Parameter determination method, device, electronic device, storage medium and product

Abstract

The present disclosure provides a parameter determination method, device, electronic device, storage medium and product, which relate to the field of autonomous driving technology, and in particular to the field of autonomous driving parameter determination technology. The specific implementation scheme is: in response to receiving the driving route, autonomous driving system version and vehicle model sent by the autonomous driving vehicle, based on a predetermined management relationship, determining the autonomous driving control parameters corresponding to the driving route, the autonomous driving system version and the vehicle model; and sending the autonomous driving control parameters to the autonomous driving vehicle. Through the present disclosure, the autonomous driving control parameters of autonomous driving vehicles under different models, different operating scenarios and different autonomous driving system versions can be determined, which effectively improves the adaptability and robustness of the autonomous driving system used by autonomous vehicles.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, in particular to the field of parameter determination technology, and specifically to a parameter determination method, device, electronic device, storage medium, and product. Background Art

[0002] With the development of autonomous vehicles, they can now drive autonomously in specific scenarios and along specific routes. Due to the differences in autonomous vehicle models and the iterations of the autonomous driving (Altium Designer, AD) system, each autonomous vehicle has different vehicle parameters, control parameters, and autonomous driving system versions.

[0003] In the process of maintaining vehicle parameters and control parameters, it is necessary to perform the maintenance in the autonomous driving system of a single autonomous vehicle, which results in high maintenance costs. Summary of the Invention

[0004] The present disclosure provides a parameter determination method, device, electronic device, storage medium and product.

[0005] According to a first aspect of the present disclosure, a parameter determination method is provided, the method comprising:

[0006] In response to receiving a driving route, an autonomous driving system version, and a vehicle model sent by an autonomous driving vehicle, the autonomous driving control parameters corresponding to the driving route, the autonomous driving system version, and the vehicle model are determined based on a predetermined management relationship; the management relationship is the management relationship between the driving route, the autonomous driving system version, the vehicle model, and the autonomous driving control parameters; the autonomous driving control parameters are sent to the autonomous driving vehicle, and the autonomous driving control parameters are used to control the driving of the autonomous driving vehicle.

[0007] According to a second aspect of the present disclosure, a parameter determination device is provided, the device comprising:

[0008] A determination module is used to determine the autonomous driving control parameters corresponding to the driving route, the autonomous driving system version, and the vehicle model in response to receiving the driving route, the autonomous driving system version, and the vehicle model sent by the autonomous driving vehicle based on a predetermined management relationship; the management relationship is the management relationship between the driving route, the autonomous driving system version, the vehicle model, and the autonomous driving control parameters; a sending module is used to send the autonomous driving control parameters to the autonomous driving vehicle, and the autonomous driving control parameters are used to control the driving of the autonomous driving vehicle.

[0009] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0010] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect.

[0011] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to the first aspect.

[0012] According to a fifth aspect of the present disclosure, a computer product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to the first aspect.

[0013] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0015] Figure 1 A flow chart of a parameter determination method provided by an embodiment of the present disclosure is shown;

[0016] Figure 2 A parameter diagram of a parameter management platform provided by an embodiment of the present disclosure is shown;

[0017] Figure 3 A schematic diagram of the structure of a parameter management platform provided by an embodiment of the present disclosure is shown;

[0018] Figure 4 The design framework of the parameter management platform provided by the embodiment of the present disclosure is shown;

[0019] Figure 5 A schematic diagram illustrating the iterative evolution of a parameter management platform provided by an embodiment of the present disclosure is shown;

[0020] Figure 6 A schematic structural diagram of a parameter determination device provided by an embodiment of the present disclosure is shown;

[0021] Figure 7 A schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0022] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] With the development of autonomous vehicles, they can now drive autonomously in specific scenarios and along specific routes. Due to the differences in autonomous vehicle models and the iterations of autonomous driving system versions, each autonomous vehicle has different vehicle parameters, control parameters, and autonomous driving system versions.

[0024] Due to the different versions of the autonomous driving system, the autonomous driving system cannot have the ability to automatically switch and quickly deploy and operate in all scenarios, all vehicle models, and all versions.

[0025] Currently, parameter management, configuration, and modification for autonomous vehicles are typically performed through icode. This maintenance is performed at the vehicle level, using the vehicle license plate to query relevant parameters. As mass-produced vehicles roll off the assembly line, the number of vehicles will continue to increase, and maintaining vehicle parameters by license plate number will increase the cost of maintaining parameters.

[0026] The parameters of autonomous vehicles are divided into equipment parameters and autonomous driving control parameters. Equipment parameters refer to the calibration parameters and model types of the autonomous vehicle hardware equipment. The equipment parameter files of each autonomous vehicle are different, and the calibration parameters and model types of autonomous vehicles usually require single-vehicle maintenance. Autonomous driving control parameters refer to the configuration parameters related to the vehicle's autonomous driving system, which are closely related to the physical experience of the entire AD system. Usually, for AD systems with the same autonomous driving system version, the autonomous driving control parameter files are usually the same. If the autonomous driving control parameter files are maintained according to the vehicle number, the parameter maintenance cost will increase significantly as vehicles are continuously mass-produced and the AD version is iteratively upgraded.

[0027] For example, maintaining the parameters of autonomous vehicles through icode only allows for one-to-one parameter maintenance. When the AD system versions of the vehicles are the same and the corresponding AD parameters of the same AD version need to be modified, the modification must be performed one-to-one on the vehicle, and the modification content must be completely consistent. This leads to the following technical problems:

[0028] (1) Maintaining multiple versions of the autonomous driving system (software) under multiple R&D lines. There are many R&D lines and versions, and the maintenance cost is high.

[0029] (2) A single version of autonomous driving software cannot support operation in multiple scenarios. Given its limited capabilities, its application scenarios are limited.

[0030] (3) A single version of autonomous driving software cannot support the adaptation of multiple vehicle models or has limited support, and cannot meet the needs of automated upgrades of autonomous driving software.

[0031] Based on this, the present disclosure provides a parameter determination method and device, which performs single-vehicle maintenance on the calibration parameters and vehicle models of autonomous driving vehicles, and manages autonomous driving control parameters according to different AD-generated autonomous driving system versions. When determining parameters for an autonomous driving vehicle, the autonomous driving control parameters are obtained by outputting the autonomous driving system version, vehicle model, and driving route, and the parameter determination scheme is optimized. This provides a solution to the normal operation of autonomous driving vehicles under different vehicle models, different operating scenarios, and different autonomous driving system versions, effectively improving the adaptability and robustness of the autonomous driving system used in autonomous vehicles, and enhancing the ability of unmanned vehicles to be quickly deployed and operated.

[0032] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0033] Figure 1 A flow chart of a parameter determination method provided by an embodiment of the present disclosure is shown. Figure 1 As shown in , the method may include:

[0034] In step S110, in response to receiving the driving route, autonomous driving system version, and vehicle model sent by the autonomous driving vehicle, the autonomous driving control parameters corresponding to the driving route, autonomous driving system version, and vehicle model are determined based on a predetermined management relationship.

[0035] Among them, the management relationship is the management relationship between the driving route, the autonomous driving system version, the vehicle model and the autonomous driving control parameters.

[0036] In an embodiment of the present disclosure, a parameter management platform may be established, which may be communicatively connected with the autonomous driving vehicle for transmitting parameter information.

[0037] In the disclosed embodiments, autonomous vehicles are configured with different parameters depending on the region and route. Furthermore, if the route of the same autonomous vehicle changes, its autonomous driving control parameters will also change accordingly. In this case, the relationship between the route, autonomous driving system version, vehicle model, and autonomous driving control parameters can be determined to determine the autonomous driving control parameters for different situations.

[0038] In the present disclosure, a driving route of an autonomous vehicle can be determined based on the region in which the autonomous vehicle is located. The driving route can be modified based on actual conditions. The autonomous vehicle can send its driving route to a parameter management platform.

[0039] Autonomous vehicles are equipped with an autonomous driving system, which is used to control the vehicle's driving, etc. Each autonomous driving system may have a different version. Therefore, it is necessary to determine the version of the autonomous driving system used by each autonomous vehicle.

[0040] In the embodiment of the present disclosure, the autonomous driving vehicles have respective vehicle models, for example, the vehicle model is RB_6_5, the vehicle model is RB_5_9, and so on.

[0041] The disclosure states that autonomous vehicles can send their driving routes to a parameter management platform. The parameter management platform can predetermine the management relationships between driving routes, autonomous driving system versions, vehicle models, and autonomous driving control parameters. This management relationship allows for unified management of the autonomous driving control parameters of each autonomous vehicle. Furthermore, for each autonomous vehicle, the autonomous driving control parameters can be determined based on the driving route, autonomous driving system version, vehicle model, and management relationship.

[0042] In step S120, the autonomous driving control parameters are sent to the autonomous driving vehicle.

[0043] In the embodiment of the present disclosure, after the parameter management platform determines the autonomous driving parameters of the autonomous driving vehicle, it can send its corresponding autonomous driving parameters to the autonomous driving vehicle.

[0044] Among them, the autonomous driving control parameters are used to control the autonomous driving vehicle so that the autonomous driving vehicle can perform autonomous driving based on a determined driving route.

[0045] In the disclosed embodiments, each autonomous vehicle also has corresponding calibration parameters. The parameter management platform can manage the calibration parameters and autonomous driving parameters. Of course, the parameter management platform can also manage other parameters of the autonomous vehicle, which are not specifically limited here.

[0046] In the present disclosure, calibration parameters and autonomous driving control parameters can be configured as operating parameters of autonomous driving vehicles through the parameter management platform, thereby quickly deploying the operating capabilities of autonomous driving vehicles.

[0047] The parameter determination method provided in the disclosed embodiments determines the corresponding autonomous driving control parameters based on the autonomous driving vehicle's route, autonomous driving system version, and vehicle model. This enables multi-dimensional parameter management for multiple autonomous driving system versions, multiple application scenario routes, and multiple vehicle models. This method addresses the operational challenges of autonomous vehicles across different vehicle models, operating scenarios, and autonomous driving system versions, effectively improving the adaptability and robustness of the autonomous driving software and enhancing the ability of autonomous vehicles to be rapidly deployed and operated.

[0048] Furthermore, when managing the parameters of autonomous driving vehicles, the entire parameter maintenance is achieved through two modules: vehicle calibration parameters and autonomous driving control parameters. At the same time, it meets the maintenance and iterative upgrades of autonomous driving control parameters in the daily research and development process, reducing the probability of the vehicle being unable to perform autonomous driving closed-loop due to changes in autonomous driving control parameters and human operational errors.

[0049] In the disclosed embodiment, the management relationships include a first management relationship between the autonomous driving system version and the vehicle model, a second management relationship between the vehicle model and the driving route, and a third management relationship between the driving route and the autonomous driving control parameters. Each first management relationship includes at least one second management relationship, and each second management relationship includes at least one of the third management relationships.

[0050] In the present disclosure, the first management relationship also includes a first sub-management relationship between the autonomous driving system version and different states of the autonomous driving vehicle, and a second sub-management relationship between different states of the autonomous driving vehicle and the vehicle model. Each first sub-management relationship includes at least one second sub-management relationship. The state of the autonomous driving vehicle can be an operational state or a testing state.

[0051] It should be noted that the process of determining the autonomous driving control parameters can be understood as an inheritance relationship, progressively determining the autonomous driving control system corresponding to the autonomous driving version, vehicle model, and driving route.

[0052] For example, each autonomous driving system version A includes multiple autonomous driving vehicles in operational and test states. Multiple autonomous driving vehicles in operational or test states also include multiple autonomous driving vehicles of different models. For example, the vehicle model is RB_6_5 and the vehicle model is RB_5_9. Multiple autonomous driving vehicles with the vehicle model RB_6_5 can travel different routes, each with its own corresponding autonomous driving control parameters.

[0053] It should be noted that the present disclosure may also first determine the model of the autonomous vehicle, and then determine the autonomous driving system version, thereby determining the corresponding autonomous driving control parameters based on the driving route. This is not specifically limited here. The present disclosure manages the corresponding autonomous driving control parameters through a three-dimensional management method based on the vehicle model, autonomous driving system version, and driving route.

[0054] For example, Figure 2 A parameter diagram of the parameter management platform provided by the embodiment of the present disclosure is shown in FIG. Figure 2 As shown in , it is divided into two parts: vehicle parameters of the autonomous driving vehicle and AD autonomous driving control parameters. The autonomous driving control parameters can also be called AD parameters, which are the software module parameters of the autonomous driving system software.

[0055] The vehicle parameters include a vehicle parameter package, which includes calibration parameters corresponding to the license plate number of each autonomous driving vehicle and the model of the vehicle body.

[0056] AD parameters include multiple autonomous driving system versions (hereinafter referred to as versions), such as version 1, version 2, version 3, version 4, and so on. They also include multiple vehicle models, multiple routes, and multiple autonomous vehicle states. Autonomous vehicle states can be operational or testing, and each state can include multiple autonomous vehicles.

[0057] Furthermore, in this disclosure, the corresponding autonomous driving control parameters for autonomous vehicles in different situations can be determined based on the relationship between the driving route, autonomous driving system version, vehicle model, and autonomous driving control parameters. This achieves three-dimensional coverage of the autonomous driving vehicle's driving route, autonomous driving system version, and vehicle model, and determines the corresponding autonomous driving control parameters for the autonomous vehicle in this three-dimensional scenario, accelerating commercialization and delivery.

[0058] Furthermore, in a pre-established parameter management platform, based on the association relationship, the autonomous driving control parameters corresponding to the driving route, autonomous driving system version and vehicle model are obtained.

[0059] Figure 3 The structure diagram of the parameter management platform provided by the embodiment of the present disclosure is shown as follows: Figure 3 As shown in , parameter configuration management is the parameter management platform in this disclosure, wherein, Figure 3 The following description will be made using the vehicle status of an operating vehicle as an example.

[0060] Figure 3 As shown, the parameter management platform includes the calibration parameters of the autonomous driving vehicle and the autonomous driving control parameters of the autonomous driving vehicle. Among them, the calibration parameters can also be called the external parameters of the autonomous driving vehicle, and the autonomous driving control parameters can also be called AD parameters.

[0061] The AD parameters also include multiple versions of the autonomous driving system, such as version 2B, version 4B, version 10B, and the fusion version. Under each different version of the autonomous driving system, the status of the autonomous driving vehicle is also included. Taking the version 2B of the autonomous driving system as an example, this version also includes autonomous driving vehicles in operation and autonomous driving vehicles in R&D status. Autonomous driving vehicles in different states can also include multiple autonomous driving vehicle models, such as RB_6_5, RB_5_9, etc. Under different models, multiple driving routes are included, such as Route A...Route Z. Different routes correspond to different vehicle numbers and corresponding AD parameters, that is, corresponding autonomous driving control parameters.

[0062] In the embodiment of the present disclosure, Figure 3 As shown, a correspondence between the autonomous driving control parameters and the vehicle number information can also be established. That is, the third management relationship also includes a third sub-management relationship between the route and the vehicle number information, and a fourth sub-management relationship between the vehicle number information and the autonomous driving control parameters.

[0063] The present disclosure realizes the switching and rolling back of different versions of autonomous driving systems and different driving routes through the three-dimensional management of the versions of autonomous driving systems, the models of autonomous driving vehicles and the driving routes of autonomous driving vehicles, thereby greatly improving the efficiency of vehicle deployment and commercial delivery.

[0064] In some embodiments of the present disclosure, in response to detecting a switching operation of a driving route, the autonomous driving control parameters are reacquired based on the switched driving route, the autonomous driving system version, and the vehicle model.

[0065] In one embodiment, in response to receiving a switching indication of the driving route of the autonomous driving vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has not changed, the autonomous driving control parameters of the autonomous driving vehicle are updated based on a third management relationship.

[0066] In other words, if the autonomous driving system version of an autonomous driving vehicle has not changed, but a different driving route has been switched, that is, when the same autonomous driving vehicle needs to switch between different driving routes, the vehicle's autonomous driving system version and vehicle model are fixed at this time, which can meet the corresponding switching of the vehicle's autonomous driving control parameters at the route level.

[0067] In another embodiment, in response to receiving a switching indication of the driving route of the autonomous driving vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has changed, the first management relationship is re-determined, and the autonomous driving control parameters of the autonomous driving vehicle are updated based on the re-determined first management relationship.

[0068] In other words, when the autonomous driving system version and driving route are different, there will be fixed autonomous driving control parameters corresponding to them. The vehicle route can be switched on the parameter management platform. After the switch is completed, the parameter management platform will have fixed autonomous driving control parameters corresponding to it. In other words, the second management relationship is re-determined based on the first management relationship, and then the third management relationship is determined, and finally the corresponding autonomous driving control parameters are obtained.

[0069] Among them, the updated automatic driving control parameters correspond to the switched driving route.

[0070] Further, Figure 4 The design framework of the parameter management platform provided by the embodiment of the present disclosure is shown as follows: Figure 4 As shown in , after the calibration parameters and autonomous driving control parameters of the autonomous driving vehicle are determined, they can be automatically pushed to the OTA platform, and then pushed to the autonomous driving system of the autonomous driving vehicle through the operation of upgrading the OTA parameter package, so that the autonomous driving system can control the autonomous driving vehicle according to the determined calibration parameters and autonomous driving control parameters to realize autonomous driving operation.

[0071] In the present disclosure, the pre-established parameter management platform can also be iteratively evolved according to actual conditions. Figure 5 A schematic diagram of the iterative evolution of the parameter management platform provided by the embodiment of the present disclosure is shown as follows: Figure 5 As shown in the , the parameter management platform's V1.0 version supports bus output parameter adaptation, completing the launch of single-vehicle parameter functionality. The parameter management platform's V2.0 version supports automatic OTA push for multiple models, versions, and routes, as well as rights management and parameter inheritance. The parameter management platform's V2.5 version includes output version management, output parameter adaptation, and integration with the Maas platform.

[0072] In the embodiment of the present disclosure, the management relationship established by the parameter management platform also includes a fourth management relationship; wherein the fourth management relationship is the management relationship between the vehicle license plate information of the autonomous driving vehicle and the calibration parameters of the autonomous driving vehicle.

[0073] That is, in a pre-established parameter management platform, the calibration parameters of the autonomous driving vehicle can be determined by determining the vehicle license plate information.

[0074] In the present disclosure, an association can also be established between the vehicle license plate information and the vehicle model, so that the vehicle model of the autonomous driving vehicle can be determined based on the vehicle license plate information of the autonomous driving vehicle.

[0075] The calibration parameter determination method provided by the present disclosure can meet the requirement of a single calibration parameter for a vehicle.

[0076] Based on Figure 1 The same principle as shown in the method, Figure 6 A schematic diagram of the structure of a parameter determination device provided by an embodiment of the present disclosure is shown. Figure 6 As shown, the parameter determination device 600 may include:

[0077] A determination module 601 is used to determine the autonomous driving control parameters corresponding to the driving route, the autonomous driving system version, and the vehicle model in response to receiving the driving route, the autonomous driving system version, and the vehicle model sent by the autonomous driving vehicle based on a predetermined management relationship; the management relationship is the management relationship between the driving route, the autonomous driving system version, the vehicle model, and the autonomous driving control parameters; a sending module 602 is used to send the autonomous driving control parameters to the autonomous driving vehicle, and the autonomous driving control parameters are used to control the driving of the autonomous driving vehicle.

[0078] In an embodiment of the present disclosure, the management relationship includes a first management relationship between the autonomous driving system version and the vehicle model, a second management relationship between the vehicle model and the driving route, and a third management relationship between the driving route and the autonomous driving control parameter;

[0079] Each of the first management relationships includes at least one of the second management relationships, and each of the second management relationships includes at least one of the third management relationships.

[0080] In the embodiment of the present disclosure, the first management relationship further includes a first sub-management relationship between the autonomous driving system version and different states of the autonomous driving vehicle, and a second sub-management relationship between different states of the autonomous driving vehicle and the vehicle model.

[0081] Each of the first sub-management relationships includes at least one of the second sub-management relationships.

[0082] In the embodiment of the present disclosure, the determining module 601 is further configured to:

[0083] In response to receiving a switching instruction for the driving route of the autonomous driving vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has not changed, updating the autonomous driving control parameters of the autonomous driving vehicle based on the third management relationship;

[0084] or

[0085] In response to receiving a switching instruction for the driving route of the autonomous vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has changed, redetermining a first management relationship, and updating the autonomous driving control parameters of the autonomous driving vehicle based on the redetermined first management relationship;

[0086] Among them, the updated automatic driving control parameters correspond to the driving route after switching.

[0087] In the embodiment of the present disclosure, the management relationship further includes a fourth management relationship;

[0088] The fourth management relationship is the management relationship between the vehicle license plate information of the autonomous driving vehicle and the calibration parameters of the autonomous driving vehicle.

[0089] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0090] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0091] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0092] like Figure 7As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0093] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0094] The computing unit 701 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the parameter determination method. For example, in some embodiments, the parameter determination method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the parameter determination method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the parameter determination method by any other appropriate means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0100] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0101] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0102] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A parameter determination method, the method comprising: In response to receiving a driving route, an autonomous driving system version, and a vehicle model sent by an autonomous driving vehicle, determining autonomous driving control parameters corresponding to the driving route, the autonomous driving system version, and the vehicle model based on predetermined management relationships; the management relationships include a first management relationship between the autonomous driving system version and the vehicle model, a second management relationship between the vehicle model and the driving route, and a third management relationship between the driving route and the autonomous driving control parameters; wherein each of the first management relationships includes at least one of the second management relationships, and each of the second management relationships includes at least one of the third management relationships; The autonomous driving control parameters are sent to the autonomous driving vehicle, where the autonomous driving control parameters are used to control the driving of the autonomous driving vehicle.

2. The method according to claim 1, wherein The first management relationship also includes a first sub-management relationship between the autonomous driving system version and different states of the autonomous driving vehicle, and a second sub-management relationship between different states of the autonomous driving vehicle and the vehicle model. Each of the first sub-management relationships includes at least one of the second sub-management relationships.

3. The method according to claim 1, wherein The method further comprises: In response to receiving a switching instruction for the driving route of the autonomous driving vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has not changed, updating the autonomous driving control parameters of the autonomous driving vehicle based on the third management relationship; or In response to receiving a switching instruction for the driving route of the autonomous vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has changed, redetermining a first management relationship, and updating the autonomous driving control parameters of the autonomous driving vehicle based on the redetermined first management relationship; Among them, the updated automatic driving control parameters correspond to the driving route after switching.

4. The method according to claim 1, wherein The management relationship also includes a fourth management relationship; The fourth management relationship is the management relationship between the vehicle license plate information of the autonomous driving vehicle and the calibration parameters of the autonomous driving vehicle.

5. A parameter determination device, comprising: a determination module configured to, in response to receiving a driving route, an autonomous driving system version, and a vehicle model sent by the autonomous driving vehicle, determine, based on predetermined management relationships, autonomous driving control parameters corresponding to the driving route, the autonomous driving system version, and the vehicle model; the management relationships comprising a first management relationship between the autonomous driving system version and the vehicle model, a second management relationship between the vehicle model and the driving route, and a third management relationship between the driving route and the autonomous driving control parameters; wherein each of the first management relationships includes at least one of the second management relationships, and each of the second management relationships includes at least one of the third management relationships; A sending module is used to send the autonomous driving control parameters to the autonomous driving vehicle, and the autonomous driving control parameters are used to control the driving of the autonomous driving vehicle.

6. The device according to claim 5, wherein The first management relationship also includes a first sub-management relationship between the autonomous driving system version and different states of the autonomous driving vehicle, and a second sub-management relationship between different states of the autonomous driving vehicle and the vehicle model. Each of the first sub-management relationships includes at least one of the second sub-management relationships.

7. The device according to claim 5, wherein The determining module is further configured to: In response to receiving a switching instruction for the driving route of the autonomous driving vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has not changed, updating the autonomous driving control parameters of the autonomous driving vehicle based on the third management relationship; or In response to receiving a switching instruction for the driving route of the autonomous vehicle and determining that the autonomous driving system version of the autonomous driving vehicle has changed, redetermining a first management relationship, and updating the autonomous driving control parameters of the autonomous driving vehicle based on the redetermined first management relationship; Among them, the updated automatic driving control parameters correspond to the driving route after switching.

8. The device according to claim 5, wherein The management relationship also includes a fourth management relationship; The fourth management relationship is the management relationship between the vehicle license plate information of the autonomous driving vehicle and the calibration parameters of the autonomous driving vehicle.

9. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

11. A computer product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 4.

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