehicles in the vehicle coordinate system also includes: converting the coordinates of the surrounding vehicles in the geodetic coordinate system into the two-dimensional coordinate system. In specific implementation, the conventional technology can be used to realize the coordinate conversion of the surrounding vehicles in the two coordinate systems, which is not limited in this application.

[0148] It should be noted that if there are multiple obstacles, the collision potential energy between each obstacle and the target vehicle can be summed based on the vector sum solution method. In this case, the above f(O) can be understood as the collision potential energy sum of multiple obstacles.

[0149] 530, judging whether there is an obstacle of the warning level according to the collision potential energy. If there is an obstacle of the warning level, executing step 540, if there is no obstacle of the warning level, the calculation process can be ended or restarted.

[0150] In the embodiment of the present application, the risk of collision between the obstacle and the vehicle 610 can be divided into three levels based on the collision potential: safety level, warning level and danger level. When the obstacle is at the safety level, there is no possibility of collision with the vehicle; when the obstacle is at the warning level, there is a possibility of collision with the vehicle, and the controller can prompt the driver to perform manual operation through the interactive system to achieve obstacle avoidance; when the obstacle is at the danger level, the controller can take over the control of the vehicle in an emergency to avoid the vehicle colliding with other vehicles in an emergency when other modules of the vehicle are executing processing.

[0151] It is worth noting that when obstacles are at a dangerous level, the controller actively takes over only when other modules perform calculations or data processing when the vehicle is in autonomous driving mode. For manual driving mode, the operation of the vehicle is fully controlled by the driver, and the controller does not need to intervene in the vehicle's driving process.

[0152] See also Figure 8 , assuming that the collision risk level can be preset according to the vehicle's obstacle avoidance capability (such as performance and size), the first collision potential energy threshold |F1| and the second collision potential energy threshold |F2|, when |F1|≤|f|<|F2|, the obstacle belongs to the warning level; when |f|≥|F2|, the obstacle belongs to the danger level; when |f|<|F1|, the obstacle belongs to the safety level, among which, |F1|<|F2|, then obstacle 11 in the danger zone belongs to the danger level, obstacles 12, 14, and 13 in the warning zone belong to the warning level, and obstacles 15, 16, and 17 in the safety zone belong to the safety level.

[0153] 540, alert the driver.

[0154] 550, judging whether there is an obstacle of a dangerous level according to the collision potential energy. If there is an obstacle of a dangerous level, executing step 560, if there is no obstacle of a dangerous level, the calculation process can be ended or restarted.

[0155] 560, alerting the driver.

[0156] 570, select safety mode or comfort mode. If safety mode is selected, execute step 580, if comfort mode is selected, execute step 590.

[0157] 580 , determining a second motion state of the vehicle based on a second objective function min(f<F1).

[0158] It should be noted that there may be countless solutions (v, θ) in the process of finding feasible solutions. In this case, the feasible solution with the smallest change relative to the current speed can be selected as the optimal solution among all feasible solutions, where v represents the speed of the vehicle 610 after adjustment, and θ represents the direction of movement of the vehicle 610 after adjustment. If there is no feasible solution (v, θ) that meets the conditions, in this case, the feasible solution (v, θ) with the smallest f can be selected as the optimal solution, and a warning can be issued to the driver.

[0159] 590 , based on the first objective function min(Δf), and Δf=(f−f0)<0, determine a second motion state of the vehicle.

[0160] It should be noted that there may be countless solutions (v, θ) in the process of finding feasible solutions. In this case, the feasible solution with the smallest change relative to the current speed can be selected as the optimal solution among all feasible solutions, where v represents the speed of the vehicle 610 after adjustment, and θ represents the direction of movement of the vehicle 610 after adjustment. If there is no feasible solution (v, θ) that meets the conditions, in this case, the feasible solution (v, θ) with the smallest Δf can be selected as the optimal solution, and a warning can be issued to the driver.

[0161] Optionally, the step of "issuing an alarm to the driver" can be accomplished by Fig. 9 The interactive system shown in Figure 1 is implemented. Fig. 9 As shown, the interactive system can remind the driver to pay attention to obstacles in various ways, and the driver can take over the vehicle or send execution instructions to the vehicle to control the vehicle's driving. For example, audio prompts, seat vibration prompts, and flashing lights inside the vehicle. The human-computer interaction system can also use different colors or backgrounds to identify different levels and areas.

[0162] Specifically, the human-machine interaction process between the vehicle and the driver can be realized by using at least one of the following methods:

[0163] Method 1: The vehicle's onboard display interface uses text to prompt that the vehicle has a risk of collision with surrounding obstacles, as well as the first speed and the second speed. For example, Fig. 9 In the figure, Va and Vb are optional obstacle avoidance directions, and the driver can choose any one of them as the direction of vehicle travel. In addition, in addition to marking Va and Vb as optional obstacle avoidance directions, different signs can also be used to indicate the collision risk of driving in the direction of obstacles. For example, Fig. 9 In the directions of obstacles O1 and O2, use five-pointed star symbols and text prompts "Danger".

[0164] Mode 2: Prompting in the vehicle through voice that there is a risk of collision between the vehicle and the surrounding obstacles, the first speed and the second speed; Prompting in the vehicle through seat vibration that there is a risk of collision between the vehicle and the surrounding obstacles.

[0165] Method 3: Flashing lights in the vehicle to warn the driver of the risk of collision with surrounding obstacles. In dangerous situations, the lights can also be flashed quickly to alert the driver.

[0166] As a possible implementation method, after the vehicle avoids obstacles according to the above method, the original driving trajectory determined by the decision module may be changed. It is also necessary to further re-plan or adjust the original driving trajectory based on the current road conditions of the vehicle to ensure that the vehicle arrives smoothly at the destination specified by the driver.

[0167] Optionally, in addition to using the above-mentioned controller to determine the speed, the vehicle can also receive the speed selected by the driver through an interface or voice, etc. After receiving the above-mentioned speed control instruction, the vehicle can be controlled at this speed.

[0168] The above human-computer interaction system can improve the driver's driving experience and help the driver to better take over and control the vehicle. On the other hand, the human-computer interaction system can also let the driver understand the vehicle's environment and reduce the driver's fear of not being able to know the vehicle's driving area in an emergency. In an emergency, the driver can also decide whether to switch the driving mode to the manual driving mode based on the situation displayed by the manual interaction system, and take over the control of the vehicle.

[0169] As a possible implementation method, in addition to using the relative speed and relative distance between the obstacle and the ego vehicle to determine the collision potential and then the collision risk between the obstacle and the ego vehicle, different weights can be added to different types of vehicles according to the type of obstacle. The specific weight setting can consider the degree of damage caused by the collision between different types of obstacles and the ego vehicle. The optimal direction and speed for obstacle avoidance can be further determined based on the above collision damage degree.

[0170] As another possible implementation, in addition to relying on the sensor data of the surrounding obstacles detected by the sensor equipment of the vehicle in which it is located, the controller can also send information of other vehicles to the vehicle, including the trajectory information of other vehicles, and the vehicle's own obstacle avoidance process can also be combined with the information of the above vehicles to achieve the vehicle's obstacle avoidance process. Among them, other obstacles can send information to the vehicle through vehicle to everything (V2X) communication technology. When there are two or more obstacle avoidance directions, the probability of collision with the obstacle can also be confirmed based on the type of obstacle, the distance from the vehicle and the relative speed, and the probability of avoiding the obstacle can be displayed through the interface. The driver can select any feasible direction as the obstacle avoidance direction through the interface.

[0171] As another possible implementation, when there are multiple directions for the second speed confirmed by the safety channel, the safest direction can also be selected as the direction of the second speed according to the degree of collision risk with the obstacle, wherein the degree of collision risk includes one or more factors such as the probability of collision with the obstacle and the degree of damage caused by the collision. The degree of damage caused by the collision can be calibrated according to the size, relative speed and relative distance of the obstacle. The larger the obstacle, the faster the relative speed and the shorter the relative distance, the higher the degree of damage caused by the collision. In the above manner, when there are multiple directions of the second speed, the optimal direction can be selected according to the degree of collision risk to avoid obstacles, further improving the safety of autonomous driving. Moreover, the above-mentioned degree of collision risk can be displayed to the driver through the human-computer interaction interface, and the driver selects the direction of the final speed, thereby controlling the vehicle to travel at the speed selected by the driver.

[0172] It is worth noting that, for the above method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required for the present application.

[0173] Other reasonable step combinations that can be thought of by those skilled in the art based on the above description also fall within the scope of protection of this application. Secondly, those skilled in the art should also be familiar with the fact that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0174] Combination of the above Figures 1 to 9 The method of the embodiment of the present application is introduced below. Figures 10 to 12 Introduce the device of the embodiment of the present application. It should be understood that Figures 10 to 12 The device shown can implement each step in the above method, and for the sake of brevity, it will not be described in detail here.

[0175] Fig.10 It is a schematic diagram of a control device for a vehicle according to an embodiment of the present application. Fig.10 The device 1000 shown includes: a collection unit 1010 and a processing unit 1020 .

[0176] The collection unit 1010 is used to collect the motion status of obstacles within a preset range;

[0177] The processing unit 1020 is used to input the motion state 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 the vehicle, wherein the potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating a collision potential energy between the obstacle and the vehicle, the greater the collision potential energy, the higher the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision, the first motion state is the motion speed and direction of the vehicle calculated based on the AI ​​model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model;

[0178] 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.

[0179] Optionally, as an embodiment, the collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

[0180] Optionally, as an embodiment, 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, the processing unit 1020 is further configured to determine a target motion state of the vehicle based on the second motion state; and / or,

[0181] 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 used to determine the target motion state of the vehicle based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.

[0182] Optionally, as an embodiment, the processing unit 1020 is also used to: determine the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(f-f0)<0, wherein f0 represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted.

[0183] Optionally, as an embodiment, the processing unit 1020 is further used to: determine the second motion state of the vehicle based on a second objective function min(f<F1), wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

[0184] Optionally, as an embodiment, the potential energy function is Among them, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

[0185] Fig.11 It is a schematic diagram of a control device for a vehicle according to an embodiment of the present application. Fig.11 The device 1100 shown includes: a collection unit 1110 and a processing unit 1120 , and the collection unit 1110 is used to collect data required by the processing unit 1120 .

[0186] The processing unit 1120 is used to calculate the relative speed and relative distance between the obstacle and the vehicle;

[0187] 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 a collision between the obstacle and the vehicle, and the greater the collision potential energy, the higher the possibility of the collision, and the smaller the collision potential energy, the smaller the possibility of the collision;

[0188] The processing unit 1120 is further configured to determine a second motion state of the vehicle based on a first objective function min(Δf), and Δf=(f-f0)<0; or

[0189] The processing unit 1120 is further configured to determine a second motion state of the vehicle based on a second objective function min(f<F1),

[0190] Wherein, f0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted, and F1 represents a preset first collision potential energy threshold.

[0191] Optionally, as an embodiment, the processing unit is further configured to:

[0192] In the comfort mode, based on the first objective function min(Δf), and Δf=(f-f0)<0, a second motion state of the vehicle is determined.

[0193] Optionally, as an embodiment, the processing unit is further configured to:

[0194] In the safety mode, the second motion state of the vehicle is determined based on the second objective function min(f<F1), wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

[0195] In an optional embodiment, the processing unit 1020 may be a processor 1220, the acquisition unit 1010 may be a communication interface 1230, and the communication device may further include a memory 1210. Fig.12 shown.

[0196] In an optional embodiment, the processing unit 1120 may be a processor 1220, the acquisition unit 1110 may be a communication interface 1230, and the communication device may further include a memory 1210. Fig.12 shown.

[0197] Fig.12 is a schematic block diagram of a computing device according to another embodiment of the present application. Fig.12 The computing device 1200 shown may include: a memory 1210, a processor 1220, and a communication interface 1230. The memory 1210, the processor 1220, and the communication interface 1230 are connected through an internal connection path, the memory 1210 is used to store instructions, and the processor 1220 is used to execute the instructions stored in the memory 1220 to control the input / output interface 1230 to receive / send at least part of the parameters of the second channel model. Optionally, the memory 1210 can be coupled to the processor 1220 through an interface, or can be integrated with the processor 1220.

[0198] It should be noted that the communication interface 1230 uses a transceiver device such as but not limited to a transceiver to implement communication between the communication device 1200 and other devices or communication networks. The communication interface 1230 may also include an input / output interface.

[0199] In the implementation process, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 1220 or an instruction in the form of software. The method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor for execution. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 1210, and the processor 1220 reads the information in the memory 1210 and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0200] 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 (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0201] It should also be understood that in the embodiment of the present application, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the processor may also include a non-volatile random access memory. For example, the processor may also store information about the device type.

[0202] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0203] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0204] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0205] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0206] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0207] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0208] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0209] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0210] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: include: Collect the movement status of obstacles within the preset range; Input the motion state of the obstacle into an artificial intelligence AI model and a potential energy function respectively to determine a first motion state and a second motion state of the vehicle, wherein the potential energy function is determined according to the relative speed and relative distance between the obstacle and the vehicle, and the potential energy function indicates the possibility of collision between the obstacle and the vehicle by calculating the collision potential energy between the obstacle and the vehicle, the greater the collision potential energy, the higher the possibility of collision, and the smaller the collision potential energy, the smaller the possibility of collision, the first motion state is the motion speed and direction of the vehicle calculated based on the AI ​​model, and the second motion state is the motion speed and direction of the vehicle calculated based on the potential energy function model; Based on the first motion state and / or the second motion state, a target motion state of the vehicle is determined, where the target motion state includes a target motion speed and a target motion direction of the vehicle.

2. The method according to claim 1, characterized in that The collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

3. The method according to claim 1 or 2, characterized in that 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 less 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, 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 target motion state of the vehicle is determined based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.

4. The method according to claim 1 or 2, characterized in that: The step of inputting the motion state of the obstacle into a potential energy function to determine a second motion state of the vehicle includes: Based on the first objective function min(Δf), and Δf=(f-f0)<0, the second motion state of the vehicle is determined, wherein f0 represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted.

5. The method according to claim 1 or 2, characterized in that: The step of inputting the motion state of the obstacle into a potential energy function to determine a second motion state of the vehicle includes: Based on the second objective function min(f<F1), the second motion state of the vehicle is determined, wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

6. The method according to claim 1 or 2, characterized in that: The potential energy function is Among them, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

7. A vehicle control method, characterized in that: include: Calculate the relative speed and distance between the obstacle and the vehicle; Calculating 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 potential energy function is determined according to the relative speed and relative distance between the obstacle and the vehicle, and the collision potential energy indicates a possibility of a collision between the obstacle and the vehicle, wherein the greater the collision potential energy, the higher the possibility of the collision, and the smaller the collision potential energy, the smaller the possibility of the collision; Based on the first objective function min(Δf), and Δf=(f-f0)<0, determining a second motion state of the vehicle; or Based on the second objective function min(f<F1), determining the second motion state of the vehicle, Wherein, f0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted, and F1 represents a preset first collision potential energy threshold.

8. The method according to claim 7, characterized in that The determining of the second motion state of the vehicle based on the first objective function min(Δf), and Δf=(f-f0)<0, comprises: In the comfort mode, based on the first objective function min(Δf), and Δf=(f-f0)<0, a second motion state of the vehicle is determined.

9. The method according to claim 8, characterized in that The method further comprises: In the safety mode, the second motion state of the vehicle is determined based on the second objective function min(f<F1), wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

10. The method according to any one of claims 7 to 9, characterized in that The collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

11. A vehicle control device, characterized in that: include: A collection unit, used to collect the motion status of obstacles within a preset range; a processing unit, configured to input the motion state 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 the vehicle, wherein the potential energy function is determined according to a relative speed and a relative distance between the obstacle and the vehicle, and the potential energy function indicates a possibility of a collision between the obstacle and the vehicle by calculating a collision potential energy between the obstacle and the vehicle, wherein a greater collision potential energy increases the possibility of the collision, and a smaller collision potential energy decreases the possibility of the collision, wherein the first motion state is a motion speed and direction of the vehicle calculated based on the AI ​​model, and the second motion state is a motion speed and direction of the vehicle calculated based on the potential energy function model; The processing unit is further used 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 device according to claim 11, characterized in that The collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

13. The device according to claim 11 or 12, characterized in that 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, the processing unit is further configured to determine 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, or the relative speed between the obstacle and the vehicle is lower than a second preset speed, the processing unit is further used to determine the target motion state of the vehicle based on the first motion state, the first preset distance is less than or equal to the second preset distance, and the first preset speed is greater than or equal to the second preset speed.

14. The device according to claim 11 or 12, characterized in that The processing unit is further used for: Based on the first objective function min(Δf), and Δf=(f-f0)<0, the second motion state of the vehicle is determined, wherein f0 represents the current collision potential energy between the vehicle and the obstacle, and f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted.

15. The device according to claim 11 or 12, characterized in that The processing unit is further used for: Based on the second objective function min(f<F1), the second motion state of the vehicle is determined, wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

16. The device according to claim 11 or 12, characterized in that The potential energy function is Among them, k, α, β represent constant coefficients, C represents a constant, ν represents the magnitude of the relative speed between the obstacle and the vehicle, and d represents the relative distance between the obstacle and the vehicle.

17. A vehicle control device, characterized in that: include: 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 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 potential energy function is determined according to the relative speed and relative distance between the obstacle and the vehicle, and the collision potential energy indicates a possibility of a collision between the obstacle and the vehicle, wherein a greater collision potential energy indicates a higher possibility of a collision, and a smaller collision potential energy indicates a smaller possibility of a collision; The processing unit is further configured to determine a second motion state of the vehicle based on a first objective function min(Δf), and Δf=(f-f0)<0; or The processing unit is further configured to determine a second motion state of the vehicle based on a second objective function min(f<F1), Wherein, f0 represents the current collision potential energy between the vehicle and the obstacle, f represents the collision potential energy between the vehicle and the obstacle that needs to be adjusted, and F1 represents a preset first collision potential energy threshold.

18. The device according to claim 17, characterized in that The processing unit is further used for: In the comfort mode, based on the first objective function min(Δf), and Δf=(f-f0)<0, a second motion state of the vehicle is determined.

19. The device according to claim 18, characterized in that The processing unit is further used for: In the safety mode, the second motion state of the vehicle is determined based on the second objective function min(f<F1), wherein F1 represents a preset first collision potential energy threshold, and f represents the collision potential energy that needs to be adjusted between the vehicle and the obstacle.

20. The device according to any one of claims 17 to 19, characterized in that The collision potential energy is inversely correlated with the relative distance between the obstacle and the vehicle, and the collision potential energy is positively correlated with the relative speed between the obstacle and the vehicle.

21. A computing device, characterized in that include: At least one processor and a memory, wherein the at least one processor is coupled to the memory and configured to read and execute instructions in the memory to perform the method according to any one of claims 1 to 10.

22. A computer readable medium, characterized in that The computer-readable medium stores a program code, and when the computer program code is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 10.

23. A chip, characterized in that: include: At least one processor and a memory, wherein the at least one processor is coupled to the memory and configured to read and execute instructions in the memory to perform the method according to any one of claims 1 to 10.

24. An autonomous driving vehicle, characterized in that: include: At least one processor and a memory, wherein the at least one processor is coupled to the memory and configured to read and execute instructions in the memory to perform the method according to any one of claims 1 to 10.

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