Vehicle control method and device, vehicle, chip, storage medium and program product

Abstract

This disclosure relates to a vehicle control method, device, vehicle, chip, storage medium, and program product, belonging to the field of assisted driving. The method includes: acquiring scene information of the vehicle in a target driving scenario, and determining a target positioning accuracy value for the vehicle in the target driving scenario based on the scene information; determining whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value; controlling the vehicle based on the target positioning state after the switch in response to the switch in the vehicle's positioning state; or controlling the vehicle based on the vehicle's current positioning state in response to the vehicle's positioning state not switching. Therefore, considering the differentiated positioning accuracy requirements of different driving scenarios, it can achieve precise matching between positioning accuracy requirements and target driving scenarios, thereby accurately determining the vehicle's positioning state and enabling on-demand switching of the vehicle's positioning state.

Description

Technical Field

[0001] This disclosure relates to the field of driver assistance technology, and more particularly to a vehicle control method, device, vehicle, chip, storage medium, and program product. Background Technology

[0002] In related technologies, determining the vehicle's location status by using a preset fixed threshold cannot adapt to the actual needs of different scenarios, affecting driving safety and experience. Summary of the Invention

[0003] This disclosure provides a vehicle control method, device, electronic device, vehicle, chip, storage medium, and program product to at least solve the problem in related technologies where the vehicle's positioning status cannot adapt to the actual needs of different scenarios, affecting driving safety and experience. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, a vehicle control method is provided, comprising: acquiring scene information of a vehicle in a target driving scenario, and determining a target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario; determining whether to switch the positioning state of the vehicle based on the magnitude relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle; controlling the vehicle based on the target positioning state after the switch in response to the switching of the vehicle's positioning state; or controlling the vehicle based on the current positioning state of the vehicle in response to the non-switching of the vehicle's positioning state.

[0004] The vehicle control method provided in the embodiments of this disclosure collects scene information of the vehicle in a target driving scenario, determines the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information, and determines whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value. In response to the switching of the vehicle's positioning state, the vehicle is controlled based on the target positioning state after the switch; or, in response to the vehicle's positioning state not switching, the vehicle is controlled based on the vehicle's current positioning state. Therefore, by taking into account the scene information of the vehicle in the target driving scenario and dynamically evaluating the target positioning accuracy value of the vehicle in the target driving scenario (used to indicate the positioning accuracy requirement), the method can take into account the differentiated positioning accuracy requirements of different driving scenarios and achieve precise matching between the positioning accuracy requirement and the target driving scenario.

[0005] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0006] In some possible implementations, determining the target positioning accuracy value of the vehicle in the target driving scenario based on the scenario information of the target driving scenario includes: processing the scenario information of the target driving scenario using a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; wherein, different driving scenarios correspond to different preset algorithms; the scenario information includes at least one of scenario category, road curvature, vehicle speed, and perception confidence.

[0007] Therefore, considering the different positioning accuracy requirements of different driving scenarios, preset algorithms can be set for different driving scenarios, which helps to improve the accuracy of the target positioning accuracy value and achieve a precise match between the positioning accuracy requirement and the target driving scenario.

[0008] In addition, a preset algorithm corresponding to the target driving scenario can be used to process at least one of the following: scenario category, road curvature, vehicle speed, and perception confidence, in order to dynamically evaluate the target positioning accuracy of the vehicle in the target driving scenario.

[0009] In some possible implementations, the scene information includes scene information of multiple dimensions, and the preset algorithm includes weights for scene information of each dimension; in preset algorithms corresponding to different driving scenarios, the weights of the same category are different; the step of processing the scene information of the target driving scenario through the preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value includes: performing a weighted summation of the scene information of each dimension of the target driving scenario based on the weights of the scene information of each dimension to determine the target positioning accuracy value.

[0010] Therefore, taking into account the weight of scene information in each dimension, the scene information in each dimension of the target driving scenario can be weighted and summed to determine the target positioning accuracy value.

[0011] In some possible implementations, determining whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value includes: determining the target positioning state based on the relationship between the target positioning accuracy value and the actual positioning accuracy value; switching the vehicle's positioning state from the current positioning state to the target positioning state in response to the current positioning state representing a higher positioning accuracy than the target positioning state representing a higher positioning accuracy, and the vehicle satisfying the target positioning state for a duration of a first set duration; or, switching the vehicle's positioning state from the current positioning state to the target positioning state in response to the current positioning state representing a lower positioning accuracy than the target positioning state representing a lower positioning accuracy, and the vehicle satisfying the target positioning state for a duration of a second set duration; wherein the first set duration is less than the second set duration.

[0012] Therefore, when the positioning accuracy represented by the current positioning state is higher than that represented by the target positioning state, and the vehicle meets the target positioning state for a duration of a first set duration, the vehicle's positioning state can be switched from the current positioning state to the target positioning state, i.e., the vehicle's positioning state is downgraded. When the positioning accuracy represented by the current positioning state is lower than that represented by the target positioning state, and the vehicle meets the target positioning state for a duration of a second set duration, the vehicle's positioning state can be switched from the current positioning state to the target positioning state, i.e., the vehicle recovers from the downgraded state. This avoids the problem of frequent switching of the vehicle's positioning state due to measurement noise or short-term fluctuations, and improves the vehicle's driving stability.

[0013] In addition, the first set time is shorter than the second set time. This means that when the actual positioning accuracy decreases, the vehicle's positioning status can be downgraded more quickly, avoiding the continued activation of high-risk functions (such as driver assistance mode) and ensuring driving safety. When the actual positioning accuracy increases, the vehicle's positioning status needs to be upgraded for a longer time, which can effectively reduce the misjudgment rate of high-precision status and prevent misjudgment as high-precision status from causing premature activation of high-risk functions (such as driver assistance mode), thus also ensuring driving safety.

[0014] In some possible implementations, the target positioning state is determined in the following manner: determining a mapping relationship between candidate intervals and candidate positioning states; wherein the upper and lower limits of the candidate intervals are associated with the target positioning accuracy value; determining the target interval in which the actual positioning accuracy value is located from each of the candidate intervals; and determining the candidate positioning state corresponding to the target interval based on the mapping relationship, as the target positioning state.

[0015] Therefore, we can take into account the dynamic determination of each candidate interval based on the target positioning accuracy value, and construct the mapping relationship between the candidate interval and the candidate positioning state. The candidate positioning states mapped by different candidate intervals may be different, and we can take into account the mapping relationship and determine the target positioning state.

[0016] In some possible implementations, the actual positioning accuracy value is characterized by a lateral positioning error value and a longitudinal positioning error value, and the target positioning accuracy value is characterized by a target positioning error value. The target positioning status is determined by at least one of the following: In response to the fact that both the lateral positioning error value and the longitudinal positioning error value are less than or equal to the target positioning error value, the target positioning state is determined to be the first positioning state; In response to the lateral positioning error value being less than or equal to the target positioning error value, and the longitudinal positioning error value being greater than the target positioning error value, the target positioning state is determined to be a second positioning state; In response to the lateral positioning error value being greater than the target positioning error value and less than or equal to M times the target positioning error value, and the longitudinal positioning error value being less than or equal to M times the target positioning error value, the target positioning state is determined to be a third positioning state; where M>1; In response to the lateral positioning error value being greater than or equal to the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state; or, in response to the lateral positioning error value being greater than the target positioning error value and less than or equal to the target positioning error value of M times, and the longitudinal positioning error value being greater than the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state.

[0017] Therefore, the target positioning status can be determined by considering the relationship between the vehicle's lateral positioning error value, longitudinal positioning error value and the target positioning error value.

[0018] In some possible implementations, the scenario category of the target driving scenario includes any one of highway, city, tunnel, and viaduct. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in a high-speed scenario include the first positioning state; The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in urban scenarios include the first positioning state and the third positioning state. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in tunnel or viaduct scenarios include the first positioning state, the second positioning state, and the third positioning state.

[0019] Therefore, the candidate positioning states for enabling assisted driving mode differ depending on whether the vehicle is on a highway, in a city, in a tunnel, or on an overpass. For example, in a highway scenario, the candidate positioning states for enabling assisted driving mode include a high-precision state; in a city scenario, the candidate positioning states for enabling assisted driving mode include both high-precision and low-precision states; and in a tunnel or overpass scenario, the candidate positioning states for enabling assisted driving mode include a high-precision state, a low-precision state, and a longitudinally restricted state.

[0020] In some possible implementations, controlling the vehicle based on either the target positioning state or the current positioning state includes: determining candidate positioning states in which the vehicle is allowed to activate the assisted driving mode under the target driving scenario; controlling the vehicle to exit the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario not including the target positioning state; and controlling the vehicle to activate or maintain the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario including the target positioning state.

[0021] Therefore, it is possible to determine the candidate positioning states in which the vehicle is allowed to activate the assisted driving mode under the target driving scenario, and to determine whether the candidate positioning states corresponding to the target driving scenario include any positioning state, so as to control the exit, maintenance or activation of the assisted driving mode, thereby achieving precise control of the assisted driving mode and optimizing the driving experience.

[0022] Furthermore, this disclosure can accurately determine the vehicle's positioning status, thereby preventing abnormal exits of the assisted driving mode, improving the availability and retention rate of the assisted driving mode, and optimizing the driving experience. For example, it can avoid the problem of frequent positioning failures caused by temporary signal obstruction (such as under bridges or trees) in high-speed scenarios, which leads to abnormal exits of the assisted driving mode. In addition, it can also avoid the problem of premature degradation of the positioning status in areas such as tunnels and overpasses due to excessive pursuit of high precision, such as positioning errors ≤15 cm, which leads to the inability to maintain the assisted driving mode. It can significantly improve the availability and retention rate of the assisted driving mode in high-speed and urban scenarios.

[0023] In some possible implementations, after controlling the vehicle to exit the assisted driving mode, the method further includes: generating first instruction information and visually displaying and / or voice-broadcasting the first instruction information; Wherein, the first indication information is used to indicate at least one of the following: The vehicle has exited the assisted driving mode; The driver is prompted to manually drive the vehicle; The system indicates that the vehicle's location status is abnormal.

[0024] Therefore, after the vehicle exits the assisted driving mode, a first instruction message can be generated and displayed visually and / or broadcast via voice. That is, the driver is informed through visual display and / or voice display that the vehicle has exited the assisted driving mode, prompted to manually drive the vehicle, or prompted to the driver that the vehicle's positioning status is abnormal, without requiring manual operation by the user, thus optimizing the driving experience.

[0025] In some possible implementations, after controlling the vehicle to start or maintain the assisted driving mode, the method further includes: generating second instruction information and visually displaying and / or voice-broadcasting the second instruction information; The second indication information is used to indicate at least one of the following: The vehicle has been started or is maintaining an assisted driving mode; The system indicates that the vehicle's location status is normal.

[0026] Therefore, after controlling the vehicle to start or maintain the assisted driving mode, a second instruction message can be generated and displayed visually and / or broadcast by voice. That is, the driver is informed through visual display and / or voice display that the vehicle has started or maintained the assisted driving mode, or that the vehicle's positioning status is not abnormal, at least one of these can be indicated, without requiring manual operation by the user, thus optimizing the driving experience.

[0027] In some possible implementations, collecting scene information of the vehicle in the target driving scenario includes: determining scene information of the predicted driving scenario in which the vehicle will be in a future time period, as the scene information of the target driving scenario; or, determining scene information of the actual driving scenario in which the vehicle is in the current time period, as the scene information of the target driving scenario.

[0028] Therefore, the scenario information of the predicted driving scenario in which the vehicle will be located in the future can be determined as the scenario information of the target driving scenario. Thus, before the vehicle enters the area corresponding to the predicted driving scenario, the vehicle's positioning status can be determined in advance based on the scenario information of the predicted driving scenario, and the vehicle's positioning status can be switched on demand. Compared with the real-time positioning status determination and switching after the vehicle enters the area corresponding to the predicted driving scenario, this solution can determine the positioning status and switch the status before the vehicle enters the area corresponding to the predicted driving scenario, which can achieve a smooth transition of the vehicle's positioning status and avoid sudden changes in the vehicle's positioning status.

[0029] In addition, it can predict potential location failures and respond to them before the vehicle enters the area corresponding to the predicted driving scenario, such as by exiting the assisted driving mode. This can significantly reduce response latency and extend the warning time for location failures, giving the driver ample time to deal with potential location failures, such as by manually driving the vehicle, thus optimizing the driving experience.

[0030] Alternatively, the scene information of the actual driving scenario in which the vehicle is currently in the current time period can be determined as the scene information of the target driving scenario.

[0031] In some possible implementations, before determining the scene information of the predicted driving scenario in which the vehicle will be in a future time period as the scene information of the target driving scenario, the method further includes at least one of the following: The predicted driving scenario is determined to be a preset driving scenario; The time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a third preset time. The distance required for the vehicle to enter the area corresponding to the predicted driving scenario is determined to be less than a set distance.

[0032] Therefore, when the predicted driving scenario is a preset driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in a future time period can be determined as the scenario information of the target driving scenario.

[0033] And / or, when the time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than the third set time, that is, when the vehicle is about to enter the area corresponding to the predicted driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in the future time period is determined as the scenario information of the target driving scenario.

[0034] And / or, when the driving distance required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a set distance, that is, when the vehicle is close to the area corresponding to the predicted driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in the future time period is determined as the scenario information of the target driving scenario.

[0035] According to a second aspect of the present disclosure, a vehicle control device is provided, comprising: a first determining module configured to collect scene information of a vehicle in a target driving scenario, and determine a target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario; a second determining module configured to determine whether to switch the positioning state of the vehicle based on the magnitude relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle; and a control module configured to control the vehicle based on the target positioning state of the vehicle after the switching, in response to the switching of the positioning state of the vehicle; or to control the vehicle based on the current positioning state of the vehicle if the positioning state of the vehicle has not been switched.

[0036] In some possible implementations, the first determining module is further configured to: process the scene information of the target driving scenario using a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; wherein, different driving scenarios correspond to different preset algorithms; the scene information includes at least one of scene category, road curvature, vehicle speed, and perception confidence.

[0037] In some possible implementations, the scene information includes scene information in multiple dimensions, and the preset algorithm includes the weights of the scene information in each dimension; in the preset algorithms corresponding to different driving scenarios, the weights of the same category are different; the first determining module is further configured to: perform a weighted summation of the scene information in each dimension of the target driving scenario based on the weights of the scene information in each dimension, so as to determine the target positioning accuracy value.

[0038] In some possible implementations, the second determining module is further configured to: determine the target positioning state based on the magnitude relationship between the target positioning accuracy value and the actual positioning accuracy value; in response to the positioning accuracy represented by the current positioning state being higher than the positioning accuracy represented by the target positioning state, and the vehicle satisfying the target positioning state for a duration of a first set duration, switch the vehicle's positioning state from the current positioning state to the target positioning state; or, in response to the positioning accuracy represented by the current positioning state being lower than the positioning accuracy represented by the target positioning state, and the vehicle satisfying the target positioning state for a duration of a second set duration, switch the vehicle's positioning state from the current positioning state to the target positioning state; wherein the first set duration is less than the second set duration.

[0039] In some possible implementations, the second determining module is further configured to: determine a mapping relationship between candidate intervals and candidate positioning states; wherein the upper and lower limits of the candidate intervals are associated with the target positioning accuracy value; determine the target interval in which the actual positioning accuracy value is located from each of the candidate intervals; and determine the candidate positioning state corresponding to the target interval based on the mapping relationship, as the target positioning state.

[0040] In some possible implementations, the actual positioning accuracy value is characterized by a lateral positioning error value and a longitudinal positioning error value; the target positioning accuracy value is characterized by a target positioning error value. The second determining module is further configured to perform at least one of the following: In response to the fact that both the lateral positioning error value and the longitudinal positioning error value are less than or equal to the target positioning error value, the target positioning state is determined to be the first positioning state; In response to the lateral positioning error value being less than or equal to the target positioning error value, and the longitudinal positioning error value being greater than the target positioning error value, the target positioning state is determined to be a second positioning state; In response to the lateral positioning error value being greater than the target positioning error value and less than or equal to M times the target positioning error value, and the longitudinal positioning error value being less than or equal to M times the target positioning error value, the target positioning state is determined to be a third positioning state; where M>1; In response to the lateral positioning error value being greater than or equal to the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state; or, in response to the lateral positioning error value being greater than the target positioning error value and less than or equal to the target positioning error value of M times, and the longitudinal positioning error value being greater than the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state.

[0041] In some possible implementations, the scenario category of the target driving scenario includes any one of highway, city, tunnel, and viaduct. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in a high-speed scenario include the first positioning state; The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in urban scenarios include the first positioning state and the third positioning state. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in tunnel or viaduct scenarios include the first positioning state, the second positioning state, and the third positioning state.

[0042] In some possible implementations, the control module is further configured to: determine candidate positioning states in which the vehicle is allowed to activate the assisted driving mode under the target driving scenario; control the vehicle to exit the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario not including any of the positioning states; and control the vehicle to start or maintain the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario including any of the positioning states.

[0043] In some possible implementations, after the vehicle is controlled to exit the assisted driving mode, the control module is further configured to: generate first instruction information and visualize and / or voice broadcast the first instruction information; Wherein, the first indication information is used to indicate at least one of the following: The vehicle has exited the assisted driving mode; The driver is prompted to manually drive the vehicle; The system indicates that the vehicle's location status is abnormal.

[0044] In some possible implementations, after controlling the vehicle to start or maintain the assisted driving mode, the control module is further configured to: generate second instruction information and visually display and / or voice broadcast the second instruction information; The second indication information is used to indicate at least one of the following: The vehicle has been started or is maintaining an assisted driving mode; The system indicates that the vehicle's location status is normal.

[0045] In some possible implementations, the first determining module is further configured to: determine the scene information of the predicted driving scenario in which the vehicle will be in a future time period, as the scene information of the target driving scenario; or, determine the scene information of the actual driving scenario in which the vehicle is in the current time period, as the scene information of the target driving scenario.

[0046] In some possible implementations, before determining the scene information of the predicted driving scenario in which the vehicle will be in a future time period as the scene information of the target driving scenario, the first determining module is further configured to perform at least one of the following: The predicted driving scenario is determined to be a preset driving scenario; The time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a third preset time. The distance required for the vehicle to enter the area corresponding to the predicted driving scenario is determined to be less than a set distance.

[0047] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the vehicle control method described in the first aspect of the present disclosure.

[0048] According to a fourth aspect of the present disclosure, a vehicle is provided, including a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the vehicle control method described in the first aspect of the present disclosure.

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

[0050] According to a sixth aspect of the present disclosure, a chip is provided, the chip including an interface circuit and a processing circuit coupled to each other, the interface circuit being used to input or output signals, and the processing circuit being configured to implement the steps of the vehicle control method described in the first aspect of the present disclosure.

[0051] According to a seventh aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the vehicle control method described in the first aspect of the present disclosure.

[0052] The technical solution provided by the embodiments of this disclosure brings at least the following beneficial effects: It collects scene information of the vehicle in the target driving scenario, determines the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information, determines whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, and controls the vehicle based on the target positioning state after the switch in response to the switch in response to the vehicle's positioning state not being switched, or controls the vehicle based on the vehicle's current positioning state in response to the vehicle's positioning state not being switched. Therefore, by taking into account the scene information of the vehicle in the target driving scenario and dynamically evaluating the target positioning accuracy value of the vehicle in the target driving scenario (used to indicate the positioning accuracy requirement), it is possible to consider the differentiated positioning accuracy requirements of different driving scenarios and achieve accurate matching between the positioning accuracy requirement and the target driving scenario.

[0053] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0054] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0056] Figure 1 This is a schematic flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0057] Figure 2 This is a schematic flowchart illustrating a vehicle control method according to another exemplary embodiment.

[0058] Figure 3 This is a schematic flowchart illustrating a vehicle control method according to another exemplary embodiment.

[0059] Figure 4 This is a schematic flowchart illustrating a vehicle control method according to another exemplary embodiment.

[0060] Figure 5 This is a schematic diagram of the structure of a vehicle control device according to an exemplary embodiment.

[0061] Figure 6 This is a schematic diagram of the structure of a vehicle according to an exemplary embodiment.

[0062] Figure 7 This is a schematic diagram of the structure of a chip according to an exemplary embodiment. Detailed Implementation

[0063] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0064] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0065] It should also be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution comply with relevant laws and regulations and do not violate public order and good morals. All data processed in this disclosure is explicitly authorized by users or relevant parties and has been de-identified or anonymized before collection and use, containing no personally identifiable information or user privacy content; all data is used solely for the purpose of assisted driving of vehicles, ensuring that data security and user privacy rights are fully protected while achieving the technical effects.

[0066] The vehicle control method, apparatus, electronic device, vehicle, chip, and storage medium of the present disclosure are described below with reference to the accompanying drawings.

[0067] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment. The vehicle control method of this disclosure includes the following steps.

[0068] S101: Collect scene information of the vehicle in the target driving scenario, and determine the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario.

[0069] It should be noted that the vehicle control method of this disclosure is executed by an electronic device, such as a vehicle, a server, or a chip. The vehicle includes an in-vehicle terminal and an in-vehicle controller, and the server includes a cloud server and a distributed server. The vehicle control method of this disclosure can be executed by the vehicle control device of this disclosure, which can be configured in any electronic device to execute the vehicle control method of this disclosure.

[0070] The target positioning accuracy value indicates the vehicle's positioning accuracy requirements in a target driving scenario. For example, the target positioning accuracy value can be characterized by a target positioning error value (e.g., 15 centimeters). Understandably, the smaller the target positioning error value, the higher the target positioning accuracy, meaning the higher the positioning accuracy requirements of the vehicle in the target driving scenario. Conversely, the larger the target positioning error value, the lower the target positioning accuracy, meaning the lower the positioning accuracy requirements of the vehicle in the target driving scenario.

[0071] The target driving scenarios are not limited in many ways, such as highways, cities, rural areas, mountainous areas, tunnels, overpasses, schools, hospitals, construction sections, daytime, nighttime, ramp merging, parking, switching between main and auxiliary roads, and following other vehicles in congested traffic.

[0072] The scenario information is not overly restricted, but includes scenario categories (such as highways, cities, tunnels, overpasses, etc.), road geometry (such as road shape, curvature, slope, width, etc.), lane topology (such as number of lanes, lane connections, driving directions, intersections, etc.), traffic rules (such as speed limits, one-way streets, etc.), traffic signs and markings (such as stop lines, pedestrian crossings, etc.), infrastructure layout (such as guardrail locations, street light locations, etc.), special area markings (such as school zones, hospital zones, construction zones, etc.), obstacle information (such as the distance between vehicles and obstacles, obstacle types, etc.), vehicle operating parameters (such as vehicle speed, acceleration, heading angle, etc.), traffic flow status (such as traffic density, average vehicle speed, congestion level, etc.), traffic light status (such as current traffic light color, countdown, etc.), weather and lighting conditions (such as rain, snow, fog, night, strong light, backlight), and perception confidence.

[0073] Perception confidence refers to the degree of credibility of a vehicle's perception results, including visual confidence, radar confidence, and ultrasonic confidence.

[0074] Optionally, the scene information includes at least one of scene category, road curvature, vehicle speed, and perception confidence.

[0075] The target positioning accuracy value is characterized by the target positioning error value, which satisfies at least one of the following: Different scene categories have different target localization error values; The target positioning error is negatively correlated with the road curvature. The target positioning error is positively correlated with vehicle speed; The target localization error is negatively correlated with the perceived confidence level.

[0076] Therefore, different scene categories have different target positioning error values. For example, the target positioning error value of a vehicle in a highway scene is greater than that of a vehicle in an urban scene, so that the target positioning error value is accurately matched with the scene category.

[0077] The target positioning error is negatively correlated with road curvature. This means that a greater road curvature indicates a more winding road, and even a small positioning error can cause a vehicle to deviate from its lane. In other words, the lower the vehicle's tolerance for positioning errors, the smaller the target positioning error, which helps ensure safe driving. Conversely, a smaller road curvature indicates a straighter road, and a larger positioning error has little impact on normal vehicle operation. Therefore, the higher the vehicle's tolerance for positioning errors, the larger the target positioning error.

[0078] The target positioning error value is positively correlated with the vehicle speed, which makes the target positioning error value and the vehicle speed accurately matched.

[0079] The target positioning error value is negatively correlated with the perceived confidence level, which makes the target positioning error value and the perceived confidence level accurately match.

[0080] For example, information such as road curvature, number of lanes, and speed limit can be determined from high-precision maps; information such as visual confidence and distance between the vehicle and obstacles can be determined from the vehicle's perception system; and information such as vehicle speed and steering angle can be determined from the vehicle bus. All of the above-determined information is then input into a decision tree model to determine the scenario category of the target driving scenario.

[0081] S102, based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, determine whether to switch the vehicle's positioning status.

[0082] It is understandable that the positioning accuracy requirements may differ in different scenarios, and therefore the positioning status corresponding to the same positioning accuracy may be different in different driving scenarios.

[0083] The vehicle's positioning status is not subject to many restrictions, such as including low-precision status, high-precision status, longitudinally restricted status, and unavailable status (also called positioning failure status). For example, at the initial stage of the positioning system startup, the vehicle's positioning status is unspecified, and during the positioning system calibration period, the vehicle's positioning status is initialized.

[0084] Optionally, the actual positioning accuracy value is characterized by the actual positioning error value, and the method further includes determining the actual positioning error value. It is understood that the smaller the actual positioning error value, the greater the actual positioning accuracy.

[0085] Optionally, the determination of whether to switch the vehicle's positioning state is based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, including determining the target positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, and determining whether to switch the vehicle's positioning state based on the target positioning state and the vehicle's current positioning state.

[0086] For example, the target positioning accuracy value is represented by the target positioning error value, and the actual positioning accuracy value is represented by the actual positioning error value. Based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, the target positioning status is determined, including determining the target positioning status as a valid positioning status when the actual positioning error value is less than or equal to the target positioning error value, and determining the target positioning status as a failed positioning status when the actual positioning error value is greater than the target positioning error value.

[0087] For example, based on the target location status and the vehicle's current location status, determine whether to switch the vehicle's location status, including maintaining the vehicle's current location status in response to the target location status being consistent with the current location status; or switching the vehicle's location status from the current location status to the target location status in response to the target location status being inconsistent with the current location status.

[0088] S103, in response to a change in the vehicle's positioning status, controls the vehicle based on the target positioning status after the change.

[0089] S104, in response to the vehicle's positioning status not changing, controls the vehicle based on the vehicle's current positioning status.

[0090] In related technologies, the vehicle's positioning status is determined by a preset fixed threshold. For example, if the actual positioning error is greater than the fixed threshold, the vehicle is determined to be in a certain preset positioning status. This ignores the driving scenario, causing the vehicle's positioning status to be unable to adapt to diverse driving scenarios, thus affecting driving safety and experience.

[0091] In this disclosure, the target positioning accuracy value of the vehicle in the target driving scenario can be dynamically evaluated (to indicate the positioning accuracy requirement) by taking into account the scenario information of the vehicle in the target driving scenario. This means that the different positioning accuracy requirements of different driving scenarios can be taken into account, and the positioning accuracy requirement can be accurately matched with the target driving scenario.

[0092] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0093] Optionally, the vehicle is controlled based on either the target location state or the current location state. This includes determining candidate location states where the vehicle is permitted to activate the assisted driving mode in the target driving scenario; controlling the vehicle to exit the assisted driving mode if the candidate location states corresponding to the target driving scenario do not include any of the specified location states; and controlling the vehicle to activate or maintain the assisted driving mode if the candidate location states corresponding to the target driving scenario include any of the specified location states. Thus, it is possible to determine candidate location states where the vehicle is permitted to activate the assisted driving mode in the target driving scenario, and whether the candidate location states corresponding to the target driving scenario include any of the specified location states, thereby controlling the exit, maintenance, or activation of the assisted driving mode. This enables precise control of the assisted driving mode and optimizes the driving experience.

[0094] Furthermore, this disclosure can accurately determine the vehicle's positioning status, thereby preventing abnormal exits of the assisted driving mode, improving the availability and retention rate of the assisted driving mode, and optimizing the driving experience. For example, it can avoid the problem of frequent positioning failures caused by temporary signal obstruction (such as under bridges or trees) in high-speed scenarios, which leads to abnormal exits of the assisted driving mode. In addition, it can also avoid the problem of premature degradation of the positioning status in areas such as tunnels and overpasses due to excessive pursuit of high precision, such as positioning errors ≤15 cm, which leads to the inability to maintain the assisted driving mode. It can significantly improve the availability and retention rate of the assisted driving mode in high-speed and urban scenarios.

[0095] Understandably, if the candidate location status corresponding to the target driving scenario does not include any location status, it indicates that in this driving scenario, any location status does not allow the activation of the assisted driving mode, and the vehicle will be controlled to exit the assisted driving mode. If the candidate location status corresponding to the target driving scenario includes any location status, it indicates that in this driving scenario, any location status allows the activation of the assisted driving mode, and the vehicle will be controlled to activate or maintain the assisted driving mode.

[0096] The candidate location states that allow the activation of assisted driving mode may differ in different driving scenarios. For example, there is a correspondence between the scenario category of a driving scenario and the candidate location states that allow the activation of assisted driving mode. The relevant content regarding the candidate location states corresponding to driving scenarios can be found in the following embodiments, and will not be repeated here.

[0097] Optionally, after controlling the vehicle to exit the assisted driving mode, the system may also generate first instruction information and visualize and / or voice-broadcast the first instruction information.

[0098] The first indication information is used to indicate at least one of the following: The vehicle has exited driver assistance mode; The driver is prompted to manually drive the vehicle; The system indicates that the vehicle's location status is abnormal. Therefore, after the vehicle exits the assisted driving mode, a first instruction message can be generated and displayed visually and / or broadcast via voice. That is, the driver is informed through visual display and / or voice display that the vehicle has exited the assisted driving mode, prompted to manually drive the vehicle, or prompted to the driver that the vehicle's positioning status is abnormal, without requiring manual operation by the user, thus optimizing the driving experience.

[0099] Optionally, after controlling the vehicle to start or maintain the assisted driving mode, the system may also generate second instruction information and display and / or voice broadcast the second instruction information.

[0100] The second indication information is used to indicate at least one of the following: The vehicle has been started or is in assisted driving mode; The system indicates that the vehicle's location status is normal.

[0101] Therefore, after controlling the vehicle to start or maintain the assisted driving mode, a second instruction message can be generated and displayed visually and / or broadcast by voice. That is, the driver is informed of at least one of the following through visual and / or voice display: the vehicle has started or maintained the assisted driving mode, or the vehicle's positioning status is not abnormal. No manual operation is required from the user, thus optimizing the driving experience.

[0102] Optionally, vehicle control can be performed based on either the target location state or the current location state, including controlling the vehicle based on either location state and the scenario category of the target driving scenario. This allows for comprehensive consideration of both the location state and the scenario category of the target driving scenario when controlling the vehicle, which helps improve the accuracy of vehicle control and optimize the driving experience.

[0103] Optionally, the vehicle is controlled based on any given location state and the scenario category of the target driving scenario. This includes determining a target driving strategy corresponding to any given location state and the scenario category of the target driving scenario, and controlling the vehicle based on the target driving strategy. It is understood that the correspondence between candidate location states, scenario categories, and candidate driving strategies can be predetermined, and the candidate driving strategies corresponding to different candidate location states and different scenario categories may be different.

[0104] It should be noted that this disclosure does not limit the execution sequence of steps S101-S104. For example, steps S101-S103 can be implemented as independent embodiments, and steps S101-S102 and S104 can be implemented as independent embodiments.

[0105] The vehicle control method provided in the embodiments of this disclosure collects scene information of the vehicle in a target driving scenario, determines the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information, and determines whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value. In response to the switching of the vehicle's positioning state, the vehicle is controlled based on the target positioning state after the switch; or, in response to the vehicle's positioning state not switching, the vehicle is controlled based on the vehicle's current positioning state. Therefore, by taking into account the scene information of the vehicle in the target driving scenario and dynamically evaluating the target positioning accuracy value of the vehicle in the target driving scenario (used to indicate the positioning accuracy requirement), the method can take into account the differentiated positioning accuracy requirements of different driving scenarios and achieve precise matching between the positioning accuracy requirement and the target driving scenario.

[0106] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0107] Figure 2 This is a flowchart illustrating a vehicle control method according to another exemplary embodiment, such as... Figure 2 As shown, the vehicle control method of this disclosure includes the following steps.

[0108] S201, collects scene information of the vehicle in the target driving scenario.

[0109] The details of step S201 can be found in the above embodiments and will not be repeated here.

[0110] S202, using a preset algorithm corresponding to the target driving scenario, processes the scene information of the target driving scenario to determine the target positioning accuracy value.

[0111] In this embodiment, different preset algorithms are used for different driving scenarios.

[0112] Optionally, the scene information includes at least one of scene category, road curvature, vehicle speed, and perception confidence. Therefore, a preset algorithm corresponding to the target driving scene can be used to process at least one of the scene category, road curvature, vehicle speed, and perception confidence of the target driving scene to dynamically evaluate the target positioning accuracy of the vehicle in the target driving scene.

[0113] Optionally, the scene information includes scene information in multiple dimensions, and the preset algorithm includes the weights of scene information in each dimension; in the preset algorithms corresponding to different driving scenarios, the weights of the same category are different.

[0114] By using a pre-defined algorithm corresponding to the target driving scenario, the scene information of the target driving scenario is processed to determine the target positioning accuracy value. This includes weighting the scene information based on each dimension and then summing the scene information from each dimension of the target driving scenario to determine the target positioning accuracy value. Therefore, the weights of the scene information in each dimension can be taken into account when performing a weighted summation of the scene information from each dimension of the target driving scenario to determine the target positioning accuracy value.

[0115] For example, the target positioning accuracy value is characterized by the target positioning error value. The target positioning error value can be determined using the following formula:

[0116] in, The target positioning error value. For road curvature, For vehicle speed, To perceive confidence level.

[0117] Let the road curvature be a function of the road itself; the greater the road curvature, the better. . It is a function with vehicle speed as the independent variable; the higher the vehicle speed, the better. . This is a function with perceived confidence as the independent variable; the higher the perceived confidence, the better. .

[0118] As the weight of road curvature, As a weight for vehicle speed, Weights for perceived confidence.

[0119] To set coefficients. For example, in high-speed scenarios. =15cm, in urban settings =30cm.

[0120] The following example illustrates the process of determining the target positioning error value.

[0121] In the first scenario, when a vehicle is traveling on a straight section of a highway with a curvature radius > 200m and a speed of 100km / h, the visual confidence level is 0.9.

[0122]

[0123] Therefore, in this driving scenario The tolerance can be relaxed to 16cm to avoid the positioning status being downgraded due to brief signal fluctuations, such as switching from high-precision to low-precision status.

[0124] In the second scenario, when the vehicle is driving on a city curve with a radius of curvature of 50m and a speed of 30km / h, the visual confidence level is 0.5.

[0125]

[0126] Therefore, in this driving scenario Strict requirements will remain in place to ensure safety on curves.

[0127] S203, based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, determine whether to switch the vehicle's positioning status.

[0128] S204, in response to a change in the vehicle's positioning status, controls the vehicle based on the target positioning status after the change.

[0129] S205, in response to the vehicle's location status not changing, controls the vehicle based on the vehicle's current location status.

[0130] The details of steps S203-S205 can be found in the above embodiments and will not be repeated here.

[0131] It should be noted that this disclosure does not limit the execution sequence of steps S201-S205. For example, steps S201-S204 can be implemented as an independent embodiment, and steps S201-S203 and S205 can be implemented as independent embodiments.

[0132] The vehicle control method provided in the embodiments of this disclosure processes scene information of the target driving scenario using a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; wherein, different preset algorithms correspond to different driving scenarios. Therefore, considering the differentiated positioning accuracy requirements of different driving scenarios, setting preset algorithms for different driving scenarios helps improve the accuracy of the target positioning accuracy value, thereby achieving a precise match between the positioning accuracy requirement and the target driving scenario.

[0133] Figure 3 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 3 As shown, the vehicle control method of this disclosure includes the following steps.

[0134] S301: Collect scene information of the vehicle in the target driving scenario, and determine the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario.

[0135] The details of step S301 can be found in the above embodiments and will not be repeated here.

[0136] S302, determine the target positioning status based on the relationship between the target positioning accuracy value and the actual positioning accuracy value.

[0137] Optionally, the target positioning state is determined in the following way: determining the mapping relationship between candidate intervals and candidate positioning states; wherein the upper and lower limits of the candidate intervals are associated with the target positioning accuracy value; determining the target interval where the actual positioning accuracy value is located from each candidate interval; and determining the candidate positioning state corresponding to the target interval based on the mapping relationship, which is then used as the target positioning state. Thus, the dynamic determination of each candidate interval based on the target positioning accuracy value and the construction of the mapping relationship between candidate intervals and candidate positioning states can be considered. Different candidate intervals may map to different candidate positioning states, and the mapping relationship and the determination of the target positioning state are taken into account.

[0138] Optionally, the actual positioning accuracy value is characterized by the lateral positioning error value and the longitudinal positioning error value; the target positioning accuracy value is characterized by the target positioning error value. The target location status is determined by at least one of the following: When both the lateral positioning error value and the longitudinal positioning error value are less than or equal to the target positioning error value, the target positioning state is determined to be the first positioning state. In response to the lateral positioning error value being less than or equal to the target positioning error value, and the longitudinal positioning error value being greater than the target positioning error value, the target positioning state is determined to be the second positioning state; In response to a lateral positioning error value being greater than the target positioning error value but less than or equal to M times the target positioning error value, and a longitudinal positioning error value being less than or equal to M times the target positioning error value, the target positioning state is determined to be the third positioning state; where M>1; In response to a lateral positioning error value being greater than or equal to M times the target positioning error value, the target positioning state is determined to be the fourth positioning state; or, in response to a lateral positioning error value being greater than the target positioning error value and less than or equal to M times the target positioning error value, and a longitudinal positioning error value being greater than M times the target positioning error value, the target positioning state is determined to be the fourth positioning state.

[0139] Therefore, the target positioning status can be determined by considering the relationship between the vehicle's lateral positioning error value, longitudinal positioning error value and the target positioning error value.

[0140] For example, the first positioning state represents the highest positioning accuracy, while the fourth positioning state represents the lowest positioning accuracy.

[0141] For example, the first positioning state is a high-precision state, the second positioning state is a longitudinally restricted state, the third positioning state is a low-precision state, and the fourth positioning state is an unavailable state.

[0142] For example, when M=2, the mapping relationship between the lateral positioning error value, the longitudinal positioning error value and the target positioning status is shown in Table 1.

[0143] Table 1. Mapping relationship between lateral positioning error value, longitudinal positioning error value and target positioning status.

[0144] Optionally, the scenario category of the target driving scenario includes any one of highway, city, tunnel, and viaduct. Candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in high-speed scenarios include the first positioning state; The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in urban scenarios include the first positioning state and the third positioning state. Candidate positioning states for enabling assisted driving mode in tunnel or viaduct scenarios include the first positioning state, the second positioning state, and the third positioning state.

[0145] Therefore, the candidate positioning states for enabling assisted driving mode differ depending on whether the vehicle is on a highway, in a city, in a tunnel, or on an overpass. For example, in a highway scenario, the candidate positioning states for enabling assisted driving mode include a high-precision state; in a city scenario, the candidate positioning states for enabling assisted driving mode include both high-precision and low-precision states; and in a tunnel or overpass scenario, the candidate positioning states for enabling assisted driving mode include a high-precision state, a low-precision state, and a longitudinally restricted state.

[0146] Optionally, the method further includes determining the duration for which the vehicle's assisted driving mode is enabled in response to the target driving scenario being a tunnel or overpass and the target positioning state being a second positioning state; and controlling the vehicle to exit the assisted driving mode in response to the duration reaching a fourth preset duration. Thus, in a tunnel or overpass scenario and when the target positioning state is a second positioning state, the duration for which the vehicle is allowed to enable the assisted driving mode is no greater than the fourth preset duration; for example, the vehicle can tolerate a certain duration of longitudinal restriction.

[0147] It should be noted that there are no strict restrictions on the duration of the fourth setting; for example, it can be 5 seconds.

[0148] S303, in response to the fact that the positioning accuracy represented by the current positioning state is higher than the positioning accuracy represented by the target positioning state, and the duration for which the vehicle meets the target positioning state reaches a first set duration, the vehicle's positioning state is switched from the current positioning state to the target positioning state.

[0149] S304, in response to the fact that the positioning accuracy represented by the current positioning state is lower than the positioning accuracy represented by the target positioning state, and the duration for which the vehicle meets the target positioning state reaches a second set duration, the positioning state of the vehicle is switched from the current positioning state to the target positioning state.

[0150] In this embodiment, the first set duration is less than the second set duration.

[0151] For example, the method further includes switching the vehicle's positioning state from a first positioning state to a third positioning state in response to a first set duration for which the lateral positioning error value is greater than the target positioning error value and less than or equal to N times the target positioning error value, and the longitudinal positioning error value is less than or equal to N times the target positioning error value for a first set duration; wherein, 1 <N<M; In response to the duration for which both the lateral positioning error value and the longitudinal positioning error value are less than or equal to the target positioning error value, the target positioning state is switched from the third positioning state to the first positioning state.

[0152] It should be noted that there are no strict limitations on the first and second set durations. For example, the first set duration can be 5 seconds and the second set duration can be 10 seconds.

[0153] S305 responds to a change in the vehicle's positioning status and controls the vehicle based on the target positioning status after the change.

[0154] The details of step S305 can be found in the above embodiments and will not be repeated here.

[0155] It should be noted that this disclosure does not limit the execution sequence of steps S301-S305. For example, steps S301-S303 and S305 can be implemented as independent embodiments, and steps S301-S302 and S304-S305 can be implemented as independent embodiments.

[0156] The vehicle control method provided in the embodiments of this disclosure can switch the vehicle's positioning state from the current positioning state to the target positioning state when the positioning accuracy represented by the current positioning state is higher than that represented by the target positioning state, and the duration for which the vehicle meets the target positioning state reaches a first set duration. That is, the vehicle's positioning state is downgraded. When the positioning accuracy represented by the current positioning state is lower than that represented by the target positioning state, and the duration for which the vehicle meets the target positioning state reaches a second set duration, the vehicle's positioning state is switched from the current positioning state to the target positioning state. That is, the vehicle is restored from the downgraded state. This can avoid the problem of frequent switching of the vehicle's positioning state due to measurement noise or short-term fluctuations, and can improve the vehicle's driving stability.

[0157] In addition, the first set time is shorter than the second set time. This means that when the actual positioning accuracy decreases, the vehicle's positioning status can be downgraded more quickly, avoiding the continued activation of high-risk functions (such as driver assistance mode) and ensuring driving safety. When the actual positioning accuracy increases, the vehicle's positioning status needs to be upgraded for a longer time, which can effectively reduce the misjudgment rate of high-precision status and prevent misjudgment as high-precision status from causing premature activation of high-risk functions (such as driver assistance mode), thus also ensuring driving safety.

[0158] Figure 4 This is a flowchart illustrating a vehicle control method according to another exemplary embodiment, such as... Figure 4 As shown, the vehicle control method of this disclosure includes the following steps.

[0159] S401, determine the scene information of the predicted driving scenario in which the vehicle will be in the future time period, as the scene information of the target driving scenario.

[0160] S402, determine the scene information of the actual driving scenario in which the vehicle is located in the current time period, and use it as the scene information of the target driving scenario.

[0161] Optionally, determining the scenario information of the predicted driving scenario in which the vehicle will be in the future time period, before using the scenario information of the target driving scenario, includes at least one of the following: The predicted driving scenario is determined to be a preset driving scenario; The time required to determine that the vehicle enters the area corresponding to the predicted driving scenario is less than the third set time. The distance required for the vehicle to enter the area corresponding to the predicted driving scenario is determined to be less than the set distance.

[0162] Therefore, when the predicted driving scenario is a preset driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in a future time period can be determined as the scenario information of the target driving scenario.

[0163] And / or, when the time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than the third set time, that is, when the vehicle is about to enter the area corresponding to the predicted driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in the future time period is determined as the scenario information of the target driving scenario.

[0164] And / or, when the driving distance required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a set distance, that is, when the vehicle is close to the area corresponding to the predicted driving scenario, the scenario information of the predicted driving scenario in which the vehicle will be in the future time period is determined as the scenario information of the target driving scenario.

[0165] It should be noted that there are no excessive restrictions on the preset driving scenarios.

[0166] Optionally, the preset driving scenario category includes tunnels. Therefore, when the predicted driving scenario is a tunnel scenario, the vehicle's positioning status can be determined in advance based on the tunnel scenario information, and the vehicle's positioning status can be switched on demand. Compared to real-time positioning status determination and switching after the vehicle enters the corresponding area of ​​the tunnel scenario, this solution can determine the positioning status and switch the status before the vehicle enters the corresponding area of ​​the tunnel scenario, achieving a smooth transition of the vehicle's positioning status and avoiding sudden changes in the vehicle's positioning status.

[0167] It should be noted that there are no strict restrictions on the duration of the third setting, such as 3-5 seconds.

[0168] There are no strict limitations on the set distance, such as setting the distance to 20 meters.

[0169] S403, based on the scene information of the target driving scenario, determines the target positioning accuracy value of the vehicle in the target driving scenario.

[0170] S404, based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value, determines whether to switch the vehicle's positioning status.

[0171] S405 responds to a change in the vehicle's positioning status and controls the vehicle based on the target positioning status after the change.

[0172] S406, in response to the vehicle's location status not changing, controls the vehicle based on the vehicle's current location status.

[0173] The details of steps S403-S406 can be found in the above embodiments and will not be repeated here.

[0174] It should be noted that this disclosure does not limit the execution sequence of steps S401-S406. For example, steps S401 and S403-S405 can be implemented as independent embodiments, and steps S401, S403-S404 and S406 can be implemented as independent embodiments. For example, steps S402-S405 can be implemented as independent embodiments, and steps S402-S404 and S406 can be implemented as independent embodiments.

[0175] The vehicle control method provided in the embodiments of this disclosure can determine the scene information of the predicted driving scenario in which the vehicle will be located in the future time period, and use it as the scene information of the target driving scenario. Thus, before the vehicle enters the area corresponding to the predicted driving scenario, the vehicle's positioning status can be determined in advance based on the scene information of the predicted driving scenario, and the vehicle's positioning status can be switched on demand. Compared with the real-time positioning status determination and switching after the vehicle enters the area corresponding to the predicted driving scenario, this solution can determine the positioning status and switch the status before the vehicle enters the area corresponding to the predicted driving scenario, which can achieve a smooth transition of the vehicle's positioning status and avoid sudden changes in the vehicle's positioning status.

[0176] In addition, it can predict potential location failures and respond to them before the vehicle enters the area corresponding to the predicted driving scenario, such as by exiting the assisted driving mode. This can significantly reduce response latency and extend the warning time for location failures, giving the driver ample time to deal with potential location failures, such as by manually driving the vehicle, thus optimizing the driving experience.

[0177] Alternatively, the scene information of the actual driving scenario in which the vehicle is currently in the current time period can be determined as the scene information of the target driving scenario.

[0178] Figure 5 This is a schematic diagram illustrating the structure of a vehicle control device according to an exemplary embodiment. (Refer to...) Figure 5The vehicle control device 500 of this embodiment includes: a first determining module 501, a second determining module 502 and a control module 503.

[0179] The first determining module 501 is configured to collect scene information of the vehicle in the target driving scenario, and determine the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario. The second determining module 502 is configured to determine whether to switch the positioning state of the vehicle based on the relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle. The control module 503 is configured to control the vehicle based on the target positioning state after the vehicle's positioning state changes in response to a change in the vehicle's positioning state; or to control the vehicle based on the vehicle's current positioning state in response to no change in the vehicle's positioning state.

[0180] In some possible implementations, the first determining module 501 is further configured to: process the scene information of the target driving scenario using a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; wherein, different driving scenarios correspond to different preset algorithms; the scene information includes at least one of scene category, road curvature, vehicle speed, and perception confidence.

[0181] In some possible implementations, the scene information includes scene information of multiple dimensions, and the preset algorithm includes the weights of scene information of each dimension; in the preset algorithms corresponding to different driving scenarios, the weights of the same category are different; the first determining module 501 is further configured to: perform a weighted summation of the scene information of each dimension of the target driving scenario based on the weights of the scene information of each dimension, so as to determine the target positioning accuracy value.

[0182] In some possible implementations, the second determining module 502 is further configured to: determine the target positioning state based on the magnitude relationship between the target positioning accuracy value and the actual positioning accuracy value; in response to the positioning accuracy represented by the current positioning state being higher than the positioning accuracy represented by the target positioning state, and the duration of the vehicle satisfying the target positioning state reaching a first set duration, switch the vehicle's positioning state from the current positioning state to the target positioning state; or, in response to the positioning accuracy represented by the current positioning state being lower than the positioning accuracy represented by the target positioning state, and the duration of the vehicle satisfying the target positioning state reaching a second set duration, switch the vehicle's positioning state from the current positioning state to the target positioning state; wherein the first set duration is less than the second set duration.

[0183] In some possible implementations, the second determining module 502 is further configured to: determine a mapping relationship between candidate intervals and candidate positioning states; wherein the upper limit and lower limit of the candidate intervals are associated with the target positioning accuracy value; determine the target interval in which the actual positioning accuracy is located from each of the candidate intervals; and determine the candidate positioning state corresponding to the target interval based on the mapping relationship, as the target positioning state.

[0184] In some possible implementations, the second determining module 502 is further configured to perform at least one of the following: In response to the fact that both the lateral positioning error and the longitudinal positioning error of the vehicle are less than or equal to the target positioning accuracy value, the target positioning state is determined to be the first positioning state; In response to the lateral positioning error being less than or equal to the target positioning accuracy value, and the longitudinal positioning error being greater than the target positioning accuracy value, the target positioning state is determined to be a second positioning state; In response to the lateral positioning error being greater than the target positioning accuracy value and less than or equal to M times the target positioning accuracy value, and the longitudinal positioning error being less than or equal to M times the target positioning accuracy value, the target positioning state is determined to be a third positioning state; where M>1; In response to the lateral positioning error being greater than or equal to the target positioning accuracy value of M times, the target positioning state is determined to be the fourth positioning state; or, in response to the lateral positioning error being greater than the target positioning accuracy value and less than or equal to the target positioning accuracy value of M times, and the longitudinal positioning error being greater than the target positioning accuracy value of M times, the target positioning state is determined to be the fourth positioning state.

[0185] In some possible implementations, the scenario category of the target driving scenario includes any one of highway, city, tunnel, and viaduct. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in a high-speed scenario include the first positioning state; The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in urban scenarios include the first positioning state and the third positioning state. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in tunnel or viaduct scenarios include the first positioning state, the second positioning state, and the third positioning state.

[0186] In some possible implementations, the control module 503 is further configured to: determine candidate positioning states in which the vehicle is allowed to activate the assisted driving mode under the target driving scenario; control the vehicle to exit the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario not including any of the positioning states; and control the vehicle to start or maintain the assisted driving mode in response to the candidate positioning states corresponding to the target driving scenario including any of the positioning states.

[0187] In some possible implementations, after the vehicle is controlled to exit the assisted driving mode, the control module 503 is further configured to: generate first instruction information and perform visual display and / or voice broadcast of the first instruction information; Wherein, the first indication information is used to indicate at least one of the following: The vehicle has exited the assisted driving mode; The driver is prompted to manually drive the vehicle; The system indicates that the vehicle's location status is abnormal.

[0188] In some possible implementations, after controlling the vehicle to start or maintain the assisted driving mode, the control module 503 is further configured to: generate second instruction information and visually display and / or voice broadcast the second instruction information; The second indication information is used to indicate at least one of the following: The vehicle has been started or is maintaining an assisted driving mode; The system indicates that the vehicle's location status is normal.

[0189] In some possible implementations, the first determining module 501 is further configured to: determine the scene information of the predicted driving scenario in which the vehicle will be in a future time period, as the scene information of the target driving scenario; or, determine the scene information of the actual driving scenario in which the vehicle is in the current time period, as the scene information of the target driving scenario.

[0190] In some possible implementations, before determining the scene information of the predicted driving scenario in which the vehicle will be in a future time period as the scene information of the target driving scenario, the first determining module 501 is further configured to perform at least one of the following: The predicted driving scenario is determined to be a preset driving scenario; The time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a third preset time. The distance required for the vehicle to enter the area corresponding to the predicted driving scenario is determined to be less than a set distance.

[0191] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0192] The vehicle control device provided in the embodiments of this disclosure collects scene information of the vehicle in a target driving scenario, and determines the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information. Based on the relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle, it determines whether to switch the vehicle's positioning state. In response to the switching of the vehicle's positioning state, the vehicle is controlled based on the target positioning state after the switch; or, in response to the vehicle's positioning state not switching, the vehicle is controlled based on the vehicle's current positioning state. Therefore, by taking into account the scene information of the vehicle in the target driving scenario and dynamically evaluating the target positioning accuracy value of the vehicle in the target driving scenario (used to indicate the positioning accuracy requirement), the device can take into account the differentiated positioning accuracy requirements of different driving scenarios and achieve precise matching between the positioning accuracy requirement and the target driving scenario.

[0193] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0194] To implement the above embodiments, this disclosure also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the vehicle control method provided in this disclosure. For example, the electronic device may include a vehicle.

[0195] To implement the above embodiments, this disclosure also proposes a vehicle, including a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the vehicle control method provided in this disclosure.

[0196] Figure 6This is a schematic diagram illustrating the structure of a vehicle according to an exemplary embodiment. For example, vehicle 600 can be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 600 can be a driver-assisted vehicle, a semi-driver-assisted vehicle, or a driver-free vehicle. It should be noted that driver-assisted driving (also called intelligent driving) refers to the technology that uses sensors, algorithms, and artificial intelligence technologies to perform environmental perception, decision-making, planning, and control command execution of the vehicle to assist the driver in driving more safely and efficiently.

[0197] Reference Figure 6 The vehicle 600 may include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 600 can be interconnected via wired or wireless means.

[0198] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, etc.

[0199] The perception system 620 may include several sensors for sensing information about the environment surrounding the vehicle 600. For example, the perception system 620 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0200] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0201] The drive system 640 may include components that provide powered motion to the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0202] Some or all of the functions of vehicle 600 are controlled by computing platform 650. Computing platform 650 may include at least one processor 651 and memory 652, processor 651 can execute instructions 653 stored in memory 652.

[0203] Processor 651 can be any conventional processor. Processors may also include, for example, a Graphic Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof.

[0204] The memory 652 can be implemented by 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.

[0205] In addition to instruction 653, memory 652 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 652 can be used by computing platform 650.

[0206] In this embodiment of the disclosure, processor 651 may execute instructions 653 to implement all or part of the steps of the vehicle control method provided in this disclosure.

[0207] The vehicle in this embodiment collects scene information of the vehicle in a target driving scenario, and determines the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information. Based on the relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle, it determines whether to switch the vehicle's positioning state. In response to the switching of the vehicle's positioning state, the vehicle is controlled based on the target positioning state after the switch; or, in response to the vehicle's positioning state not switching, the vehicle is controlled based on the vehicle's current positioning state. Thus, by taking into account the scene information of the vehicle in the target driving scenario, the target positioning accuracy value of the vehicle in the target driving scenario (used to indicate the positioning accuracy requirement) can be dynamically evaluated, thus taking into account the differentiated positioning accuracy requirements of different driving scenarios and achieving a precise match between the positioning accuracy requirement and the target driving scenario.

[0208] Furthermore, the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value can be considered to determine whether to switch the vehicle's positioning state. If the vehicle's positioning state switches, control is applied based on the target positioning state after the switch; otherwise, control is applied based on the vehicle's current positioning state. This allows for precise determination of the vehicle's positioning state and on-demand switching, improving the accuracy of vehicle positioning, ensuring driving safety, and optimizing the driving experience.

[0209] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the vehicle control method provided in this disclosure.

[0210] Alternatively, non-transitory computer-readable storage media may be ROM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices, etc.

[0211] To implement the above embodiments, this disclosure also proposes a chip including an interface circuit and a processing circuit coupled to each other, wherein the interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the vehicle control method provided in this disclosure.

[0212] Figure 7 This is a schematic diagram illustrating the structure of a chip according to an exemplary embodiment. See also... Figure 7 The diagram shown is a schematic representation of the structure of chip 700, but is not limited thereto.

[0213] Chip 700 includes processing circuit 701, which is configured to execute any of the above vehicle control methods.

[0214] In some embodiments, chip 700 further includes one or more interface circuits 702. Optionally, interface circuit 702 is connected to memory 703, and interface circuit 702 can be used to receive signals from memory 703 or other devices, and interface circuit 702 can be used to send signals to memory 703 or other devices. For example, interface circuit 702 can read instructions stored in memory 703 and send the instructions to processing circuit 701.

[0215] In some embodiments, the interface circuit 702 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 701 performs other steps.

[0216] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0217] In some embodiments, chip 700 further includes one or more memories 703 for storing instructions. Optionally, all or part of the memories 703 may be located outside of chip 700.

[0218] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle control method provided in this disclosure.

[0219] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0220] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A vehicle control method, characterized in that, include: Collect scene information of the vehicle in the target driving scenario, and determine the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario; Based on the relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle, it is determined whether to switch the positioning status of the vehicle. In response to a change in the vehicle's location status, control is applied to the vehicle based on the target location status after the change; or, in response to no change in the vehicle's location status, control is applied to the vehicle based on the vehicle's current location status.

2. The method according to claim 1, characterized in that, Determining the target positioning accuracy value of the vehicle in the target driving scenario based on the scenario information of the target driving scenario includes: The scene information of the target driving scenario is processed by a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; Different driving scenarios correspond to different preset algorithms; The scene information includes at least one of the following: scene category, road curvature, vehicle speed, and perception confidence.

3. The method according to claim 2, characterized in that, The scene information includes scene information in multiple dimensions, and the preset algorithm includes the weights of scene information in each dimension; in the preset algorithms corresponding to different driving scenarios, the weights of the same category are different. The step of processing the scene information of the target driving scenario using a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value includes: Based on the weights of scene information in each dimension, the scene information in each dimension of the target driving scenario is weighted and summed to determine the target positioning accuracy value.

4. The method according to claim 1, characterized in that, The step of determining whether to switch the vehicle's positioning state based on the relationship between the target positioning accuracy value and the vehicle's actual positioning accuracy value includes: The target positioning status is determined based on the relationship between the target positioning accuracy value and the actual positioning accuracy value. In response to the current positioning state indicating a higher positioning accuracy than the target positioning state indicating a higher positioning accuracy, and the vehicle satisfying the target positioning state for a duration of a first preset duration, the vehicle's positioning state is switched from the current positioning state to the target positioning state; or, In response to the fact that the positioning accuracy represented by the current positioning state is lower than the positioning accuracy represented by the target positioning state, and the duration of the vehicle meeting the target positioning state reaches a second set duration, the positioning state of the vehicle is switched from the current positioning state to the target positioning state. Wherein, the first set duration is less than the second set duration.

5. The method according to claim 1, characterized in that, The target positioning status is determined in the following ways: Determine the mapping relationship between candidate intervals and candidate positioning states; wherein, the upper and lower limits of the candidate intervals are associated with the target positioning accuracy value; Determine the target interval in which the actual positioning accuracy value is located from each of the candidate intervals; Based on the mapping relationship, the candidate positioning state corresponding to the target interval is determined and used as the target positioning state.

6. The method according to claim 1, characterized in that, The actual positioning accuracy value is characterized by the lateral positioning error value and the longitudinal positioning error value, and the target positioning accuracy value is characterized by the target positioning error value; The target positioning status is determined by at least one of the following: In response to the fact that both the lateral positioning error value and the longitudinal positioning error value are less than or equal to the target positioning error value, the target positioning state is determined to be the first positioning state; In response to the lateral positioning error value being less than or equal to the target positioning error value, and the longitudinal positioning error value being greater than the target positioning error value, the target positioning state is determined to be a second positioning state; In response to the lateral positioning error value being greater than the target positioning error value and less than or equal to M times the target positioning error value, and the longitudinal positioning error value being less than or equal to M times the target positioning error value, the target positioning state is determined to be a third positioning state; where M>1; In response to the lateral positioning error value being greater than or equal to the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state; or, in response to the lateral positioning error value being greater than the target positioning error value and less than or equal to the target positioning error value of M times, and the longitudinal positioning error value being greater than the target positioning error value of M times, the target positioning state is determined to be the fourth positioning state.

7. The method according to claim 6, characterized in that, The target driving scenario includes any one of the following: highway, city, tunnel, and viaduct. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in a high-speed scenario include the first positioning state; The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in urban scenarios include the first positioning state and the third positioning state. The candidate positioning states in which the vehicle is allowed to activate the assisted driving mode in tunnel or viaduct scenarios include the first positioning state, the second positioning state, and the third positioning state.

8. The method according to claim 1, characterized in that, Controlling the vehicle based on either the target positioning state or the current positioning state includes: Determine candidate positioning states where the vehicle is allowed to activate the assisted driving mode in the target driving scenario; In response to the fact that the candidate positioning status corresponding to the target driving scenario does not include any of the positioning statuses, the vehicle is controlled to exit the assisted driving mode. In response to the candidate positioning state corresponding to the target driving scenario, including any one of the positioning states, the vehicle is controlled to start or maintain the assisted driving mode.

9. The method according to claim 8, characterized in that, After controlling the vehicle to exit the assisted driving mode, the method further includes: Generate first instruction information, and visualize and / or broadcast the first instruction information by voice. Wherein, the first indication information is used to indicate at least one of the following: The vehicle has exited the assisted driving mode; The driver is prompted to manually drive the vehicle; The system indicates that the vehicle's location status is abnormal.

10. The method according to claim 8, characterized in that, After controlling the vehicle to start or maintain the assisted driving mode, the method further includes: Generate a second instruction message, and visualize and / or voice-broadcast the second instruction message; The second indication information is used to indicate at least one of the following: The vehicle has been started or is maintaining an assisted driving mode; The system indicates that the vehicle's location status is normal.

11. The method according to any one of claims 1-10, characterized in that, The collected scene information of the vehicle in the target driving scenario includes: Determine the scene information of the predicted driving scenario in which the vehicle will be located in a future time period, and use it as the scene information of the target driving scenario; or, The scene information of the actual driving scenario in which the vehicle is located in the current time period is determined, and is used as the scene information of the target driving scenario.

12. The method according to claim 11, characterized in that, Before determining the scene information of the predicted driving scenario in which the vehicle will be in a future time period, as the scene information of the target driving scenario, the method further includes at least one of the following: The predicted driving scenario is determined to be a preset driving scenario; The time required for the vehicle to enter the area corresponding to the predicted driving scenario is less than a third preset time. The distance required for the vehicle to enter the area corresponding to the predicted driving scenario is determined to be less than a set distance.

13. A vehicle control device, characterized in that, include: The first determining module is configured to collect scene information of the vehicle in the target driving scenario, and determine the target positioning accuracy value of the vehicle in the target driving scenario based on the scene information of the target driving scenario. The second determining module is configured to determine whether to switch the positioning state of the vehicle based on the relationship between the target positioning accuracy value and the actual positioning accuracy value of the vehicle. The control module is configured to control the vehicle based on the target location state after the location state of the vehicle changes in response to a change in the vehicle's location state; or, to control the vehicle based on the current location state of the vehicle if the location state of the vehicle does not change.

14. The apparatus according to claim 13, characterized in that, The first determining module is further configured to: The scene information of the target driving scenario is processed by a preset algorithm corresponding to the target driving scenario to determine the target positioning accuracy value; Different driving scenarios correspond to different preset algorithms; The scene information includes at least one of the following: scene category, road curvature, vehicle speed, and perception confidence.

15. The apparatus according to claim 14, characterized in that, The scene information includes scene information in multiple dimensions, and the preset algorithm includes the weights of scene information in each dimension; in the preset algorithms corresponding to different driving scenarios, the weights of the same category are different. The first determining module is further configured to: Based on the weights of scene information in each dimension, the scene information in each dimension of the target driving scenario is weighted and summed to determine the target positioning accuracy value.

16. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: The steps for implementing the method according to any one of claims 1-12.

17. A non-transitory computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1-12.

18. A chip, characterized in that, The chip includes an interface circuit and a processing circuit coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the method according to any one of claims 1-12.

19. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-12.

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