Vehicle control method, electronic equipment, vehicle, chip and medium

Abstract

The invention relates to a vehicle control method, electronic equipment, a vehicle, a chip and a medium. The method comprises the steps that first environment information, determined based on user input, of a scene where a vehicle is located is obtained, and second environment information, determined based on data sensed by a vehicle sensing assembly, of the scene where the vehicle is located is obtained; and based on the first environment information and the second environment information, generating a driving control instruction of the vehicle. Understanding of the scene where the vehicle is located is enriched based on user input, and then the ability to deal with the dynamic driving scene is improved.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and more specifically, to a vehicle control method, electronic device, chip, vehicle, and computer-readable storage medium. Background Technology

[0002] Intelligent driving systems primarily rely on environmental information perceived by onboard sensors such as cameras and radar to make driving decisions and control. Given the limitations of onboard sensors in perceiving the environment, and the fact that intelligent driving systems cannot combine driving experience and intuition with those of human drivers to respond to current driving scenarios, they often fail to quickly generate effective driving control commands in complex and dynamic driving situations. Summary of the Invention

[0003] In view of this, the present disclosure proposes a new technical solution for vehicle control to improve the vehicle's ability to cope with dynamic driving scenarios.

[0004] According to a first aspect of this disclosure, a vehicle control method is provided, the method comprising:

[0005] Acquire first environmental information about the vehicle's location based on user input, and second environmental information about the vehicle's location based on data perceived by the vehicle perception component.

[0006] Based on the first environmental information and the second environmental information, driving control commands for the vehicle are generated.

[0007] Optionally, generating the vehicle driving control command based on the first environmental information and the second environmental information includes:

[0008] Determine the scene map of the area where the vehicle is located;

[0009] Based on the scene map and the first environmental information, a target driving strategy adapted to the scene is generated.

[0010] Based on the target driving strategy and the second environmental information in the first time window, driving control commands for the vehicle are generated.

[0011] Optionally, based on the scene map and the first environmental information, a target driving strategy adapted to the scene is generated, including:

[0012] Based on the first environmental information, determine the decision weight of each map element in the scene map;

[0013] The target driving strategy is generated based on the decision weights of each map element.

[0014] Optionally, generating a target driving strategy adapted to the scene based on the scene map and the first environmental information includes:

[0015] The scene map and the first environmental information are input into the first decision module to obtain the target driving strategy;

[0016] The step of generating driving control commands for the vehicle based on the target driving strategy and the second environmental information in the first time window includes:

[0017] The target driving strategy and the second environmental information in the first time window are input into the second decision module to generate driving control instructions; wherein, the processing cycle of the second decision module generating the driving control instructions is less than the processing cycle of the first decision module generating the target driving strategy.

[0018] Optionally, the user input may also include information about driving strategies, and the method may further include:

[0019] Obtain the first driving strategy determined based on user input;

[0020] The step of generating driving control commands for the vehicle based on the first environmental information and the second environmental information includes:

[0021] Based on the first environmental information, the second environmental information, and the first driving strategy, driving control commands for the vehicle are generated.

[0022] Optionally, generating driving control commands for the vehicle based on the first environmental information, the second environmental information, and the first driving strategy includes:

[0023] Determine the scene map of the area where the vehicle is located;

[0024] Based on the scene map and the first environmental information, a second driving strategy is generated;

[0025] Based on the scene map, determine the effectiveness of the first driving strategy;

[0026] If the first driving strategy is effective, the first score of the first driving strategy and the second score of the second driving strategy are determined based on the set evaluation model.

[0027] Based on the first score and the second score, select one trip strategy as the target driving strategy from the first driving strategy and the second driving strategy.

[0028] Based on the target driving strategy and the second environmental information corresponding to the first time window, driving control commands for the vehicle are generated.

[0029] Optionally, the step of selecting a trip strategy as the target driving strategy from the first driving strategy and the second driving strategy based on the first score and the second score includes:

[0030] If the first score is higher than the second score, the first driving strategy will be used as the target driving strategy.

[0031] If the second score is higher than the first score, the second driving strategy will be output for user confirmation.

[0032] With user confirmation, the second driving strategy will be used as the target driving strategy;

[0033] If the user does not confirm, the first trip strategy will be used as the target driving strategy.

[0034] Optionally, the scene map for determining the location of the vehicle includes:

[0035] Based on the second environmental information in the second time window, a first map element of the scene is determined; wherein the start time of the second time window is before the start time of the first time window;

[0036] The scene map is obtained based on the first map element and the pre-stored second map element of the scene.

[0037] Optionally, the user input includes voice input, and the first environmental information is determined by parsing the text data converted from the user input.

[0038] According to a second aspect of this disclosure, an electronic device is provided according to some embodiments, the electronic device comprising:

[0039] processor;

[0040] Memory used to store processor-executable instructions;

[0041] The processor is configured to implement the method according to the first aspect of this disclosure when executing instructions stored in the memory.

[0042] According to a third aspect of this disclosure, a chip is provided according to some embodiments, the chip including:

[0043] Storage unit for storing computer programs; and,

[0044] A processing unit configured to implement the method according to the first aspect of the present disclosure when executing a computer program stored in the storage unit.

[0045] According to a fourth aspect of this disclosure, a vehicle is provided according to some embodiments, the vehicle may include electronic equipment according to a second aspect of this disclosure; or, may include a chip according to a third aspect of this disclosure; or, may include:

[0046] processor;

[0047] Memory used to store processor-executable instructions;

[0048] The processor is configured to implement the method according to the first aspect of this disclosure when executing instructions stored in the memory.

[0049] According to a fifth aspect of this disclosure, a non-volatile computer-readable storage medium is also provided, having stored thereon computer program instructions that, when executed by a processor, implement the method described in the first aspect of this disclosure.

[0050] According to embodiments of this disclosure, a vehicle control method is provided. This method can fully and effectively understand the vehicle's current scenario based on user input and data perceived by vehicle perception components, thereby facilitating the generation of effective driving control commands to cope with the current scenario and improving the vehicle's ability to cope with dynamic driving scenarios.

[0051] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with their description, serve to explain the principles of the invention.

[0053] Figure 1 This is a schematic diagram of an intelligent connected system to which the methods provided in the embodiments of this disclosure can be applied;

[0054] Figure 2 It is based on Figure 1 The illustrated embodiment provides a schematic diagram of a vehicle;

[0055] Figure 3 This is a flowchart illustrating a vehicle control method according to some embodiments;

[0056] Figure 4 This is a flowchart illustrating a vehicle control method according to other embodiments;

[0057] Figure 5 This is a schematic diagram of the composition structure of an intelligent driving system according to some embodiments;

[0058] Figure 6This is a flowchart illustrating a vehicle control method according to some other embodiments;

[0059] Figure 7 This is a schematic diagram of the chip's structural composition according to some embodiments;

[0060] Figure 8 This is a schematic diagram of the composition structure of an electronic device according to some embodiments;

[0061] Figure 9 This is a schematic diagram of the composition of a vehicle according to some embodiments. Detailed Implementation

[0062] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0063] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0064] Techniques, methods, and apparatus known to those skilled in the art in the relevant field may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0065] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0066] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0067] This disclosure relates to a vehicle control scheme for human-machine co-driving in the field of intelligent driving. Human-machine co-driving is a driving mode in which the user and the intelligent driving system cooperate to complete driving tasks, and can be used to deal with relatively complex dynamic driving scenarios. Dynamic driving scenarios here refer to non-standardized driving scenarios that change relative to a set standardized scenario, such as traffic light malfunctions, detours around construction areas, traffic accidents or other emergencies, parking in narrow spaces, and severe weather. Through human-machine co-driving, the problem of intelligent driving systems being unable to respond quickly and effectively to current scenarios due to insufficient understanding of dynamic driving scenarios can be solved.

[0068] Vehicle control involves driving strategies and the driving control commands that execute those strategies. Driving strategies constrain the long-term behavior of a vehicle during a single driving task, such as route planning, speed control, and lane selection; they are the higher-level guiding principles for vehicle operation. Driving control commands, on the other hand, are specific operational commands based on the driving strategy. They are concrete control signals that control the vehicle's actions at a specific moment, such as controlling steering wheel angle, throttle opening, and braking force. These commands are the concrete execution of the driving strategy and have a high degree of real-time performance.

[0069] In human-machine co-driving technologies, one possible co-driving mode is where the user inputs driving strategy commands into the intelligent driving system via voice, such as "move to the left lane," "maintain a distance of at least 3 meters from the vehicle in front," or "go straight at the intersection ahead." The intelligent driving system then directly controls the vehicle's actions according to the user-provided driving strategy. In this mode, the intelligent driving system simply executes the user-instructed driving strategy and does not participate in driving decision-making. This means that the precise environmental information perceived by sensors is not applied to driving decisions, which not only poses driving risks but also fails to utilize the intelligent driving system's precise planning capabilities to generate driving strategies that are efficient, safe, and reasonable.

[0070] Among the technologies related to human-machine co-driving, another possible co-driving mode is where the intelligent driving system, based on environmental information perceived by sensors, determines whether the user-input driving strategy poses a danger, and outputs a hazard warning when a danger is detected. While this co-driving mode improves safety to some extent, the intelligent driving system only passively provides hazard warnings and does not participate in actual driving decisions; the precise planning capabilities of the intelligent driving system are still not effectively utilized.

[0071] In human-machine co-driving technologies, another co-driving approach involves the intelligent driving system parsing user input to obtain the user's intentions regarding driving strategies, such as route preferences, safety requirements, or destination. Based on these parsed intentions and environmental perception information from sensors, the intelligent driving system generates driving strategies and corresponding control commands that align with the user's intent. In this approach, although the intelligent driving system participates in driving decisions, these decisions are still constrained by the user's intentions. The intelligent driving system plays a supporting role in driving decision-making and does not fully utilize its precise planning capabilities.

[0072] It is evident that human-machine co-driving is typically applied in relatively complex and dynamically changing scenarios, while the environmental perception capabilities of onboard sensors are usually one-dimensional, affecting the intelligent driving system's ability to understand dynamic driving scenarios. Therefore, in human-machine co-driving, the user is essentially the driving decision-maker, limiting the precise planning capabilities of the intelligent driving system. To address this issue, this disclosure proposes a vehicle control method that assists the intelligent driving system in environmental understanding, thereby fully leveraging the system's precise planning capabilities.

[0073] The method described in this disclosure can be applied to intelligent connected systems. Figure 1 An intelligent connected system 100 that can apply the methods provided in the embodiments of this disclosure is illustrated. Figure 1 As shown, the intelligent connected system 100 may include: a vehicle 101, a server 102, and a user terminal 103. The aforementioned intelligent driving system can be deployed on the vehicle 101, on the server 102, or on both the vehicle 101 and the server 102.

[0074] In some examples, vehicle 101 may be a vehicle equipped with autonomous driving capabilities. Autonomous driving, also known as intelligent driving, refers to a vehicle capable of performing driving tasks such as environmental perception, decision-making, planning, and control execution. The levels of autonomous driving can be classified according to the vehicle intelligence grading standards established by the Society of Automotive Engineers (SAE), for example, L0 is manual driving, L1 is driver assistance, L2 is partial autonomous driving, L3 is conditional autonomous driving, L4 is highly automated driving, and L5 is fully automated driving. The above classification of autonomous driving levels is merely illustrative, and this disclosure does not limit the classification standards and levels of autonomous driving.

[0075] In some examples, server 102 can be a single server or a distributed server cluster consisting of multiple servers, and its deployment can include local servers and / or cloud servers. Server 102 can communicate with vehicle 101 and / or user terminal 103 via a communication network, providing various services to vehicle 101 and / or user terminal 103. For example, the server can receive perception data sent by the vehicle and provide services such as high-precision maps, data analysis, and decision planning for the vehicle. Alternatively, server 102 can receive query commands or control commands sent by user terminal 103, providing corresponding services to the user.

[0076] In some examples, the user terminal 103 can be any form of electronic device that provides services to the user, such as a personal computer, laptop, smart tablet, smartphone, smart wearable device, etc. The user can interact with the vehicle 101 or server 102 through the human-machine interface terminal configured in the vehicle 101, or through the user terminal 103. For example, the user terminal can query the vehicle's status and / or parameters, or control the vehicle to perform set tasks and / or modify configuration parameters. The user terminal 103 runs an application based on the intelligent connected system to achieve interaction with the vehicle 101 or server 102. This application can be a local application, a web application, or a mini-program, etc., and is not limited thereto.

[0077] In some examples, the aforementioned application running on user terminal 103 can provide authentication or authorization services to users. Users who are successfully authenticated and granted the corresponding permissions can query and / or control the vehicle within the scope of the granted permissions.

[0078] Vehicle 101, server 102, and user terminal 103 can communicate via a communication link provided by communication network 104. This communication network 104 can include one or more networks of any type, such as the Internet, Local Area Network (LAN), Wide Area Network (WAN), Virtual Private Network (VPN), Public Switched Telephone Network (PSTN), satellite communication network, Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IoT, eMTC, infrared, Bluetooth, NFC, or a combination of these networks. The communication networks between vehicle 101 and server 102, between user terminal 103 and server 102, and between user terminal 103 and vehicle 101 can be the same or different.

[0079] It should be noted that, Figure 1 The structure of the intelligent connected system 100 shown is merely illustrative. The intelligent connected system in this embodiment is not limited to the above structure and may include more or fewer devices as needed, and the devices may be combined or separated. For example, the intelligent connected system may not include... Figure 1 User terminals and / or servers in the system; for example, user terminals and servers can be deployed together.

[0080] Figure 2 It is based on Figure 1 The illustrated embodiment provides a schematic diagram of a vehicle 101. (As shown) Figure 2As shown, the vehicle 101 may include a sensing component 1011, a computing platform 1012, an execution component 1013, etc. The sensing component 1011, the computing platform 1012, and the execution component 1013 may be connected via a bus or other means.

[0081] In some examples, the perception component 1011 can be used to collect information about the vehicle itself or its external environment. The perception component 1011 may include at least one of a visual sensing unit, radar, a positioning and navigation unit, an inertial measurement unit (IMU), or other sensing units. The visual sensor unit may include one or more cameras, the radar may include at least one of lidar, millimeter-wave radar, ultrasonic radar, or other radars, and the positioning and navigation unit may include at least one of a GPS system, a BeiDou system, or other global positioning systems.

[0082] In some examples, the perception component 1011 may also include a vehicle-to-everything (V2X) device for enabling communication between the vehicle and other objects to obtain data representing the vehicle's surrounding environment. For example, the V2X device may be used to implement at least one of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N) communication.

[0083] In some examples, the computing platform 1012 may include a computing-capable device for processing the perception information collected by the perception component 1011 to obtain control information, and sending corresponding control commands to the execution component 1013 to cause the execution component 1013 to perform corresponding actions, thereby achieving control of the vehicle 101. For example, the computing platform 1012 can perform Simultaneous Localization and Mapping (SLAM), path planning, and behavior decision-making on the vehicle, thereby achieving autonomous vehicle control. The computing platform 1012 may include at least one processor and at least one memory, whereby each processor can individually or jointly execute instructions stored in the memory to implement the methods provided in the embodiments of this disclosure. The processor in the embodiments of this disclosure may include at least one of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), a Micro Controller Unit (MCU), or other processors. Memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. In addition to storing instructions, memory can also store data, such as high-definition maps, route information, vehicle position, direction, and speed. The data stored in memory can be accessed and used by the processor.

[0084] In some examples, the vehicle's computing platform can perform intelligent driving computing tasks independently, or it can communicate with a server to complete the computing tasks. For example, the vehicle's computing platform can cooperate with a server to complete the corresponding computing tasks.

[0085] The computing platform 1012 can be installed in the vehicle 101. Some or all of the computing platform 1012 can also be installed in the server corresponding to the vehicle. For example, some functions of the computing platform 1012 with high real-time requirements can be installed in the vehicle, while other functions with lower real-time requirements can be installed in the server corresponding to the vehicle.

[0086] In some examples, the execution component 1013 is used to perform corresponding actions based on the control of the computing platform 1012, enabling the vehicle 101 to complete the movement task. The execution component 1013 may include, for example, a power component, a braking component, a transmission component, a steering component, etc., for acting according to driving control commands.

[0087] It should be noted that, Figure 2 The structure of vehicle 101 shown is merely illustrative. The vehicle in this embodiment is not limited to the above structure and may include more or fewer components as needed. The devices may also be combined or separated. For example, the vehicle may not include the aforementioned computing platform. Furthermore, the vehicle may also include communication components, interface components, multimedia components, input components, output components, etc.

[0088] The following is combined with Figure 1 The systems shown illustrate various embodiments of this disclosure.

[0089] <First Embodiment>

[0090] Figure 3 A vehicle control method according to some embodiments is illustrated. This method can, for example, be... Figure 1 The implementation of vehicle 101 can also be carried out by... Figure 1 The implementation can be carried out by server 102, or jointly by vehicle 101 and server 102, specifically by an intelligent driving system deployed on at least one of vehicle 101 and server 102. For example... Figure 3 As shown, the vehicle control method of this embodiment may include the following steps S310 and S320.

[0091] Step S310: Obtain first environmental information about the vehicle's location based on user input, and second environmental information about the vehicle's location based on data perceived by the vehicle perception component.

[0092] In this embodiment, user input may include at least one of the following: voice, text input through the vehicle terminal or user terminal, circle markings on the map displayed on the vehicle terminal or user terminal, and operations to adjust the pose of the vehicle sensors in the vehicle coordinate system.

[0093] Vehicle perception components may include on-board sensors, which may include at least one of the following: cameras, lidar, millimeter-wave radar, ultrasonic sensors, inertial measurement units (IMUs), positioning devices, etc.

[0094] The vehicle perception component may further include vehicle-to-everything (V2X) devices to obtain data reflecting environmental information about the vehicle's location through communication between the V2X devices and other objects.

[0095] In this embodiment, the scene in which the vehicle is located is a spatiotemporal region. This spatiotemporal region can be the area that the user or the vehicle's sensing components can reach in time and space, rather than simply referring to the spatial location of the vehicle.

[0096] In this embodiment, the environmental information refers to map element information reflecting the scene in which the vehicle is located. Map elements can be any element related to vehicle control, including but not limited to roads, traffic lights, road markings, traffic conditions, and weather conditions. In other words, the environmental information in this embodiment is used by the intelligent driving system to understand the scene in which the vehicle is located, enabling the intelligent driving system to control the vehicle to move reliably within the current scene to complete the driving task.

[0097] The environmental information in this embodiment includes first environmental information and second environmental information. The first environmental information is determined by parsing user input, and the second environmental information is determined by parsing data sensed by the vehicle perception component.

[0098] In this embodiment, the environmental information obtained through step S310 may include dynamic map element information of the scene where the vehicle is located, and may further include static map element information of the scene where the vehicle is located. Static map elements refer to elements that do not usually change over time or do not usually change in the first time period, while dynamic map elements refer to map elements that usually change over time in the second time period. The duration of the first time period is longer than the duration of the second time period. The duration of the first time period is, for example, "month" or "week", etc., and is not limited here.

[0099] The dynamic map elements mentioned above include at least one of the following: traffic participants, road conditions, weather conditions, traffic conditions, and spatial constraints. Traffic participants include other vehicles and pedestrians, and their information may include their location and speed. Road conditions include road smoothness, road obstacles, road slope, and road slipperiness. Weather conditions include visibility and rain / snow conditions. Traffic conditions include traffic light malfunctions and roadblock installations. Spatial constraint information reflects the space available for vehicle movement. The static traffic elements mentioned above include, for example, roads, lanes, traffic lights, signs, and road markings.

[0100] In this embodiment, the first environmental information and the second environmental information may involve different information content. That is, the first environmental information expressed by the user input can be environmental information that the vehicle sensor cannot capture or cannot capture quickly. For example, the first environmental information includes at least one of traffic condition information, weather condition information, road condition information, and spatial constraint information.

[0101] In this embodiment, the second environmental information may also include at least a portion of the first environmental information. The first environmental information can be used to identify the attention level of traffic elements in the scene where the vehicle is located, so that when the intelligent driving system makes driving decisions, it can pay attention to the traffic elements associated with the first environmental information, and make a fast and effective response in vehicle control in combination with user prompts.

[0102] In this embodiment, based on the specific manner of user input, the intelligent driving system can parse the user input through at least one pre-set functional module to determine the first environmental information expressed by the user input.

[0103] In some examples, the initial environmental information can be determined by parsing text data converted from user input. In this example, the user input can be converted into text data by a recognition module, and then parsed by a Natural Language Processing (NLP) module based on Large Language Models (LLM) to obtain the initial environmental information. This NLP module can also be understood as a parsing module. In this example, the user input includes at least one of speech, text, and images. Converting the user input into text data for processing leverages the excellent text data understanding capabilities of the NLP model, accurately interpreting the user input, and obtaining initial environmental information that meets the user's expectations. This improves the accuracy and effectiveness of vehicle control based on the initial environmental information.

[0104] In some examples, user input includes voice. Accordingly, the intelligent driving system can use a voice recognition module to convert the voice input into text data, allowing for the analysis of the initial environmental information expressed by the user. In this example, the user can easily describe the vehicle's location using natural language, without requiring manual operation, offering convenience, speed, and efficiency.

[0105] In another example, the intelligent driving system can also process user input through other types of functional modules. For example, if the user input includes an image, the intelligent driving system can process the image through a deep learning-based target recognition module to obtain initial environmental information. This is not a limitation.

[0106] In this embodiment, the intelligent driving system can integrate data collected by different sensing components to obtain second environmental information.

[0107] Regarding the determination of second environmental information based on data collected by vehicle-mounted sensors, the intelligent driving system can use any means known to those skilled in the art, without limitation. For example, the intelligent driving system can use at least one functional module, such as a first-type computing module, a second-type computing module, or an expert knowledge base module, to analyze the data collected by the vehicle-mounted sensors and thus determine the second environmental information reflected in the data. The first-type computing model is a model expressed by a defined functional relationship. The second-type computing model is a neural network model composed of multiple layers of neurons, etc., and can be trained using sample data based on the task to be performed. The expert knowledge base is generated based on rule settings and determines the corresponding results according to the conditions satisfied by the input data.

[0108] Step S320: Based on the first environmental information and the second environmental information, generate vehicle driving control commands.

[0109] In this embodiment, the first environmental information can be used to generate a driving strategy. For example, the intelligent driving system can focus on map elements associated with the first environmental information in the scene map to generate a driving strategy. Alternatively, the intelligent driving system can combine the first environmental information to determine the scene map used to generate the driving strategy.

[0110] In this embodiment, the second environmental information can be used to generate driving control commands that execute the driving strategy.

[0111] Of course, the second environmental information can also be combined with the first environmental information to generate driving strategies, and this is not limited here. When used to generate driving strategies, the second environmental information can be used to determine the scene map of the vehicle's location required to generate the driving strategy.

[0112] The following examples illustrate the application of generating driving strategies based on the first environmental information in different scenarios.

[0113] For example, in the scenario of a traffic light malfunction, if the user inputs the message "The traffic light in the straight lane ahead is malfunctioning", the intelligent driving system can determine the first environmental information "The traffic light associated with the straight lane at the intersection ahead is malfunctioning" based on the user input. At this time, the intelligent driving system can pay attention to the surrounding vehicle traffic conditions and / or traffic police command situation related to the first environmental information and generate corresponding driving strategies.

[0114] For example, in a scenario where a road is closed due to construction, if the user inputs the message "The road ahead is closed due to construction," the intelligent driving system can determine the first environmental information that "the road ahead is closed and cannot be passed" based on the user's input. At this time, the intelligent driving system can focus on other connected roads and generate corresponding driving strategies.

[0115] For example, in scenarios where there are sudden events such as pedestrians crossing the road or traffic accidents ahead, if the user inputs a prompt such as "a pedestrian is crossing the road" or "there is a traffic accident ahead", the intelligent driving system can determine the corresponding first environmental information based on the user input. At this time, the intelligent driving system can focus on the "target pedestrian" or "target accident" related to the first environmental information and generate driving strategies such as deceleration and / or lane changing.

[0116] For example, in scenarios where the weather changes suddenly, such as dense fog or heavy rain, if the user inputs "dense fog", the intelligent driving system can determine the first environmental information of "low visibility" based on the user input. At this time, the intelligent driving system can pay attention to the headlights of other vehicles related to the first environmental information and generate driving strategies such as determining the position of other vehicles based on their headlights.

[0117] For example, in the scenario of parking in a narrow space, if the user inputs the prompt "There is not enough space ahead, the right-hand parking space is available", the intelligent driving system can determine the corresponding first environmental information based on the user input. At this time, the intelligent driving system can focus on the right-hand parking space related to the first environmental information and generate a corresponding driving strategy. The driving strategy in this scenario can also be understood as a parking strategy.

[0118] In some examples, the intelligent driving system can verify the first environmental information through data perceived by the vehicle's perception components. If the first environmental information passes the verification, the system can then control the vehicle based on the first environmental information to improve the safety of vehicle control.

[0119] In some examples, such as Figure 4 As shown, step S320, which generates vehicle driving control commands based on the first environmental information and the second environmental information, may include the following steps S3211 to S3213:

[0120] Step S3211: Determine the scene map of the scene where the vehicle is located.

[0121] The scene map in step S3211 can be a basic scene map determined based on the vehicle's location. This basic scene map can be pre-stored in a database for use by the intelligent driving system. The basic scene map can be pre-created based on at least one of the following: satellite imagery, aerial photography, or ground vehicle mapping. The basic scene map can also be updated based on data obtained from the traffic system by the server; this is not limited here. The basic scene map can include static map element information, and may further include dynamic map element information; this is not limited here.

[0122] The scene map in step S3211 can also be determined based on the second environmental information, or based on both the second and first environmental information. The environmental information can be used to determine static and dynamic map elements.

[0123] The scene map in step S3211 can also be determined based on the above-mentioned basic scene map and second environment information, or based on the basic scene map, second environment information and first environment information, without any limitation here.

[0124] Since the scene map can include dynamic map elements, the scene map in step S3211 can be the scene map of the vehicle's location within a set time window, so as to observe the changes of dynamic map elements over time, which is beneficial for making driving decisions.

[0125] Step S3212: Generate a target driving strategy that is adapted to the scene where the vehicle is located, based on the scene map and the first environment information.

[0126] In step S3212, map elements associated with the first environment information can be marked in the scene map based on the first environment information, and a target driving strategy can be generated based on the marked map elements.

[0127] In some examples, step S3212, which generates a target driving strategy adapted to the scene where the vehicle is located based on the scene map and the first environment information, may include the following steps: determining the decision weight of each map element in the scene map based on the first environment information; and generating the target driving strategy based on the decision weight of each map element.

[0128] Those skilled in the art will understand that intelligent driving systems formulate driving strategies based on information from map elements in a scene map using decision-making algorithms. These map elements have predetermined decision weights in the decision-making algorithm. In this example, the intelligent driving system can adjust the decision weights of each map element involved in the decision-making algorithm based on first environmental information. For example, map elements associated with the first environmental information can be given a larger decision weight compared to other map elements. Another example is that when the first environmental information involves at least two map elements, different decision weights can be set for these at least two map elements according to their degree of association. Yet another example is that, based on the predetermined decision weights, the decision weights of map elements associated with the first environmental information can be increased, while the decision weights of map elements unrelated to the first environmental information can be decreased. The adjustment step size can be set according to the degree of association between the corresponding map element and the first environmental information.

[0129] In this example, since user input is usually information provided by the user based on driving experience that is helpful in effectively dealing with the current driving scenario, the decision weight of each map element in the scenario map is determined based on the first environmental information. This enables the intelligent driving system to use the experience of human drivers to deal with complex dynamic driving scenarios, solving the problem that relying on vehicle perception components cannot fully understand the scenario in which the vehicle is located and therefore has difficulty dealing with dynamic driving scenarios.

[0130] In some examples, in step S3212, the scene map and the first environmental information can be input into the first decision module to obtain the target driving strategy.

[0131] For example, the first decision module can be configured to: use the first environmental information as a query vector, perform cross-attention query in the scene map, query map elements associated with the first environmental information in the scene map, and generate a target driving strategy based on the queried map elements.

[0132] For example, the first decision module can be configured to: determine the decision weight of each map element in the scene map based on the first environmental information; and generate a target driving strategy based on the decision weight of each map element.

[0133] The first decision module can be a visual language model (VLM). Visual language models perform well in understanding and processing scenarios that combine image data and text data. Therefore, in this example, using a visual language model as the first decision module is beneficial for integrating scene map and first environmental information to generate a target driving strategy that can effectively cope with the current driving scenario.

[0134] Of course, the first decision module can also be a neural network model with other structures, which is not limited here.

[0135] Step S3213: Generate vehicle driving control commands based on the target driving strategy and the second environmental information in the first time window.

[0136] When executing a target driving strategy, an intelligent driving system can generate driving control commands based on the second environmental information within a first time window. Here, compared to the driving strategy, the driving control commands have higher real-time requirements; therefore, depending on the vehicle control needs, the first time window can be a second-level window, or even correspond to a single data acquisition moment.

[0137] When a scene map is determined based on second environmental information, the second environmental information used to determine the scene map, or in other words, for driving decisions, can have a relatively long time window. In other words, the validity period of driving decisions is longer than the validity period of driving control commands. For the second time window corresponding to the second environmental information used for trip decisions, its start time can be at least earlier than the first time window to achieve advance decision-making.

[0138] Furthermore, those skilled in the art should understand that in step S3213, the generation of driving control commands should incorporate not only the target driving strategy and the second environmental information, but also conventional vehicle status information, which will not be elaborated upon here. Vehicle status information refers to information representing the vehicle's own state, such as vehicle position, current wheel speed, current steering angle, current attitude, etc.

[0139] Therefore, in some examples, determining the scene map of the scene where the vehicle is located in step S3211 may include the following steps: determining a first map element based on the second environmental information of the second time window, wherein the start time of the second time window is before the start time of the first time window; and obtaining the scene map based on the first map element and the pre-stored second map element of the scene.

[0140] In the example, the second map element can be a map element from the basic scene map described above. Combining the second environmental information to determine the scene map can effectively improve the accuracy of the scene map, thereby helping to ensure the accuracy and reliability of the generated driving decisions.

[0141] In some examples, step S3213, which generates vehicle driving control instructions based on the target driving strategy and the second environmental information in the first time window, may include: inputting the target driving strategy and the second environmental information in the first time window into the second decision module to generate driving control instructions.

[0142] In this example, the second decision module can be an end-to-end model with high processing efficiency to meet the real-time requirements of driving control commands. The second decision module can be a neural network-based model.

[0143] In some examples, a target driving strategy can be generated based on the first decision module, and driving control commands can be generated based on the second decision module. The processing cycle for generating the target driving strategy by the first decision module is longer than the processing cycle for generating the driving control commands by the second decision module. Here, the first decision module can be understood as a slow network equivalent to the "human brain," while the second decision module can be understood as a fast network equivalent to the "human cerebellum." Through the combination of the slow and fast networks, the intelligent driving system can simultaneously handle complex strategy optimization and rapid real-time scene changes. The slow network continuously optimizes and adjusts the global driving strategy, while the fast network is responsible for timely response to changes in data collected by onboard sensors and generating driving control commands, thereby improving the intelligent driving system's ability to cope with dynamic driving scenarios.

[0144] In these examples, such as Figure 5 As shown, the intelligent driving system may include a recognition module, a parsing module, a first decision module, and a second decision module connected in series. The recognition module is used to convert user input to obtain user data that conforms to the input data format of the parsing module. For example, the user data is... Figure 5 The text data shown is analyzed. A parsing module parses the user data to obtain first environmental information and inputs this information into a first decision module. The first decision module generates a target driving strategy based on the first environmental information and inputs this strategy into a second decision module. The second decision module generates driving control commands to execute the target driving strategy and outputs these commands to the drive unit of the vehicle's execution components, causing the drive unit to drive the corresponding execution components based on the driving control commands.

[0145] According to steps S310 and S320, the method of this embodiment can achieve a full understanding of the vehicle's current scenario based on the environmental information described by the user input. Since it enhances the understanding of environmental information that can effectively cope with the current driving scenario, it is conducive to generating driving control commands that can effectively cope with the current scenario, thereby improving the vehicle's ability to cope with dynamic driving scenarios.

[0146] <Second Embodiment>

[0147] Figure 6 A vehicle control method according to some other embodiments of the present disclosure is illustrated. In this embodiment, unlike the first embodiment, the user input includes not only information about the vehicle's location to determine first environmental information, but also information about driving strategies.

[0148] like Figure 6 As shown, the method of this embodiment may include the following steps S610 and S620.

[0149] Step S610: Obtain first environmental information of the vehicle's location scene determined based on user input, second environmental information of the vehicle's location scene determined based on data perceived by the vehicle perception component, and first driving strategy determined based on user input.

[0150] In this embodiment, the user simultaneously provides environmental information and driving strategy information based on that environmental information.

[0151] For example, in the scenario of a traffic light malfunction, if the user inputs the prompt "The traffic light in the straight lane ahead is malfunctioning, follow traffic instructions to proceed", the intelligent driving system can determine the first environmental information "The traffic light associated with the straight lane at the intersection ahead is malfunctioning" and the first driving strategy "follow traffic police instructions to proceed" based on the user input.

[0152] For example, in a scenario where a road is closed due to construction, if the user inputs the prompt "The road ahead is closed due to construction, detour via the small road on the right", the intelligent driving system can determine the first environmental information "the road ahead is closed and cannot be passed" and the first driving strategy "detour via the small road on the right" based on the user input.

[0153] For example, in the event of a sudden incident such as a pedestrian crossing the road or a traffic accident ahead, the user inputs a prompt such as "A pedestrian is crossing the road, please slow down" or "There is a traffic accident ahead, switch to the right lane". The intelligent driving system can determine the corresponding first environmental information and the first driving strategy of "slowing down or switching lanes" based on the user input.

[0154] For example, in scenarios where the weather changes suddenly, such as dense fog or heavy rain, if the user inputs the prompt "Dense fog, keep a distance of at least 5 meters from the vehicle in front," the intelligent driving system can determine the first environmental information of "low visibility" and the first driving strategy of "controlling the distance from the vehicle in front" based on the user input.

[0155] For example, in the scenario of parking in a narrow space, if the user inputs the prompt "There is not enough space ahead, the right-hand parking space is available, use the right-hand parking space to make a U-turn", the intelligent driving system can determine the first environmental information about the status of adjacent parking spaces based on the user input, as well as the first driving strategy of "using the right-hand parking space to make a U-turn".

[0156] Step S620: Based on the first environmental information, the second environmental information, and the first driving strategy, generate driving control commands for the vehicle.

[0157] In this embodiment, the intelligent driving system can generate driving control commands by integrating the first driving strategy, the first environmental information, and the second environmental information, rather than simply generating driving control commands directly based on the first driving strategy, thereby improving the safety and rationality of vehicle control.

[0158] In some examples, the effective portion of the first driving strategy can be determined based on the first environmental information and the scene map. This effective portion can be understood as the feasible part that conforms to the driving task, and a target driving strategy can be generated based on this effective portion. If all aspects of the first driving strategy are effective, the first driving strategy can be used as the target driving strategy. If all aspects of the first driving strategy are ineffective, driving control commands can be generated based on the first environmental information and the second environmental information, referring to the first embodiment.

[0159] In other examples, to reduce the complexity of vehicle control, decrease the amount of data processing, and improve processing speed, step S620, which generates vehicle driving control commands based on the first environmental information, the second environmental information, and the first driving strategy, may also include the following steps:

[0160] Step S6211: Determine the scene map of the scene where the vehicle is located.

[0161] Step S6211 can be understood by referring to step S3211 above, and will not be repeated here.

[0162] Step S6212: Generate a second driving strategy based on the scene map and the first environment information; determine the first score of the first driving strategy and the second score of the second driving strategy based on the set evaluation model; and select a trip strategy as the target driving strategy from the first driving strategy and the second driving strategy based on the first score and the second score.

[0163] The second driving strategy in this embodiment is equivalent to the target driving strategy in the first embodiment. The step S6212, "generate the second driving strategy according to the scene map and the first environment information", can be understood by referring to the above step S3212.

[0164] Building upon the first embodiment, this embodiment uses an evaluation model to score the first driving strategy and the second travel strategy, selecting the driving strategy with the highest score as the final target driving strategy. The evaluation model can include evaluations across multiple dimensions such as distance, time, and comfort, and is not limited here.

[0165] In some examples, in step S6212, the effectiveness of the first driving strategy can be determined based on the scene map. If the first driving strategy is effective, the first driving strategy and the second driving strategy can be scored based on the set evaluation model. If it is determined that the first driving strategy cannot complete the driving task, the second driving strategy can be used as the target driving strategy.

[0166] In some examples, if the first rating is higher than the second rating, the first driving strategy can be used as the target driving strategy. If the second rating is higher than the first rating, the second driving strategy can be output for user confirmation, and if the user confirms, the second driving strategy can be used as the target driving strategy; otherwise, if the user does not confirm, the first driving strategy can be used as the target driving strategy.

[0167] Users can confirm or deny via voice. If no confirmation instruction is received from the user within the set time, it can be considered that the user has not confirmed.

[0168] Here, since the first driving strategy reflects the user's intent, if the second rating is higher than the first, the user can be asked to confirm whether to use the second driving strategy. Only if the user confirms will the driving be based on the second driving strategy. This approach, compared to directly using the second driving strategy as the target driving strategy, provides the user with a better driving strategy while fully respecting the user's wishes and improving the user experience.

[0169] Step S6213: Generate driving control commands for the vehicle based on the target driving strategy and the second environmental information in the first time window.

[0170] This step S6213 can be understood by referring to step S3213 above, and will not be repeated here.

[0171] In some examples, when the user input does not contain information about driving policies, driving control commands are generated based on first environmental information and second environmental information; while when the user input contains information about driving policies, driving control commands can be generated based on first environmental information, second environmental information, and first driving policies.

[0172] According to steps S610 and S620, in this embodiment, when the user inputs information about driving strategy, driving control can also be performed in combination with a first driving strategy determined based on the information, thereby improving the user experience.

[0173] <Third Embodiment>

[0174] This embodiment provides a chip capable of implementing the methods of the embodiments of this disclosure, such as... Figure 7 As shown, the chip 700 includes a storage unit 720 and a processing unit 710. The storage unit 720 is used to store a computer program. The processing unit 710 is configured to implement the method according to any embodiment of this disclosure when executing the computer program stored in the storage unit.

[0175] Chip 700 can be a processor chip with data processing capabilities.

[0176] Chip 700 can be a processor chip used in Automated Driving Control Units (ADCUs), etc.

[0177] Chip 700 can be a system-on-a-chip (SoC) that integrates multiple processors, multiple memories, I / O interfaces, etc., to achieve miniaturized controller design.

[0178] <Fourth Embodiment>

[0179] This embodiment provides an electronic device, such as... Figure 8 As shown, the electronic device 800 includes a memory 802 and a processor 801. The memory 802 is used to store a computer program executed by the processor 801. The processor 801 is configured to implement the method according to any embodiment of the present disclosure when executing the computer program stored in the memory 802.

[0180] The electronic device 800 may be a server or controller for implementing the methods of the embodiments of this disclosure. The controller may be, for example, an intelligent driving domain controller or a vehicle's central controller.

[0181] <Fifth Embodiment>

[0182] This embodiment provides a vehicle that may include a chip 700 according to a third embodiment or a controller according to a fourth embodiment.

[0183] In some embodiments, such as Figure 9 As shown, the vehicle 900 may also include a memory 902 and a processor 901. The memory 902 is used to store a computer program executed by the processor 901. The processor 901 is configured to implement the method according to any embodiment of the present disclosure when executing the computer program stored in the memory 902.

[0184] In addition, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that is loaded and executed by a processor to implement the method according to any embodiment of this disclosure.

[0185] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0186] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0187] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0188] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0189] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0190] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0191] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0192] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0193] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A vehicle control method, characterized in that, include: Acquire first environmental information about the vehicle's location based on user input, and second environmental information about the vehicle's location based on data perceived by the vehicle perception component. Based on the first environmental information and the second environmental information, driving control commands for the vehicle are generated.

2. The method according to claim 1, characterized in that, The step of generating driving control commands for the vehicle based on the first environmental information and the second environmental information includes: Determine the scene map of the area where the vehicle is located; Based on the scene map and the first environmental information, a target driving strategy adapted to the scene is generated. Based on the target driving strategy and the second environmental information in the first time window, driving control commands for the vehicle are generated.

3. The method according to claim 2, characterized in that, Based on the scene map and the first environmental information, a target driving strategy adapted to the scene is generated, including: Based on the first environmental information, determine the decision weight of each map element in the scene map; The target driving strategy is generated based on the decision weights of each map element.

4. The method according to claim 2, characterized in that, The step of generating a target driving strategy adapted to the scene based on the scene map and the first environmental information includes: The scene map and the first environmental information are input into the first decision module to obtain the target driving strategy; The step of generating driving control commands for the vehicle based on the target driving strategy and the second environmental information in the first time window includes: The target driving strategy and the second environmental information in the first time window are input into the second decision module to generate driving control instructions; wherein, the processing cycle of the second decision module generating the driving control instructions is less than the processing cycle of the first decision module generating the target driving strategy.

5. The method according to claim 1, characterized in that, The user input also includes information about driving strategies, and the method further includes: Obtain the first driving strategy determined based on user input; The step of generating driving control commands for the vehicle based on the first environmental information and the second environmental information includes: Based on the first environmental information, the second environmental information, and the first driving strategy, driving control commands for the vehicle are generated.

6. The method according to claim 5, characterized in that, The step of generating driving control commands for the vehicle based on the first environmental information, the second environmental information, and the first driving strategy includes: Determine the scene map of the area where the vehicle is located; Based on the scene map and the first environmental information, a second driving strategy is generated; Based on the scene map, determine the effectiveness of the first driving strategy; If the first driving strategy is effective, the first score of the first driving strategy and the second score of the second driving strategy are determined based on the set evaluation model. Based on the first score and the second score, select one trip strategy as the target driving strategy from the first driving strategy and the second driving strategy. Based on the target driving strategy and the second environmental information corresponding to the first time window, driving control commands for the vehicle are generated.

7. The method according to claim 6, characterized in that, The step of selecting a trip strategy as the target driving strategy from the first driving strategy and the second driving strategy based on the first score and the second score includes: If the first score is higher than the second score, the first driving strategy will be used as the target driving strategy. If the second score is higher than the first score, the second driving strategy will be output for user confirmation. With user confirmation, the second driving strategy will be used as the target driving strategy; If the user does not confirm, the first trip strategy will be used as the target driving strategy.

8. The method according to any one of claims 2-4, 6, and 7, characterized in that, The scene map used to determine the location of the vehicle includes: Based on the second environmental information in the second time window, the first map element of the scene is determined; wherein the start time of the second time window is before the start time of the first time window; The scene map is obtained based on the first map element and the pre-stored second map element of the scene.

9. The method according to any one of claims 1-7, characterized in that, The user input includes voice input, and the first environmental information is determined by parsing the text data converted from the user input.

10. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 9 when executing instructions stored in the memory.

11. A chip, characterized in that, include: Storage unit, used to store computer programs; as well as, A processing unit configured to implement the method of any one of claims 1 to 9 when executing a computer program stored in the storage unit.

12. A vehicle, characterized in that, The vehicle includes the electronic device according to claim 10; or, the vehicle includes the chip according to claim 11; or, the vehicle includes: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 9 when executing instructions stored in the memory.

13. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 9.

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