Control method and system for intelligent driving of automobile based on edge computing

Abstract

The present invention provides a control method and system for intelligent driving of automobiles based on edge computing, wherein multiple intelligent driving strategies of automobiles to be configured are sorted according to the driving stages of a preset driving route, and the route-related segments of each intelligent driving strategy of automobiles and the previous intelligent driving strategy of the intelligent driving strategy of automobiles are loaded into the route-related segment cluster, thereby dividing the sorted multiple intelligent driving strategies of automobiles into multiple sub-strategy clusters, and determining the route-related segments of each sub-strategy cluster, storing the route-related segments of each sub-strategy cluster and the non-route-related segments of each intelligent driving strategy of automobiles in the sub-strategy cluster, and recording the route-related segments corresponding to each intelligent driving strategy of automobiles in each sub-strategy cluster and the shared control channels of each intelligent driving strategy of automobiles and the route-related segments, so as to make intelligent driving control decisions based on edge computing for the target automobile. In this way, the reliability of automobile driving decisions can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile intelligent driving control, and in particular to a control method and system for automobile intelligent driving based on edge computing. Background Art

[0002] At present, in the simulation process of automobile intelligent driving, some application suggestions of automobile intelligent driving strategies are usually provided to the relevant simulated automobiles. However, there are many automobile intelligent driving strategies. How to improve the reliability of automobile driving decisions is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0003] In view of this, an object of an embodiment of the present invention is to provide a control method and system for intelligent automobile driving based on edge computing, which can improve the reliability of automobile driving decisions.

[0004] According to one aspect of an embodiment of the present invention, a control method for intelligent driving of a car based on edge computing is provided, which is applied to a server, and the method includes:

[0005] Sorting multiple automobile intelligent driving strategies to be configured according to the driving phases of the preset driving route;

[0006] For an automobile intelligent driving strategy among the sorted plurality of automobile intelligent driving strategies, in response to the automobile intelligent driving strategy and a previous automobile intelligent driving strategy of the automobile intelligent driving strategy having a route-related segment, loading the route-related segment into a route-related segment cluster;

[0007] Dividing the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster;

[0008] For a sub-strategy cluster among the multiple sub-strategy clusters, the route-related segments of the sub-strategy cluster and the non-route-related segments of the intelligent driving strategies of each vehicle in the sub-strategy cluster are stored, and the route-related segments corresponding to the intelligent driving strategies of each vehicle in each sub-strategy cluster and the shared control channels between each intelligent driving strategy of each vehicle and the route-related segments are recorded, so as to make edge computing-based intelligent driving control decisions for the target vehicle according to the route-related segments of the sub-strategy cluster and the non-route-related segments of the intelligent driving strategies of each vehicle in the sub-strategy cluster and the shared control channels.

[0009] In a possible example, the steps of dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster include:

[0010] Determining a numerical order node of route travel difficulty of route-related segments in the route-related segment cluster;

[0011] The multiple automobile intelligent driving strategies are divided into multiple sub-strategy clusters according to the numerical order nodes, wherein the automobile intelligent driving strategy corresponding to the numerical order node is divided into the next sub-strategy cluster; for the multiple sub-strategy clusters, the route-related segment with the greatest driving difficulty corresponding to each automobile intelligent driving strategy in the sub-strategy cluster is determined as the route-related segment of the sub-strategy cluster.

[0012] In a possible example, the method further includes:

[0013] Determining the a priori application times of the plurality of intelligent driving strategies for the vehicles;

[0014] If the a priori application number is less than the target number, the non-route-related segmented parts of the automobile intelligent driving strategies of each sub-strategy cluster are sorted according to the driving phase of the preset driving route to generate the next round of automobile intelligent driving strategy clusters to be configured, and the following multiple rounds of application steps are performed based on the data cluster: for the automobile intelligent driving strategies in the data cluster, in response to the automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy having route-related segments, the route-related segments are loaded into the multiple rounds of route-related segment clusters;

[0015] Dividing the data cluster into a plurality of sub-strategy clusters based on the plurality of rounds of route-related segment clusters, and determining a route-related segment of each sub-strategy cluster;

[0016] Updating the a priori application times of the multiple automobile intelligent driving strategies;

[0017] If the updated a priori application number is greater than or equal to the target number or the current application number reaches a predetermined current application number threshold, then the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of each sub-strategy cluster are stored, and the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments are recorded;

[0018] If the updated prior application number is less than the target number, and the current application number does not reach the predetermined current application number threshold, the non-route-related segmented parts of the automobile intelligent driving strategy of each sub-strategy cluster are sorted according to the driving stage of the preset driving route, and the next round of automobile intelligent driving strategy cluster to be configured is updated, and the above-mentioned multiple rounds of application steps are continued.

[0019] In a possible example, the method further includes:

[0020] For the automobile intelligent driving strategy in the data cluster, in response to the fact that the automobile intelligent driving strategy does not have a route-related segment with the previous automobile intelligent driving strategy of the automobile intelligent driving strategy, the automobile intelligent driving strategy is loaded into the next round of automobile intelligent driving strategy cluster to be configured.

[0021] In a possible example, the step of determining the a priori application times of the multiple intelligent driving strategies for the vehicles includes:

[0022] Data statistics are performed based on the application records in the database corresponding to each of the multiple intelligent driving strategies for automobiles to obtain the a priori application times of the multiple intelligent driving strategies for automobiles.

[0023] According to another aspect of an embodiment of the present invention, a control system for intelligent driving of an automobile based on edge computing is provided, which is applied to a server, and the system includes:

[0024] A sorting module, used to sort multiple automobile intelligent driving strategies to be configured according to the driving phases of a preset driving route;

[0025] A loading module, for loading, for a vehicle intelligent driving strategy among the sorted plurality of vehicle intelligent driving strategies, the route-related segment into a route-related segment cluster in response to the vehicle intelligent driving strategy and a previous vehicle intelligent driving strategy of the vehicle intelligent driving strategy having a route-related segment;

[0026] A determination module, configured to divide the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determine the route-related segment of each sub-strategy cluster;

[0027] A decision-making module is used to store, for a sub-strategy cluster among the multiple sub-strategy clusters, the route-related segments of the sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of the sub-strategy cluster, and record the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments, so as to make an intelligent driving control decision based on edge computing for the target vehicle according to the route-related segments of the sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of the sub-strategy cluster and the shared control channels.

[0028] According to another aspect of an embodiment of the present invention, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned control method for intelligent driving of an automobile based on edge computing can be executed.

[0029] Compared with the prior art, the control method and system of automobile intelligent driving based on edge computing provided by the embodiment of the present invention, by sorting the multiple automobile intelligent driving strategies to be configured according to the driving stage of the preset driving route, loading the route-related segments of each automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy into the route-related segment cluster, thereby dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters, and determining the route-related segments of each sub-strategy cluster, storing the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of the sub-strategy cluster, and recording the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels of each automobile intelligent driving strategy and the route-related segments, so as to make intelligent driving control decisions based on edge computing for the target automobile. In this way, the reliability of automobile driving decisions can be improved.

[0030] In order to make the above-mentioned objects, features and advantages of the embodiments of the present invention more obvious and easy to understand, the embodiments will be described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A schematic diagram showing components of a server provided by an embodiment of the present invention is shown;

[0033] Figure 2 A schematic diagram of a flow chart of a method for controlling intelligent driving of an automobile based on edge computing provided in an embodiment of the present invention is shown;

[0034] Figure 3 A functional module block diagram of a control system for intelligent automobile driving based on edge computing provided in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] In order to enable students in this technical field to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. According to the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] The terms "first", "second", "third", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the automotive intelligent driving strategies used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] Figure 1 An exemplary component diagram of a server 100 is shown. The server 100 may include one or more processors 104, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. Non-limitingly, for example, the storage medium 106 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of the server 100. In one case, when the processor 104 executes an associated instruction stored in any storage medium or a combination of storage media, the server 100 may perform any operation of the associated instruction. The server 100 also includes one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.

[0038] The server 100 also includes input / output 110 (I / O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114). One specific output mechanism may include a presentation device 116 and an associated graphical user interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.

[0039] The communication unit 122 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, etc. governed by any protocol or combination of protocols.

[0040] Figure 2 The flowchart of the control method of the intelligent driving of an automobile based on edge computing provided by an embodiment of the present invention is shown. The control method of the intelligent driving of an automobile based on edge computing can be Figure 1 The server 100 shown in the figure executes the control method of automobile intelligent driving based on edge computing. The detailed steps are introduced as follows.

[0041] Step S110, sorting the multiple automobile intelligent driving strategies to be configured according to the driving phases of the preset driving route;

[0042] Step S120, for an intelligent driving strategy of the sorted plurality of intelligent driving strategies of the vehicles, in response to the existence of a route-related segment between the intelligent driving strategy of the vehicle and a previous intelligent driving strategy of the intelligent driving strategy of the vehicle, loading the route-related segment into a route-related segment cluster;

[0043] Step S130, dividing the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster;

[0044] Step S140, for a sub-strategy cluster in the multiple sub-strategy clusters, store the route-related segments of the sub-strategy cluster and the non-route-related segments of each vehicle intelligent driving strategy of the sub-strategy cluster, and record the route-related segments corresponding to each vehicle intelligent driving strategy in each sub-strategy cluster and the shared control channels between each vehicle intelligent driving strategy and the route-related segments, so as to make edge computing-based intelligent driving control decisions for the target vehicle according to the route-related segments of the sub-strategy cluster and the non-route-related segments of each vehicle intelligent driving strategy of the sub-strategy cluster and the shared control channels.

[0045] Based on the above steps, this embodiment sorts the multiple automobile intelligent driving strategies to be configured according to the driving stages of the preset driving route, loads the route-related segments of each automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy into the route-related segment cluster, thereby dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters, and determining the route-related segments of each sub-strategy cluster, storing the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of the sub-strategy cluster, and recording the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels of each automobile intelligent driving strategy and the route-related segments, so as to make intelligent driving control decisions based on edge computing for the target automobile. In this way, the reliability of automobile driving decisions can be improved.

[0046] In a possible example, for step S130, this embodiment can determine the numerical order node of the route driving difficulty of the route-related segment in the route-related segment cluster, and divide the multiple automobile intelligent driving strategies into multiple sub-strategy clusters according to the numerical order node, wherein the automobile intelligent driving strategy corresponding to the numerical order node is divided into the next sub-strategy cluster. For the multiple sub-strategy clusters, the route-related segment with the greatest route driving difficulty corresponding to each automobile intelligent driving strategy in the sub-strategy cluster is determined as the route-related segment of the sub-strategy cluster.

[0047] In a possible example, during the implementation of the above-mentioned solution, the a priori application times of the multiple automobile intelligent driving strategies can be further determined. If the a priori application times are less than the target times, the non-route-related segmented parts of the automobile intelligent driving strategies of each sub-strategy cluster are sorted according to the driving stages of the preset driving route to generate the next round of automobile intelligent driving strategy clusters to be configured, and the following multiple rounds of application steps are performed based on the data clusters:

[0048] For the automobile intelligent driving strategy in the data cluster, in response to the automobile intelligent driving strategy and the previous automobile intelligent driving strategy having route-related segments, the route-related segments are loaded into the multi-round route-related segment cluster, and the data cluster is divided into multiple sub-strategy clusters based on the multiple-round route-related segment clusters, and the route-related segments of each sub-strategy cluster are determined, and then the a priori application times of the multiple automobile intelligent driving strategies are updated. If the updated a priori application times are greater than or equal to the target times or the current application times reach the predetermined current application times threshold, the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of each sub-strategy cluster are stored, and the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments are recorded.

[0049] If the updated prior application number is less than the target number, and the current application number does not reach the predetermined current application number threshold, the non-route-related segmented parts of the automobile intelligent driving strategy of each sub-strategy cluster are sorted according to the driving stage of the preset driving route, and the next round of automobile intelligent driving strategy cluster to be configured is updated, and the above-mentioned multiple rounds of application steps are continued.

[0050] In a possible example, for the automobile intelligent driving strategy in the data cluster, in response to the fact that the automobile intelligent driving strategy does not have a route-related segment with the previous automobile intelligent driving strategy of the automobile intelligent driving strategy, the automobile intelligent driving strategy is loaded into the next round of automobile intelligent driving strategy cluster to be configured.

[0051] In a possible example, when determining the a priori application times of the multiple intelligent automobile driving strategies, data statistics can be performed based on the application records in the database corresponding to each of the multiple intelligent automobile driving strategies to obtain the a priori application times of the multiple intelligent automobile driving strategies.

[0052] Figure 3 The functional module diagram of the control system 200 for intelligent driving of an automobile based on edge computing provided by an embodiment of the present invention is shown. The functions implemented by the control system 200 for intelligent driving of an automobile based on edge computing can correspond to the steps performed by the above method. The control system 200 for intelligent driving of an automobile based on edge computing can be understood as the above server 100, or the processor of the server 100, or can be understood as a component independent of the above server 100 or the processor that implements the functions of the present invention under the control of the server 100, such as Figure 3 As shown, the functions of each functional module of the control system 200 for intelligent driving of an automobile based on edge computing are respectively explained in detail below.

[0053] A sorting module 210, for sorting a plurality of automobile intelligent driving strategies to be configured according to the driving phases of a preset driving route;

[0054] A loading module 220 is configured to load, for a vehicle intelligent driving strategy among the sorted plurality of vehicle intelligent driving strategies, the route-related segment into a route-related segment cluster in response to the vehicle intelligent driving strategy and a previous vehicle intelligent driving strategy of the vehicle intelligent driving strategy having a route-related segment;

[0055] A determination module 230, configured to divide the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determine the route-related segments of each sub-strategy cluster;

[0056] The decision module 240 is used to store the route-related segments of the sub-strategy cluster and the non-route-related segments of each vehicle intelligent driving strategy of the sub-strategy cluster for each sub-strategy cluster, and record the route-related segments corresponding to each vehicle intelligent driving strategy in each sub-strategy cluster and the shared control channels between each vehicle intelligent driving strategy and the route-related segments, so as to make edge computing-based intelligent driving control decisions for the target vehicle according to the route-related segments of the sub-strategy cluster and the non-route-related segments of each vehicle intelligent driving strategy of the sub-strategy cluster and the shared control channels.

[0057] In a possible example, the method of dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster includes:

[0058] Determining a numerical order node of route travel difficulty of route-related segments in the route-related segment cluster;

[0059] The multiple automobile intelligent driving strategies are divided into multiple sub-strategy clusters according to the numerical order nodes, wherein the automobile intelligent driving strategy corresponding to the numerical order node is divided into the next sub-strategy cluster; for the multiple sub-strategy clusters, the route-related segment with the greatest driving difficulty corresponding to each automobile intelligent driving strategy in the sub-strategy cluster is determined as the route-related segment of the sub-strategy cluster.

[0060] In a possible example, the determination module is further used to determine the a priori application times of the multiple automobile intelligent driving strategies;

[0061] The decision module is further configured to generate the next round of automobile intelligent driving strategy clusters to be configured by sorting the non-route-related segments of the automobile intelligent driving strategies of each sub-strategy cluster according to the driving stages of the preset driving route if the a priori application number is less than the target number, and execute based on the data cluster:

[0062] For the automobile intelligent driving strategy in the data cluster, in response to the automobile intelligent driving strategy and a previous automobile intelligent driving strategy of the automobile intelligent driving strategy having route-related segments, loading the route-related segments into the multi-round route-related segment cluster;

[0063] Dividing the data cluster into a plurality of sub-strategy clusters based on the plurality of rounds of route-related segment clusters, and determining a route-related segment of each sub-strategy cluster;

[0064] Updating the a priori application times of the multiple automobile intelligent driving strategies;

[0065] If the updated a priori application number is greater than or equal to the target number or the current application number reaches a predetermined current application number threshold, then the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of each sub-strategy cluster are stored, and the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments are recorded;

[0066] If the updated prior application number is less than the target number, and the current application number does not reach the predetermined current application number threshold, the non-route-related segmented parts of the automobile intelligent driving strategy of each sub-strategy cluster are sorted according to the driving stage of the preset driving route, and the next round of automobile intelligent driving strategy clusters to be configured are updated, and the above-mentioned multiple rounds of application operations are continued.

[0067] In a possible example, the loading module is also used to load the automobile intelligent driving strategy in the data cluster into the next round of automobile intelligent driving strategy cluster to be configured in response to the fact that the automobile intelligent driving strategy does not have a route-related segment with the previous automobile intelligent driving strategy of the automobile intelligent driving strategy.

[0068] In a possible example, the method of determining the a priori application times of the multiple intelligent driving strategies for the vehicles includes:

[0069] Data statistics are performed based on the application records in the database corresponding to each of the multiple intelligent driving strategies for automobiles to obtain the a priori application times of the multiple intelligent driving strategies for automobiles.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and scope of the equivalent elements of the claims be included in the invention. Any drawings in the claims should not be considered as limiting the claims to which they relate.

Claims

1. A control method for intelligent driving of an automobile based on edge computing, characterized in that: Applied to a server, the method comprises: Sorting multiple automobile intelligent driving strategies to be configured according to the driving phases of the preset driving route; For an automobile intelligent driving strategy among the sorted plurality of automobile intelligent driving strategies, in response to the automobile intelligent driving strategy and a previous automobile intelligent driving strategy of the automobile intelligent driving strategy having a route-related segment, loading the route-related segment into a route-related segment cluster; Dividing the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster; For a sub-strategy cluster in the multiple sub-strategy clusters, the route-related segments of the sub-strategy cluster and the non-route-related segments of the intelligent driving strategies of each automobile in the sub-strategy cluster are stored, and the route-related segments corresponding to the intelligent driving strategies of each automobile in each sub-strategy cluster and the shared control channels between the intelligent driving strategies of each automobile and the route-related segments are recorded, so as to make an intelligent driving control decision based on edge computing for the target automobile according to the route-related segments of the sub-strategy cluster and the non-route-related segments of the intelligent driving strategies of each automobile in the sub-strategy cluster and the shared control channels; The steps of dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster include: Determining a numerical order node of route travel difficulty of route-related segments in the route-related segment cluster; Dividing the plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters according to the numerical order nodes, wherein the automobile intelligent driving strategy corresponding to the numerical order node is divided into the next sub-strategy cluster; For the multiple sub-strategy clusters, the route-related segment with the greatest driving difficulty corresponding to each of the automobile intelligent driving strategies in the sub-strategy cluster is determined as the route-related segment of the sub-strategy cluster.

2. The control method for intelligent driving of an automobile based on edge computing according to claim 1 is characterized in that: The method further comprises: Determining the a priori application times of the plurality of intelligent driving strategies for the vehicles; If the a priori application number is less than the target number, the non-route-related segmented parts of the automobile intelligent driving strategies of each sub-strategy cluster are sorted according to the driving phase of the preset driving route to generate the next round of automobile intelligent driving strategy clusters to be configured, and the following multiple rounds of application steps are performed based on the automobile intelligent driving strategy clusters: for the automobile intelligent driving strategies in the automobile intelligent driving strategy clusters, in response to the automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy having route-related segments, the route-related segments are loaded into the multiple rounds of route-related segment clusters; Dividing the automobile intelligent driving strategy cluster into a plurality of sub-strategy clusters based on the multiple rounds of route-related segment clusters, and determining the route-related segments of each sub-strategy cluster; Updating the a priori application times of the multiple automobile intelligent driving strategies; If the updated a priori application number is greater than or equal to the target number or the current application number reaches a predetermined current application number threshold, then the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of each sub-strategy cluster are stored, and the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments are recorded; If the updated prior application number is less than the target number, and the current application number does not reach the predetermined current application number threshold, the non-route-related segmented parts of the automobile intelligent driving strategy of each sub-strategy cluster are sorted according to the driving stage of the preset driving route, and the next round of automobile intelligent driving strategy cluster to be configured is updated, and the above-mentioned multiple rounds of application steps are continued.

3. The control method for intelligent driving of an automobile based on edge computing according to claim 2 is characterized in that: The method further comprises: For the automobile intelligent driving strategy in the automobile intelligent driving strategy cluster, in response to the fact that the automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy do not have route-related segments, the automobile intelligent driving strategy is loaded into the next round of automobile intelligent driving strategy cluster to be configured.

4. The control method for intelligent driving of an automobile based on edge computing according to claim 2 is characterized in that: The step of determining the a priori application times of the multiple automobile intelligent driving strategies comprises: Data statistics are performed based on the application records in the database corresponding to each of the multiple intelligent driving strategies for automobiles to obtain the a priori application times of the multiple intelligent driving strategies for automobiles.

5. A control system for intelligent driving of automobile based on edge computing, characterized in that: Applied to a server, the system comprises: A sorting module, used to sort multiple automobile intelligent driving strategies to be configured according to the driving phases of a preset driving route; A loading module, for loading, for a vehicle intelligent driving strategy among the sorted plurality of vehicle intelligent driving strategies, the route-related segment into a route-related segment cluster in response to the vehicle intelligent driving strategy and a previous vehicle intelligent driving strategy of the vehicle intelligent driving strategy having a route-related segment; A determination module, configured to divide the sorted plurality of automobile intelligent driving strategies into a plurality of sub-strategy clusters based on the route-related segment clusters, and determine the route-related segment of each sub-strategy cluster; A decision module, for storing, for a sub-strategy cluster in the multiple sub-strategy clusters, the route-related segments of the sub-strategy cluster and the non-route-related segments of each intelligent driving strategy of the sub-strategy cluster, and recording the route-related segments corresponding to each intelligent driving strategy of the vehicle in each sub-strategy cluster and the shared control channels between each intelligent driving strategy of the vehicle and the route-related segments, so as to make an intelligent driving control decision based on edge computing for the target vehicle according to the route-related segments of the sub-strategy cluster and the non-route-related segments of each intelligent driving strategy of the vehicle in the sub-strategy cluster and the shared control channels; The method of dividing the sorted multiple automobile intelligent driving strategies into multiple sub-strategy clusters based on the route-related segment clusters, and determining the route-related segments of each sub-strategy cluster, includes: Determining a numerical order node of route travel difficulty of route-related segments in the route-related segment cluster; The multiple automobile intelligent driving strategies are divided into multiple sub-strategy clusters according to the numerical order nodes, wherein the automobile intelligent driving strategy corresponding to the numerical order node is divided into the next sub-strategy cluster; for the multiple sub-strategy clusters, the route-related segment with the greatest driving difficulty corresponding to each automobile intelligent driving strategy in the sub-strategy cluster is determined as the route-related segment of the sub-strategy cluster.

6. The control system for intelligent driving of an automobile based on edge computing according to claim 5 is characterized in that: The determination module is further used to determine the a priori application times of the multiple automobile intelligent driving strategies; The decision module is further configured to, if the a priori application number is less than the target number, sort the non-route-related segmented parts of the automobile intelligent driving strategies of each sub-strategy cluster according to the driving phase of the preset driving route to generate the next round of automobile intelligent driving strategy clusters to be configured, and perform the following multiple rounds of application steps based on the automobile intelligent driving strategy clusters: For the automobile intelligent driving strategy in the automobile intelligent driving strategy cluster, in response to the automobile intelligent driving strategy and the automobile intelligent driving strategy before the automobile intelligent driving strategy having route-related segments, loading the route-related segments into the multi-round route-related segment cluster; Dividing the automobile intelligent driving strategy cluster into a plurality of sub-strategy clusters based on the multiple rounds of route-related segment clusters, and determining the route-related segments of each sub-strategy cluster; Updating the a priori application times of the multiple automobile intelligent driving strategies; If the updated a priori application number is greater than or equal to the target number or the current application number reaches a predetermined current application number threshold, then the route-related segments of each sub-strategy cluster and the non-route-related segments of each automobile intelligent driving strategy of each sub-strategy cluster are stored, and the route-related segments corresponding to each automobile intelligent driving strategy in each sub-strategy cluster and the shared control channels between each automobile intelligent driving strategy and the route-related segments are recorded; If the updated prior application number is less than the target number, and the current application number does not reach the predetermined current application number threshold, the non-route-related segmented parts of the automobile intelligent driving strategy of each sub-strategy cluster are sorted according to the driving stage of the preset driving route, and the next round of automobile intelligent driving strategy cluster to be configured is updated, and the above-mentioned multiple rounds of application steps are continued.

7. The control system for intelligent driving of an automobile based on edge computing according to claim 6 is characterized in that: The loading module is also used to load the automobile intelligent driving strategy in the automobile intelligent driving strategy cluster into the next round of automobile intelligent driving strategy cluster to be configured in response to the fact that the automobile intelligent driving strategy and the previous automobile intelligent driving strategy of the automobile intelligent driving strategy do not have route-related segments.

8. The control system for intelligent driving of an automobile based on edge computing according to claim 6 is characterized in that: The method of determining the a priori application times of the multiple automobile intelligent driving strategies includes: Data statistics are performed based on the application records in the database corresponding to each of the multiple intelligent driving strategies for automobiles to obtain the a priori application times of the multiple intelligent driving strategies for automobiles.

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