Control method, device, storage medium and equipment for autonomous driving vehicle

Abstract

This application provides a control method, apparatus, storage medium, and device for an autonomous vehicle. The method first obtains road surface environment information for a first road surface area. Subsequently, the road surface environment information is input into a trained prediction model, and a predicted friction coefficient for the first road surface area, output by the trained prediction model, is obtained. The trained prediction model is generated based on a sample training set that includes historical road surface environment information and measured friction coefficients corresponding to the historical road surface environment information. The predicted friction coefficient is then used to determine a route for the autonomous vehicle to travel. This method allows the predicted friction coefficient for the first road surface area to be obtained based on the road surface environment information, thereby enabling timely determination of whether the autonomous driving function is suitable for the route to be traveled based on the predicted friction coefficient, thereby ensuring the proper operation of the autonomous driving function.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a control method, device, storage medium and equipment for an autonomous driving vehicle. Background Art

[0002] In recent years, with the continuous development and improvement of intelligent driving technology, autonomous vehicles have attracted widespread attention. The Society of Automotive Engineers (SAE) standard divides intelligent driving capabilities into six levels, from L0 to L5. Autonomous vehicles with intelligent driving capabilities at level L4 and below must operate within a specified operational design domain (ODD).

[0003] The operational design domain has certain requirements for the road friction coefficient. For example, if the road friction coefficient is less than 0.5, the autonomous vehicle's intelligent driving function cannot be activated. Currently, due to the lack of road friction coefficient information in the high-precision maps shared by autonomous vehicles, autonomous vehicles cannot timely obtain the road friction coefficient corresponding to the route to be traveled. As a result, it is impossible to determine whether the autonomous driving function is applicable to the route to be traveled, which in turn limits its use. Summary of the Invention

[0004] The present application provides a control method, device, storage medium and equipment for an autonomous driving vehicle to solve the technical problem in the prior art of being unable to timely obtain the road friction coefficient corresponding to the route to be traveled.

[0005] In a first aspect, the present application provides a method for controlling an autonomous driving vehicle, the method comprising:

[0006] Acquiring road surface environment information of a first road surface area;

[0007] Inputting the road surface environment information into a trained prediction model, and obtaining a predicted friction coefficient of the first road surface area output by the trained prediction model, wherein the trained prediction model is generated based on a sample training set, the sample training set including historical road surface environment information and a measured friction coefficient corresponding to the historical road surface environment information;

[0008] A route for the autonomous driving vehicle to travel is determined based on the predicted friction coefficient.

[0009] In an optional implementation, determining the route to be traveled by the autonomous driving vehicle includes:

[0010] Correcting the predicted friction coefficient according to a correction coefficient of the friction coefficient;

[0011] The route to be traveled by the autonomous driving vehicle is determined based on the corrected predicted friction coefficient.

[0012] In an optional embodiment, the correction coefficient of the friction coefficient is determined based on the predicted friction coefficient of a second road surface area and the measured friction coefficient of the second road surface area, where the second road surface area is the road surface area on which the autonomous driving vehicle has been traveling or is currently traveling.

[0013] In an optional embodiment, the first road area is on the initial route to be traveled by the autonomous driving vehicle.

[0014] In an optional implementation, determining the route to be traveled by the autonomous driving vehicle includes:

[0015] determining whether the corrected predicted friction coefficient is within a preset friction coefficient range;

[0016] If not, adjusting the initial route to be traveled to a first route to be traveled according to the current position information of the autonomous driving vehicle and the position information of the first road surface area;

[0017] Control the autonomous driving vehicle to travel along the first route to be traveled.

[0018] In an optional embodiment, after determining whether the corrected predicted friction coefficient is within a preset friction coefficient range, the method further includes:

[0019] If the corrected predicted friction coefficient is within the preset friction coefficient range, the autonomous driving vehicle is controlled to travel along the initial route to be traveled.

[0020] In a second aspect, the present application provides a control device for an autonomous driving vehicle, the device comprising:

[0021] An acquisition module, configured to acquire road surface environment information of a first road surface area;

[0022] a prediction module, configured to input the road surface environment information into a trained prediction model and obtain a predicted friction coefficient of the first road surface area output by the trained prediction model, wherein the trained prediction model is generated based on a sample training set, wherein the sample training set includes historical road surface environment information and a measured friction coefficient corresponding to the historical road surface environment information;

[0023] A control module is used to determine a route to be traveled by the autonomous driving vehicle based on the predicted friction coefficient.

[0024] In an optional embodiment, the control module is specifically used to correct the predicted friction coefficient based on a correction coefficient of the friction coefficient; and determine the route to be traveled by the autonomous driving vehicle based on the corrected predicted friction coefficient.

[0025] In an optional embodiment, the correction coefficient of the friction coefficient is determined based on the predicted friction coefficient of a second road surface area and the measured friction coefficient of the second road surface area, where the second road surface area is the road surface area on which the autonomous driving vehicle has been traveling or is currently traveling.

[0026] In an optional embodiment, the first road area is on the initial route to be traveled by the autonomous driving vehicle.

[0027] In an optional embodiment, the control module is specifically used to determine whether the corrected predicted friction coefficient is within a preset friction coefficient range; if not, adjusting the initial route to be traveled to a first route to be traveled based on the current position information of the autonomous driving vehicle and the position information of the first road surface area; and controlling the autonomous driving vehicle to travel along the first route to be traveled.

[0028] In an optional embodiment, the control module is further configured to control the autonomous driving vehicle to travel along the initial route to be traveled if the corrected predicted friction coefficient is within the preset friction coefficient range.

[0029] In a third aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in any one of the first aspects.

[0030] In a fourth aspect, the present invention further provides a computer storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the method as described in any one of the first aspects.

[0031] In a fifth aspect, the present application further provides an electronic device comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method as described in any one of the first aspects.

[0032] This application provides a control method, apparatus, storage medium, and device for an autonomous vehicle. The method first obtains road surface environment information for a first road surface area. Subsequently, the road surface environment information is input into a trained prediction model, and a predicted friction coefficient for the first road surface area, output by the trained prediction model, is obtained. The trained prediction model is generated based on a sample training set that includes historical road surface environment information and measured friction coefficients corresponding to the historical road surface environment information. The predicted friction coefficient is then used to determine a route for the autonomous vehicle to travel. This method allows the predicted friction coefficient for the first road surface area to be obtained based on the road surface environment information, thereby enabling timely determination of whether the autonomous driving function is suitable for the route to be traveled based on the predicted friction coefficient, thereby ensuring the proper operation of the autonomous driving function. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0034] Figure 1 A system architecture diagram of a control system for an autonomous driving vehicle provided in an embodiment of the present application;

[0035] Figure 2 A flowchart of a control method for an autonomous driving vehicle provided in an embodiment of the present application;

[0036] Figure 3 A flowchart of another method for controlling an autonomous vehicle provided in an embodiment of the present application;

[0037] Figure 4 A flowchart of another method for controlling an autonomous driving vehicle provided in an embodiment of the present application;

[0038] Figure 5 A schematic diagram of the structure of a control device for an autonomous driving vehicle provided in an embodiment of the present application;

[0039] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] In recent years, with the continuous development and improvement of intelligent driving technology, autonomous vehicles have attracted widespread attention. The Society of Automotive Engineers (SAE) standard divides intelligent driving capabilities into six levels, from L0 to L5. Autonomous vehicles with intelligent driving capabilities at level L4 and below must operate within a specified operational design domain (ODD).

[0042] The operational design domain has certain requirements for the road friction coefficient. For example, if the road friction coefficient is less than 0.5, the autonomous vehicle's intelligent driving function cannot be activated. Currently, due to the lack of road friction coefficient information in the high-precision maps shared by autonomous vehicles, autonomous vehicles cannot timely obtain the road friction coefficient corresponding to the route to be traveled. As a result, it is impossible to determine whether the autonomous driving function is applicable to the route to be traveled, which in turn limits its use.

[0043] In order to solve the above-mentioned technical problems, the embodiments of the present application provide a control method, device, storage medium and equipment for an autonomous driving vehicle. By obtaining the predicted friction coefficient of the road surface area on the route to be traveled based on the road surface environment information of the road surface area, it is possible to timely determine whether the autonomous driving function is suitable for the route to be traveled based on the predicted friction coefficient, thereby ensuring the normal use of the autonomous driving function.

[0044] The following describes the system architecture of a control system for an autonomous driving vehicle involved in this application.

[0045] Figure 1 This is a system architecture diagram of a control system for an autonomous driving vehicle provided in an embodiment of the present application. Figure 1 As shown, the system architecture includes: a sensor component 101 and a controller 102 .

[0046] Sensor assembly 101 is connected to controller 102. Sensor assembly 101 is configured to input detected road surface environment information of a first road surface area into controller 102. Controller 102 is configured to obtain a predicted friction coefficient for the first road surface area based on the road surface environment information of the first road surface area and a trained prediction model; and to determine a desired route for the autonomous vehicle based on the predicted friction coefficient for the first road surface area.

[0047] The sensor components may include, but are not limited to, camera sensors, lidar sensors, and the like. The controller may be a single controller or a controller group consisting of multiple controllers. For example, the controller may include an autonomous driving controller and a vehicle motion controller, etc., although this embodiment of the present application does not impose any restrictions on this.

[0048] It should be understood that the system architecture of the control system of the autonomous driving vehicle of the technical solution of this application can be Figure 1 The system architecture in, but not limited to, can also be other types of system architecture.

[0049] It can be understood that the control method of the autonomous driving vehicle of the technical solution of the present application can be implemented by the control device of the autonomous driving vehicle provided in the embodiment of the present application. The control device of the autonomous driving vehicle can be part or all of a certain device, such as a controller.

[0050] The following uses a controller integrated with or installed with relevant execution code as an example to describe the technical solutions of the embodiments of the present application in detail with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0051] Figure 2 This is a flow chart of a control method for an autonomous driving vehicle provided in an embodiment of the present application. This embodiment relates to a process of controlling an autonomous driving vehicle based on a road friction coefficient. Figure 2 As shown, the method includes:

[0052] S201: Acquire road surface environment information of a first road surface area.

[0053] In an embodiment of the present application, the controller may first obtain road surface environment information of a first road surface area, and then determine the route to be traveled by the autonomous driving vehicle based on the obtained road surface environment information.

[0054] It is understood that when an autonomous vehicle departs from starting point A and travels to destination B, it may have at least one travel route. If the current position of the autonomous vehicle during travel is recorded as position C, then the route from starting point A to position C is the traveled route, and the route from position C to destination B is the route to be traveled.

[0055] Furthermore, the first road surface area may be a road surface area on the initial route to be traveled by the autonomous driving vehicle, or other road surface areas; the road surface environment information may include image feature information, lidar point cloud, etc., which is not limited in this embodiment of the present application.

[0056] In some embodiments, the controller can obtain the above-mentioned road environment information through a sensor component. For example, the controller can obtain image feature information of the first road area through a camera sensor; or obtain a lidar point cloud of the first road area through a lidar sensor, etc., which is not limited in this embodiment of the present application.

[0057] S202: Inputting road surface environment information into the trained prediction model, and obtaining a predicted friction coefficient of the first road surface area output by the trained prediction model.

[0058] In this step, after obtaining the road surface environment information of the first road surface area, the controller may input the road surface environment information into the trained prediction model and obtain the predicted friction coefficient of the first road surface area output by the model.

[0059] Among them, the trained prediction model is generated based on the sample training set, and the sample training set may include historical road surface environment information and the measured friction coefficient corresponding to the historical road surface environment information. The embodiment of the present application does not limit the type of prediction model. In some embodiments, the above-mentioned prediction model can be built based on a convolutional neural network. Exemplarily, the controller inputs the image features, lidar point cloud, etc. of the first road surface area detected by the on-board camera sensor and / or lidar sensor into the prediction model built based on the convolutional neural network, and then obtains the predicted friction coefficient of the first road surface area output by the model.

[0060] In other embodiments, after inputting the image features of the first road surface area, the LiDAR point cloud and other road surface environmental information into the trained prediction model, the controller can also obtain the model output of the road surface material, road surface characteristics and other information of the first road surface area. Among them, the road surface material can include cement, concrete, asphalt, gravel, etc.; the road surface characteristics can include dry, wet, waterlogged, ice and snow, etc. It can be understood that the reflection energy of the LiDAR sensor in road surface areas with different materials or characteristics is different, and the visual images of the camera sensor in road surface areas with different materials or characteristics are also different. By training the deep learning neural network with a large amount of data, the road surface material and road surface characteristics can be regressed and classified. Furthermore, based on the road surface material, road surface characteristics and other information output by the model, the controller can also obtain the predicted friction coefficient of the first road surface area. For example, the friction coefficient of a dry asphalt road surface is 0.6.

[0061] In other embodiments, after obtaining the predicted friction coefficient of the first road surface area, the controller may further correct the predicted friction coefficient based on a correction factor of the friction coefficient; and then determine the route to be traveled by the autonomous vehicle based on the corrected predicted friction coefficient. The correction factor of the friction coefficient is determined based on the predicted friction coefficient of the second road surface area and the measured friction coefficient of the second road surface area, where the second road surface area is the road surface area on which the autonomous vehicle has traveled or is currently traveling. For example, if the controller has already obtained the predicted friction coefficient of the second road surface area and obtains the measured friction coefficient of the second road surface area when passing through the second road surface area, the correction factor of the friction coefficient may be determined based on the predicted and measured friction coefficients of the second road surface area; and then, based on the correction factor of the friction coefficient, the predicted friction coefficient of the first road surface area obtained later may be corrected.

[0062] S203: Determine a route for the autonomous driving vehicle to travel based on the predicted friction coefficient.

[0063] In this step, after obtaining the predicted friction coefficient of the first road surface area output by the model, the controller can determine the route to be traveled by the autonomous driving vehicle based on the predicted friction coefficient.

[0064] The embodiments of the present application do not limit how the controller determines the route to be traveled by the autonomous vehicle. In some embodiments, the controller may first determine whether the corrected predicted friction coefficient is within a preset friction coefficient range; if not, the controller may adjust the initial route to be traveled to a first route to be traveled based on the current position information of the autonomous vehicle and the position information of the first road surface area; and then control the autonomous vehicle to travel along the first route to be traveled. The preset friction coefficient range may be set to a friction coefficient greater than or equal to 0.5; the first route to be traveled may be a route that does not include the above-mentioned first road surface area, and this application does not limit this.

[0065] In other embodiments, if the corrected predicted friction coefficient is within a preset friction coefficient range, the controller may control the autonomous vehicle to travel along the initial route to be traveled. Furthermore, when the autonomous vehicle reaches a first road surface area, the controller may obtain the measured friction coefficient of the first road surface area; and then adjust the correction factor for the friction coefficient based on the predicted and measured friction coefficients of the first road surface area.

[0066] Furthermore, the controller can package and upload the measured friction coefficient of the first road area and its high-precision positioning information to a cloud database server. The cloud database server can then update the friction coefficient information for the first road area in the HD map and automatically distribute the updated HD map to vehicles traveling on the road section where the first road area is located. The intelligent driving controllers of these vehicles directly read the friction coefficient information for the first road area from the HD map, eliminating the need for the vehicle to make a prediction. This improves the accuracy, efficiency, and speed of obtaining the road friction coefficient.

[0067] High-precision positioning information can include Global Positioning System (GPS) and real-time kinematic (RTK) carrier phase differential technology. The controller can upload this packaged information to a cloud server via the vehicle's telematics box (T-BOX). If multiple vehicles upload this packaged information to the cloud server simultaneously, road friction coefficient information for that road section can be quickly and efficiently generated.

[0068] It can be understood that the method for predicting the road friction coefficient provided in the embodiment of the present application is not only applicable to autonomous driving vehicles, but also to vehicles that do not have or have not turned on the autonomous driving function, thereby providing road condition information for reference to the drivers of these vehicles, so that the drivers can slow down and drive safely in slippery road areas.

[0069] It should be noted that existing technologies cannot systematically address the friction coefficient issues faced by autonomous vehicles. The road friction coefficient prediction method for autonomous vehicles, provided in the embodiments of this application, can accurately determine whether the road ahead meets the friction coefficient requirements of the operational design domain (ODD) corresponding to high-order intelligent driving functions. The vehicle's intended route can then be determined based on the predicted road friction coefficient, and the vehicle's lateral and longitudinal kinematic control models can then be optimized to control the vehicle's travel along the intended route.

[0070] This application provides a control method for an autonomous vehicle. The method first obtains road surface environment information for a first road surface area. Subsequently, the road surface environment information is input into a trained prediction model, and a predicted friction coefficient for the first road surface area is obtained from the trained prediction model. The trained prediction model is generated based on a sample training set that includes historical road surface environment information and measured friction coefficients corresponding to the historical road surface environment information. A route to be driven by the autonomous vehicle is then determined based on the predicted friction coefficient. This method allows the predicted friction coefficient for the first road surface area to be obtained based on the road surface environment information, thereby enabling timely determination of whether the autonomous driving function is suitable for the route to be driven based on the predicted friction coefficient, thereby ensuring the proper operation of the autonomous driving function.

[0071] Based on the above embodiment, how to correct the predicted friction coefficient is described below. Figure 3 A flow chart of another control method for an autonomous driving vehicle provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method includes:

[0072] S301: Acquire road surface environment information of a first road surface area.

[0073] S302: Inputting road surface environment information into the trained prediction model, and obtaining the predicted friction coefficient of the first road surface area output by the trained prediction model.

[0074] S303: Correct the predicted friction coefficient according to the correction coefficient of the friction coefficient.

[0075] S304: Determine a route for the autonomous driving vehicle to travel based on the corrected predicted friction coefficient.

[0076] The technical terms, technical effects, technical features, and optional implementation methods of S301-S304 can be referred to Figure 2 The S201-S203 shown are understood, and the repeated content will not be repeated here.

[0077] Based on the above embodiment, the following describes how to determine the route to be traveled by the autonomous driving vehicle. Figure 4 A flow chart of another method for controlling an autonomous vehicle provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the method includes:

[0078] S401: Acquire road surface environment information of a first road surface area.

[0079] S402: Inputting road surface environment information into the trained prediction model, and obtaining the predicted friction coefficient of the first road surface area output by the trained prediction model.

[0080] S403: Correct the predicted friction coefficient according to the correction coefficient of the friction coefficient.

[0081] S404: Determine whether the corrected predicted friction coefficient is within a preset friction coefficient range.

[0082] If yes, execute step S405; if no, execute step S406.

[0083] S405: Control the autonomous driving vehicle to travel along the initial route to be traveled.

[0084] S406. Adjust the initial route to be traveled to the first route to be traveled based on the current position information of the autonomous driving vehicle and the position information of the first road surface area.

[0085] S407: Control the automatic driving vehicle to travel along the first route to be traveled.

[0086] The technical terms, technical effects, technical features, and optional implementation methods of S401-S407 can be referred to Figure 2 The S201-S203 shown are understood, and the repeated content will not be repeated here.

[0087] This application provides a control method for an autonomous vehicle. The method first obtains road surface environment information for a first road surface area. Subsequently, the road surface environment information is input into a trained prediction model, and a predicted friction coefficient for the first road surface area is obtained from the trained prediction model. The trained prediction model is generated based on a sample training set that includes historical road surface environment information and measured friction coefficients corresponding to the historical road surface environment information. A route to be driven by the autonomous vehicle is then determined based on the predicted friction coefficient. This method allows the predicted friction coefficient for the first road surface area to be obtained based on the road surface environment information, thereby enabling timely determination of whether the autonomous driving function is suitable for the route to be driven based on the predicted friction coefficient, thereby ensuring the proper operation of the autonomous driving function.

[0088] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented by hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0089] Figure 5This is a schematic diagram of the structure of a control device for an autonomous vehicle provided in an embodiment of the present application. The control device for the autonomous vehicle can be implemented by software, hardware, or a combination of both, and can be, for example, the controller in the above embodiment to execute the control method for the autonomous vehicle in the above embodiment. Figure 5 As shown, the control device 500 of the autonomous driving vehicle includes:

[0090] An acquisition module 501 is configured to acquire road surface environment information of a first road surface area;

[0091] Prediction module 502, configured to input road surface environment information into a trained prediction model and obtain a predicted friction coefficient of a first road surface area output by the trained prediction model, wherein the trained prediction model is generated based on a sample training set including historical road surface environment information and measured friction coefficients corresponding to the historical road surface environment information;

[0092] The control module 503 is used to determine the route to be traveled by the autonomous driving vehicle based on the predicted friction coefficient.

[0093] In an optional implementation, the control module 503 is specifically configured to correct the predicted friction coefficient based on a correction coefficient of the friction coefficient; and determine a route for the autonomous driving vehicle to travel based on the corrected predicted friction coefficient.

[0094] In an optional embodiment, the correction coefficient of the friction coefficient is determined based on the predicted friction coefficient of the second road surface area and the measured friction coefficient of the second road surface area, where the second road surface area is the road surface area on which the autonomous driving vehicle has been traveling or is currently traveling.

[0095] In an optional embodiment, the first road surface area is on an initial route to be traveled by the autonomous driving vehicle.

[0096] In an optional embodiment, the control module 503 is specifically used to determine whether the corrected predicted friction coefficient is within a preset friction coefficient range; if not, the initial route to be traveled is adjusted to the first route to be traveled based on the current position information of the autonomous driving vehicle and the position information of the first road surface area; and the autonomous driving vehicle is controlled to travel along the first route to be traveled.

[0097] In an optional embodiment, the control module 503 is further configured to control the autonomous driving vehicle to travel along the initial route to be traveled if the corrected predicted friction coefficient is within a preset friction coefficient range.

[0098] Need to explain, Figure 5The control device for the autonomous driving vehicle provided in the illustrated embodiment can be used to execute the control method for the autonomous driving vehicle provided in any of the above embodiments. The specific implementation methods and technical effects are similar and will not be described in detail here.

[0099] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 may include: at least one processor 601 and a memory 602. Figure 6 An electronic device is shown using a processor as an example.

[0100] The memory 602 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer operation instructions.

[0101] The memory 602 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0102] The processor 601 is used to execute computer-executable instructions stored in the memory 602 to implement the above-mentioned control method of the autonomous driving vehicle; wherein, the processor 601 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0103] Alternatively, in a specific implementation, if the communication interface, memory 602, and processor 601 are implemented independently, the communication interface, memory 602, and processor 601 may be interconnected via a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or only one type of bus.

[0104] Optionally, in a specific implementation, if the communication interface, memory 602 and processor 601 are integrated on a chip, the communication interface, memory 602 and processor 601 can complete communication through an internal interface.

[0105] The present application also provides a chip comprising a processor and an interface. The interface is used to input and output data or instructions processed by the processor. The processor is used to execute the method provided in the above method embodiment. The chip can be used in a control device for an autonomous vehicle.

[0106] An embodiment of the present application also provides a program, which, when executed by a processor, is used to execute the control method for the autonomous driving vehicle provided in the above method embodiment.

[0107] An embodiment of the present application also provides a program product, such as a computer-readable storage medium, which stores instructions. When the program product is run on a computer, it enables the computer to execute the control method of the autonomous driving vehicle provided by the above method embodiment.

[0108] The present application also provides a computer-readable storage medium, which may include any medium capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program information used in the aforementioned autonomous vehicle control method.

[0109] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for an autonomous driving vehicle, characterized in that: The method comprises: Acquiring road environment information of a first road area, wherein the first road area is on an initial to-be-traveled route of the autonomous driving vehicle; Inputting the road surface environment information into a trained prediction model, and obtaining a predicted friction coefficient of the first road surface area output by the trained prediction model, wherein the trained prediction model is generated based on a sample training set, the sample training set including historical road surface environment information and a measured friction coefficient corresponding to the historical road surface environment information; Calculating a correction factor for the friction coefficient based on the predicted friction coefficient and the measured friction coefficient for a second road surface area, wherein the second road surface area is an area of the road surface on which the autonomous driving vehicle has traveled or is currently traveling; Correcting the predicted friction coefficient of the first road surface area using the correction coefficient to obtain a corrected predicted friction coefficient; Determine whether the corrected predicted friction coefficient is within a preset friction coefficient range. If not, adjust the initial route to be traveled to the first route to be traveled based on the current position information of the autonomous driving vehicle and the position information of the first road surface area, and control the autonomous driving vehicle to travel along the first route to be traveled.

2. The method according to claim 1, characterized in that After determining whether the corrected predicted friction coefficient is within a preset friction coefficient range, the method further includes: If the corrected predicted friction coefficient is within the preset friction coefficient range, the autonomous driving vehicle is controlled to travel along the initial route to be traveled.

3. A control device for an autonomous driving vehicle, characterized in that: The device comprises: An acquisition module, configured to acquire road surface environment information of a first road surface area, wherein the first road surface area is on an initial to-be-traveled route of the autonomous driving vehicle; a prediction module, configured to input the road surface environment information into a trained prediction model and obtain a predicted friction coefficient of the first road surface area output by the trained prediction model, wherein the trained prediction model is generated based on a sample training set, wherein the sample training set includes historical road surface environment information and a measured friction coefficient corresponding to the historical road surface environment information; A control module is configured to calculate a correction coefficient for the friction coefficient based on a predicted friction coefficient and a measured friction coefficient of a second road surface area, wherein the second road surface area is a road surface area on which the autonomous vehicle has traveled or is currently traveling; use the correction coefficient to correct the predicted friction coefficient of the first road surface area to obtain a corrected predicted friction coefficient; determine whether the corrected predicted friction coefficient is within a preset friction coefficient range; if not, adjust the initial route to be traveled to the first route to be traveled based on the current position information of the autonomous vehicle and the position information of the first road surface area, and control the autonomous vehicle to travel along the first route to be traveled.

4. A computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 2 when executed by a processor.

5. An electronic device, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method according to any one of claims 1 to 2.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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