Vehicle control method, device, nonvolatile storage medium and processor

Abstract

The application discloses a vehicle control method and device, a nonvolatile storage medium and a processor. The method comprises the following steps: acquiring an environment image of an area where a vehicle is located and an external environment type; if a target object is identified from the environment image, acquiring distance information of the target object from the vehicle; determining a driving strategy model matched with the external environment type of the vehicle according to the external environment type, wherein the driving strategy model is obtained by training a neural network model based on sample driving data of the vehicle; inputting the environment image and the distance information into the driving strategy model to obtain a driving route by prediction; and controlling the vehicle to drive according to the driving route. The application solves the technical problem that the vehicle is difficult to reasonably plan a route in various road conditions.

Description

Technical Field

[0001] This invention relates to the field of vehicle driving, and more specifically, to a vehicle control method, apparatus, non-volatile storage medium, and processor. Background Technology

[0002] In the field of vehicle driving, driver assistance systems (ADAS) and autonomous driving systems have broad application prospects. Both ADAS and autonomous driving systems require vehicle control, which presupposes accurate identification and response to road conditions, and the planning of the vehicle's driving route based on these conditions. However, current technologies for identifying road conditions and planning routes are typically only applicable to specific scenarios, such as urban road traffic, and cannot handle other complex road conditions in the real world.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a vehicle control method, device, non-volatile storage medium, and processor to at least solve the technical problem of vehicles struggling to perform reasonable route planning under various road conditions.

[0005] According to one aspect of the present invention, a vehicle control method is provided, comprising: acquiring an environmental image of the area where the vehicle is located and an external environment type; if a target object is identified from the environmental image, acquiring distance information between the target object and the vehicle; determining a driving strategy model matching the external environment type of the vehicle based on the external environment type, wherein the driving strategy model is obtained by training a neural network model based on sample driving data of the vehicle; inputting the environmental image and the distance information into the driving strategy model to predict a driving route; and controlling the vehicle to drive according to the driving route.

[0006] Optionally, inputting the environmental image and the distance information into a driving strategy model to predict the driving route includes: identifying the type of the target object in the environmental image; inputting the type of the target object and the distance information into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predicting the driving route of the vehicle based on the type and degree of interference.

[0007] Optionally, when multiple target objects are identified from the environmental image, the distance information of each target object from the vehicle is obtained. The process of inputting the environmental image and the distance information into a driving strategy model to predict the driving route includes: identifying the type of each target object in the environmental image; inputting the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the interference type and degree of each target object on the vehicle; obtaining multiple local driving strategies corresponding to the multiple target objects based on the interference type and degree of each target object; and predicting the driving route of the vehicle based on the multiple local driving strategies.

[0008] Optionally, the local driving strategy includes at least one of the following: avoidance, following, steering, decelerating through, accelerating through, stopping, and constant speed driving.

[0009] Optionally, obtaining the distance information between the target object and the vehicle includes: using a lidar to obtain the distance information between the target object and the vehicle.

[0010] Optionally, the driving strategy model is pre-trained using sample driving data from multiple vehicles. Each set of sample driving data includes: an environmental image of the area where the vehicle is located, the type of the external environment, the type of the target object, the distance information of the target object from the vehicle, and the driving route of the vehicle. The environmental image includes different types of objects.

[0011] Optionally, the external environment type includes: seasonal type, weather type, road administrative type, and road physical condition.

[0012] According to another aspect of the present invention, a vehicle control device is also provided, comprising:

[0013] According to another aspect of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium including a stored program, wherein, when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any of the above-described vehicle control methods.

[0014] According to another aspect of the present invention, a processor is further provided, the processor being configured to run a program, wherein the program, when running, executes any of the vehicle control methods described above.

[0015] In this embodiment of the invention, the method of obtaining the vehicle's external environment type and identifying the target object and the distance information between the target object and the vehicle from the environmental image of the area where the vehicle is located is adopted. By inputting the environmental image and distance information into a driving strategy model that matches the external environment type, the predicted driving route is obtained, thereby achieving the purpose of controlling the vehicle to drive according to the driving route. This realizes the technical effect of the vehicle rationally planning the driving route according to the environment of the area where it is located, and solves the technical problem that it is difficult for the vehicle to perform reasonable route planning in various road conditions. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a schematic flowchart of a vehicle control method according to an embodiment of the present invention;

[0018] Figure 2 This is a structural block diagram of a vehicle control device according to an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Example 1

[0022] According to an embodiment of the present invention, a vehicle control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is a flowchart illustrating a vehicle control method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0024] Step S101: Obtain the environmental image of the area where the vehicle is located and the type of the external environment. The environmental image of the area where the vehicle is located may include the road image in front of the vehicle, and may also include other environmental images around the vehicle, such as the environmental image along the route the vehicle is traveling, and may also include the image of the sky above.

[0025] It should be noted that the external environment type refers to the environment type outside the vehicle. The external environment type can be obtained in various ways. For example, it can be actively input or manually selected by the occupants based on their judgment of the external environment; alternatively, it can be obtained from the network by connecting the vehicle's network communication device to the internet; or it can be obtained by using algorithms or image recognition models to identify the environmental image of the area where the vehicle is located. This invention does not limit the method used to obtain the external environment type.

[0026] Step S102: If a target object is identified from the environmental image, obtain the distance information between the target object and the vehicle. The target object may include obstacles in the road, flying objects in the air, terrain, and road edges. For example, the target object may include trees, birds, water bodies, and road guardrails. The distance information between the target object and the vehicle may include its straight-line distance from the vehicle and its orientation relative to the vehicle.

[0027] Step S103: Based on the external environment type, determine a driving strategy model that matches the vehicle's external environment type. This involves training a neural network model using sample driving data from the vehicle to obtain the driving strategy model. It should be noted that the driving strategy model can be a pre-trained neural network model, stored on the vehicle's local computer or on a cloud server, and directly invoked after matching with the vehicle's external environment type. Different driving strategy models can correspond to different external environment types. When training different driving strategy models, the sample driving data is categorized according to the environment type to which the sample driving data belongs, ensuring that the trained driving strategy model corresponding to each environment type performs optimally in that environment type and makes the most reasonable driving route planning decision.

[0028] Step S104: Input the environmental image and distance information into the driving strategy model to predict the driving route.

[0029] Step S105: Control the vehicle to travel according to the driving route.

[0030] Through the above steps, by acquiring the vehicle's external environment type and identifying target objects and their distance from the vehicle from the environmental image of the area where the vehicle is located, the predicted driving route is obtained by inputting the environmental image and distance information into a driving strategy model that matches the external environment type. This achieves the goal of controlling the vehicle to drive according to the driving route, thereby realizing the technical effect of the vehicle rationally planning its driving route according to the environment of the area it is in, and thus solving the technical problem of the difficulty of rational route planning for vehicles in various road conditions.

[0031] As an optional embodiment, the external environment type can include seasonal type, weather type, road administrative type, and road physical condition. When a vehicle travels in different environments, the driving conditions it faces differ due to the characteristics of those environments, and the optimal driving plan will also differ when facing the same unexpected situation. Furthermore, the external environment type can be a combination of the above types. For example, a vehicle may travel on urban roads in sunny summer weather or on rural roads with icy and snowy surfaces in winter. Obviously, the driving strategies for the vehicle in these two external environment types are completely different. This embodiment can determine the driving strategy that best matches the vehicle's current driving environment.

[0032] As an optional implementation, LiDAR can be used to acquire distance information between the target object and the vehicle. LiDAR can efficiently and accurately sense the distance and orientation of the target object, and feed the distance information back to the vehicle to enable the vehicle to quickly adjust its driving strategy in response to the target object.

[0033] As an optional embodiment, the driving strategy model is pre-trained using sample driving data from multiple vehicles. Each set of data in the sample driving data may include an environmental image of the area where the vehicle is located, the type of external environment, the type of target object, the distance information of the target object from the vehicle, and the driving route of the vehicle, wherein the environmental image includes the target object.

[0034] As an optional implementation, a set of sample driving data may include the following: a road image in front of the vehicle, an external environment type of dusty weather, a target object type of a poplar forest, a target object distance information of 500 meters away from the vehicle, an orientation of 15 degrees north of east and 45 degrees north of east, and a driving route of the vehicle that avoids the poplar forest.

[0035] As an optional embodiment, the driving route can be predicted as follows: identify the type of target object in the environmental image; input the type and distance information of the target object into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predict the vehicle's driving route based on the type and degree of interference. This embodiment can realize intelligent driving route planning for different types of target objects and adjust the driving strategy according to different target object types. An example is given below for illustration.

[0036] When a vehicle identifies a puddle as a target area, this information can be input into the driving strategy model to determine the type and degree of interference that puddle terrain area causes to the vehicle's driving. Specifically, when driving on urban roads, where road conditions are more controllable, puddles are usually just water accumulation on the road surface with minimal interference. Therefore, the driving strategy model classifies puddles in urban roads as a slow-down / passage interference, with a low level of interference, meaning the vehicle only needs to slow down slightly to pass. However, when driving in off-road environments, puddles may be deep potholes or dangerous areas like swamps. Therefore, the driving strategy model may classify puddles in off-road environments as an avoidance interference, with a high level of interference, meaning the vehicle needs to avoid the puddle, and the avoidance route is more strictly defined to prevent the vehicle from getting stuck.

[0037] As an optional embodiment, when multiple target objects are identified from an environmental image, the distance information of each target object from the vehicle is obtained. The process of inputting the environmental image and distance information into a driving strategy model to predict the driving route includes: identifying the type of each target object in the environmental image; inputting the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the interference type and degree of each target object on the vehicle; obtaining multiple local driving strategies corresponding to the multiple target objects based on the interference type and degree of each target object; and predicting the vehicle's driving route based on the multiple local driving strategies. When multiple target objects exist in the environmental image, the local driving strategies for individual target objects may conflict with each other. In this case, the driving strategy model in this embodiment can integrate multiple local driving strategies to predict the overall driving route of the vehicle, achieving overall route optimization.

[0038] As an alternative embodiment, local driving strategies may include avoidance, following, steering, decelerating through, accelerating through, stopping, and constant speed driving.

[0039] Example 2

[0040] According to embodiments of the present invention, a vehicle control device for implementing the above-described vehicle control method is also provided. Figure 2 This is a structural block diagram of a vehicle control device according to an embodiment of the present invention, such as... Figure 2 As shown, the vehicle control device 20 includes: an acquisition module 21, a data collection module 22, a determination module 23, a prediction module 24, and a control module 25. The vehicle control device 20 will be described below.

[0041] The acquisition module 21 is used to acquire environmental images of the area where the vehicle is located and the type of external environment;

[0042] The acquisition module 22, connected to the acquisition module 21, is used to acquire the distance information between the target object and the vehicle if the target object is identified from the environmental image.

[0043] The determination module 23, connected to the acquisition module 22, is used to determine a driving strategy model that matches the external environment type of the vehicle based on the external environment type. The driving strategy model is obtained by training a neural network model based on the vehicle's sample driving data.

[0044] Prediction module 24, connected to the determination module 23, is used to input environmental images and distance information into the driving strategy model to predict the driving route;

[0045] The control module 25, connected to the prediction module 24, is used to control the vehicle to travel according to the driving route.

[0046] It should be noted that the above-mentioned acquisition module 21, collection module 22, determination module 23, prediction module 24 and control module 25 correspond to steps S101 to S105 in Embodiment 1. The three modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1.

[0047] Example 3

[0048] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0049] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the vehicle control method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned vehicle control method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0050] The processor can access information and applications stored in memory via a transmission device to perform the following steps: acquiring an environmental image of the area where the vehicle is located and the type of the external environment; if a target object is identified from the environmental image, acquiring the distance information between the target object and the vehicle; determining a driving strategy model that matches the type of the external environment based on the type of the external environment, wherein a neural network model is trained based on sample driving data of the vehicle to obtain the driving strategy model; inputting the environmental image and distance information into the driving strategy model to predict the driving route; and controlling the vehicle to drive according to the driving route.

[0051] Optionally, the processor may also execute program code that performs the following steps: inputting environmental images and distance information into a driving strategy model to predict a driving route, including: identifying the type of target object in the environmental image; inputting the type and distance information of the target object into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predicting the vehicle's driving route based on the type and degree of interference.

[0052] Optionally, the processor may also execute program code with the following steps: when multiple target objects are identified from an environmental image, the distance information between the multiple target objects and the vehicle is obtained respectively. The process of inputting the environmental image and distance information into a driving strategy model to predict the driving route includes: identifying the type of each target object in the environmental image; inputting the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the type and degree of interference of each target object to the vehicle; obtaining multiple local driving strategies corresponding to the multiple target objects based on the type and degree of interference of each target object to the vehicle; and predicting the vehicle's driving route based on the multiple local driving strategies.

[0053] Optionally, the processor may also execute program code that includes at least one of the following local driving strategies: avoidance, following, steering, deceleration through, acceleration through, stopping, and constant speed driving.

[0054] Optionally, the processor may also execute program code that performs the following steps: obtaining distance information between the target object and the vehicle, including: using a lidar to obtain distance information between the target object and the vehicle.

[0055] Optionally, the processor may also execute program code for the following steps: the driving strategy model is pre-trained using sample driving data from multiple vehicles, and each set of data in the sample driving data includes: an environmental image of the area where the vehicle is located, the type of the external environment, the type of the target object, the distance information of the target object from the vehicle, and the driving route of the vehicle, wherein the environmental image includes different types of objects.

[0056] Optionally, the processor may also execute program code that includes the following steps: external environment types include: season type, weather type, road administrative type, and road physical condition.

[0057] The present invention provides an image processing solution. This achieves the objective and solves the technical problems in related technologies.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0059] Example 4

[0060] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the vehicle control method provided in Embodiment 1.

[0061] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0062] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring an environmental image of the area where the vehicle is located and the type of the external environment; if a target object is identified from the environmental image, acquiring the distance information between the target object and the vehicle; determining a driving strategy model that matches the type of the external environment of the vehicle, wherein the driving strategy model is obtained by training a neural network model based on the vehicle's sample driving data; inputting the environmental image and distance information into the driving strategy model to predict the driving route; and controlling the vehicle to drive according to the driving route.

[0063] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: inputting environmental images and distance information into a driving strategy model to predict a driving route, including: identifying the type of target object in the environmental image; inputting the type and distance information of the target object into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predicting the driving route of the vehicle based on the type and degree of interference.

[0064] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: when multiple target objects are identified from an environmental image, the distance information of each target object from the vehicle is obtained, wherein inputting the environmental image and distance information into a driving strategy model to predict the driving route includes: identifying the type of each target object in the environmental image; inputting the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the interference type and interference level of each target object to the vehicle; obtaining multiple local driving strategies corresponding to the multiple target objects based on the interference type and interference level of each target object to the vehicle; and predicting the driving route of the vehicle based on the multiple local driving strategies.

[0065] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the local driving strategy includes at least one of the following: avoidance, following, steering, deceleration through, acceleration through, stopping, and constant speed driving.

[0066] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining distance information between the target object and the vehicle, including: using a lidar to obtain the distance information between the target object and the vehicle.

[0067] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the driving strategy model is pre-trained using sample driving data from multiple sets of vehicles, and each set of data in the multiple sets of sample driving data includes: an environmental image of the area where the vehicle is located, the type of external environment, the type of target object, the distance information of the target object from the vehicle, and the driving route of the vehicle, wherein the environmental image includes different types of objects.

[0068] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the external environment type includes: season type, weather type, road administrative type, and road physical condition.

[0069] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0070] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0072] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0075] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A vehicle control method, characterized in that, include: Acquire environmental images of the area where the vehicle is located and the type of the external environment; If a target object is identified from the environmental image, the distance information between the target object and the vehicle is obtained; Based on the external environment type, a driving strategy model matching the external environment type of the vehicle is determined, wherein the driving strategy model is obtained by training a neural network model based on sample driving data of the vehicle. The environmental image and the distance information are input into the driving strategy model to predict the driving route. Control the vehicle to travel along the stated route; The process of inputting the environmental image and the distance information into a driving strategy model to predict a driving route includes: identifying the type of the target object in the environmental image; inputting the type of the target object and the distance information into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predicting the driving route of the vehicle based on the type and degree of interference. When multiple target objects are identified from the environmental image, the distance information of each target object from the vehicle is obtained. The process of inputting the environmental image and the distance information into a driving strategy model to predict the driving route includes: identifying the type of each target object in the environmental image; inputting the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the interference type and degree of each target object on the vehicle; obtaining multiple local driving strategies corresponding to the multiple target objects based on the interference type and degree of each target object; and predicting the driving route of the vehicle based on the multiple local driving strategies.

2. The method according to claim 1, characterized in that, The local driving strategy includes at least one of the following: Avoid, follow, turn, slow down when passing through, speed up when passing through, stop, and drive at a constant speed.

3. The method according to claim 1, characterized in that, Obtaining the distance information between the target object and the vehicle includes: The distance information between the target object and the vehicle is obtained using LiDAR.

4. The method according to any one of claims 1 to 3, characterized in that, The driving strategy model is pre-trained using sample driving data from multiple vehicles. Each set of sample driving data includes: an environmental image of the area where the vehicle is located, the type of the external environment, the type of the target object, the distance information of the target object from the vehicle, and the driving route of the vehicle. The environmental image includes different types of objects.

5. The method according to claim 4, characterized in that, The external environment types include: Season type, weather type, road administrative type, road physical condition.

6. A vehicle control device, characterized in that, include: The acquisition module is used to acquire environmental images of the area where the vehicle is located and the type of the external environment. The acquisition module is used to obtain distance information between the target object and the vehicle if a target object is identified from the environmental image; The determination module is used to determine a driving strategy model that matches the external environment type of the vehicle based on the external environment type, wherein the driving strategy model is obtained by training a neural network model based on sample driving data of the vehicle. The prediction module is used to input the environmental image and the distance information into the driving strategy model to predict the driving route; The control module is used to control the vehicle to travel according to the driving route; The prediction module is further configured to identify the type of the target object in the environmental image; input the type of the target object and the distance information into the driving strategy model to obtain the type and degree of interference of the target object to the vehicle; and predict the driving route of the vehicle based on the type and degree of interference. When multiple target objects are identified from the environmental image, the distance information of each target object from the vehicle is obtained. The prediction module is further configured to identify the type of each target object in the environmental image; input the type of each target object and the distance information between each target object and the vehicle into the driving strategy model to obtain the interference type and degree of each target object on the vehicle; based on the interference type and degree of each target object on the vehicle, obtain multiple local driving strategies corresponding to the multiple target objects; and predict the driving route of the vehicle based on the multiple local driving strategies.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, the device containing the non-volatile storage medium is controlled to perform the vehicle control method according to any one of claims 1 to 5.

8. A processor, characterized in that, The processor is used to run a program, wherein the program executes the vehicle control method according to any one of claims 1 to 5 when it runs.

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