Control method of vehicle and vehicle
By using a multi-data source fusion evaluation mechanism to acquire and evaluate road speed limit information, the problem of inaccurate identification in existing technologies is solved, thereby improving vehicle driving safety and the timeliness of navigation maps.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GREAT WALL MOTOR CO LTD
- Filing Date
- 2026-02-09
- Publication Date
- 2026-06-12
Smart Images
Figure CN122201020A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of intelligent driving technology, specifically to a vehicle control method and a vehicle. Background Technology
[0002] With the increasing demand for intelligent vehicles, intelligent driving technology is experiencing rapid development. In the process of developing intelligent driving technology, accurately obtaining speed limit information of the road ahead to ensure driving safety and compliance with traffic regulations is a crucial requirement.
[0003] Currently, in related technologies, vehicles primarily rely on static navigation map data and the basic visual functions of onboard cameras to identify road speed limit signs. However, in practical applications, the long update cycle and insufficient timeliness of static navigation map data lead to a lag in the acquisition of road speed limit information, making it difficult to promptly reflect temporary speed limit information or the latest adjusted speed limit information for road sections. Furthermore, onboard cameras are susceptible to limitations in field of view, obstructions, or the range of vision, which may result in misidentification or missed identification. Therefore, neither of these two identification methods can consistently provide accurate and reliable road speed limit information, posing potential safety risks to vehicle operation.
[0004] Therefore, improving the accuracy and reliability of road speed limit information recognition to enhance vehicle driving safety has become an urgent problem to be solved. Summary of the Invention
[0005] In view of this, embodiments of this application provide a vehicle control method and a vehicle, which determines road speed limit information for the vehicle by constructing an evaluation mechanism that integrates multiple data sources, thereby improving the accuracy and reliability of the road speed limit information.
[0006] In a first aspect, embodiments of this application provide a vehicle control method, the method comprising: acquiring road speed limit information from at least two independent data sources for the same road segment; evaluating the credibility of each road speed limit information from the at least two independent data sources from at least two dimensions, and outputting the credibility evaluation result; determining the final road speed limit information for the vehicle based on the credibility evaluation result of each road speed limit information; and controlling the vehicle speed based on the final road speed limit information for the vehicle.
[0007] As one possible implementation of the first aspect, the credibility assessment of each road speed limit information from at least two independent data sources includes: assessing the credibility of each road speed limit information from at least two dimensions, namely source data quality, spatiotemporal consistency, and logical rationality, based on the road speed limit information from at least two independent data sources.
[0008] As one possible implementation of the first aspect, the credibility assessment of each road speed limit information from the perspective of source data quality, based on road speed limit information from at least two independent data sources, includes: obtaining additional information related to the source data quality of the road speed limit information, wherein the additional information is used to assist in determining the accuracy of the road speed limit information from at least two independent data sources; and calculating the confidence score of each road speed limit information from at least two independent data sources based on the additional information, thereby achieving the credibility assessment of the road speed limit information.
[0009] As one possible implementation of the first aspect, the method further includes: storing road speed limit information in a pre-constructed cache space comprising at least two different cache levels, wherein the timeliness and reliability of the road speed limit information stored in the cache spaces of different cache levels are different.
[0010] As one possible implementation of the first aspect, storing road speed limit information in a pre-built cache space comprising at least two different cache levels includes: determining the timeliness and reliability of the road speed limit information, wherein the timeliness is determined based on the interval between the time the road speed limit information was generated and the current time, with a longer interval resulting in lower timeliness; determining the level of the road speed limit information based on its timeliness and reliability; and storing the road speed limit information in a cache space of the corresponding level based on its level, wherein a higher level of cache space results in lower timeliness and reliability of the stored road speed limit information.
[0011] As one possible implementation of the first aspect, the method also includes: acquiring vehicle status information and environmental information around the vehicle in real time; Based on status and environmental information, special road scenarios are identified. A special road scenario refers to a scenario that requires specific speed limit management due to specific factors, including road structure, environmental conditions, or temporary events. When a special road scenario is identified, the road speed limit information for the special road scenario is determined according to a pre-built special road scenario rule base. The special road scenario rule base includes speed limit information for at least one special road scenario.
[0012] As a possible implementation of the first aspect, the method further includes: acquiring the current state information of the vehicle; comparing the current state information of the vehicle with road speed limit information determined for the vehicle, and outputting the comparison result; determining the level of the overspeed warning prompt based on the comparison result, and controlling the vehicle to execute the corresponding level of the overspeed warning prompt, wherein the level of the overspeed warning prompt includes at least two levels.
[0013] As a possible implementation of the first aspect, the method further includes: loading the latest navigation map data after the vehicle is started or when the difference between the road speed limit information obtained from at least two independent data sources exceeds a preset difference threshold, in order to determine whether the road speed limit information of the road segment has changed.
[0014] As one possible implementation of the first aspect, the independent data source includes at least two of the following: the vehicle camera, the vehicle navigation system, and the vehicle communication module.
[0015] Secondly, embodiments of this application provide a vehicle including a processor; and a memory for storing processor-executable instructions, wherein the processor is used to execute the vehicle control method described in the first aspect above.
[0016] This application provides a vehicle control method and a vehicle. The method acquires road speed limit information from multiple different independent data sources, evaluates the reliability of the acquired road speed limit information from multiple dimensions, constructs an evaluation mechanism that integrates multiple data sources to determine the final road speed limit information used for the vehicle, and realizes the collaboration and cross-validation of multiple data sources to improve the accuracy and reliability of road speed limit information, thereby improving the safety of vehicle driving. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of the structure of a road speed limit information decision system provided in an exemplary embodiment of this application.
[0019] Figure 2 This is a schematic flowchart of a vehicle control method provided in an exemplary embodiment of this application.
[0020] Figure 3 This is a flowchart illustrating a method for assessing the credibility of road speed limit information provided in an exemplary embodiment of this application.
[0021] Figure 4a This is a schematic diagram of an overspeed warning prompt interface provided by an exemplary embodiment of this application.
[0022] Figure 4b This is a schematic diagram of another overspeed warning prompt interface provided by an exemplary embodiment of this application.
[0023] Figure 4c This is a schematic diagram of another overspeed warning prompt interface provided in an exemplary embodiment of this application.
[0024] Figure 5 This is a flowchart illustrating a method for determining road speed limit information in a specific road scenario provided by an exemplary embodiment of this application.
[0025] Figure 6 This is a schematic diagram of the structure of a cross-device vehicle control device provided in an exemplary embodiment of this application.
[0026] Figure 7 This is a block diagram of an electronic device for controlling a vehicle, provided in an exemplary embodiment of this application. Detailed Implementation
[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0028] Application Overview Intelligent driving technology comprises modules such as perception, decision-making, and control, enabling vehicles to autonomously perceive the road environment, make driving decisions, and execute driving operations. In intelligent driving technology, the accuracy of recognizing the speed limit ahead directly affects the reliability and safety of the intelligent driving system, serving as a crucial foundation for ensuring vehicle safety and compliance with traffic regulations.
[0029] In related technologies, road speed limit information is primarily identified through forward-facing vehicle cameras and static navigation map data. However, both methods have limitations in recognizing road speed limits. Recognizing speed limits via onboard cameras is susceptible to external interference, such as lighting conditions, weather conditions, obstruction, and shooting angle, leading to misidentification or omission of speed limit information. Static map data is delayed in updating, lacking timeliness and failing to reflect temporary or changing latest speed limit information. In summary, both of these recognition methods struggle to provide accurate and stable road speed limit information, posing potential safety risks to vehicle operation.
[0030] To address the aforementioned technical issues, this application's embodiments creatively acquire road speed limit information from multiple different independent data sources, evaluate the reliability of the acquired road speed limit information, and construct a multi-data source fusion evaluation mechanism to determine the final road speed limit information used for vehicles, thereby ensuring the accuracy and reliability of the road speed limit information and improving vehicle driving safety.
[0031] Various non-limiting embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0032] Control system of an exemplary vehicle Figure 1 This is a schematic diagram of the structure of a speed limit information decision system provided in some embodiments of this application, such as... Figure 1 As shown, the speed limit information decision system 100 may include a vehicle terminal 110. The vehicle terminal 110 includes a data acquisition device 111, a data communication device 112, a human-machine interaction device 113, and a control system 114, wherein the data acquisition device 111, the data communication device 112, and the human-machine interaction device 113 are respectively connected to the control system 114.
[0033] The vehicle terminal 110 mainly acquires the data required during driving through the data acquisition device 111 and the data communication device 112.
[0034] The data acquisition device 111 is used to acquire vehicle status information and surrounding environmental information. In some embodiments, the data acquisition device 111 may include a vehicle status sensor component and an environmental perception sensor component. The vehicle status sensor component can be used to acquire vehicle driving data; for example, the vehicle status sensor may include an inertial measurement unit (IMU), wheel speed sensors, lateral acceleration sensors, and longitudinal acceleration sensors. The environmental perception sensor component may include a vision sensor component and an environmental sensor component. The vision sensor is used to acquire image data of the environment surrounding the vehicle; specifically, the vision sensor can be used to acquire image data of road speed limit signs ahead of the vehicle; for example, the vision sensor component may include an onboard camera. The environmental sensor component is used to acquire weather data of the road section surrounding the vehicle; for example, the environmental sensor component may include a temperature sensor and a rain sensor.
[0035] In some embodiments, the data acquisition device may also integrate an in-vehicle navigation and positioning system. For example, the in-vehicle navigation and positioning system can combine satellite positioning data with high-precision map data pre-stored in the vehicle to achieve vehicle positioning. By matching the real-time location coordinates of the vehicle with pre-coded road attribute information (e.g., road speed limits) in the map, the road speed limits for the current and forward road segments are determined.
[0036] The data communication device 112 can be used for data interaction between the vehicle and external entities to obtain data required during vehicle operation. For example, the data communication device 112 can establish a communication link with external devices via Vehicle-to-Everything (V2X) wireless communication technology to receive data sent by external devices. For instance, the data communication device can utilize V2X communication to receive road speed limit information sent from other vehicles, roadside units, or cloud control platforms.
[0037] The human-machine interface device 113 is used to enable interaction between the user and the vehicle, acquiring user input data or displaying prompts and warnings to remind the user. For example, the human-machine interface device 113 may include a display device, such as an in-vehicle display screen, a head-up display (HUD), or an instrument panel. For instance, the user can interact with the vehicle terminal 110 by entering information in an input box displayed on the in-vehicle display screen. The vehicle terminal can then provide warnings or prompts to the user through the in-vehicle display screen, HUD, or instrument panel. This can be achieved by displaying warning information on the in-vehicle display screen, HUD, or instrument panel, or by adjusting the brightness and color of the in-vehicle display screen, HUD, or instrument panel, thus enabling interaction with the user.
[0038] In some embodiments, the control system 114 is used to control the data acquisition device 111 and the data communication device 112 to acquire corresponding data in order to execute the vehicle control method described below.
[0039] In some embodiments, the control system 114 may include advanced driver assistance systems (ADAS).
[0040] Exemplary vehicle control method To further illustrate the specific process for determining road speed limit information for vehicles, this application provides a flowchart of a vehicle control method ( Figure 2 ).
[0041] In some embodiments, such as Figure 2As shown, the control system 114 can perform the following steps: S210. Obtain road speed limit information for the same road segment from at least two independent data sources.
[0042] A data source can refer to a device or system used to acquire road speed limit information ahead of a vehicle. In some embodiments, to improve the reliability of road speed limit information, road speed limit information for the same road segment is acquired from at least two data sources. For example, the data sources may include at least two of the following: an in-vehicle camera, an in-vehicle navigation system, and an in-vehicle communication module. That is, road speed limit information for the same road segment can be acquired through a combination of multiple data sources. For instance, only road speed limit information from the in-vehicle camera and road speed limit information from the in-vehicle navigation system may be acquired; road speed limit information from the in-vehicle camera and road speed limit information received from the in-vehicle communication module may also be acquired; or road speed limit information from the in-vehicle camera, road speed limit information from the in-vehicle navigation system, and road speed limit information received from the in-vehicle communication module may be acquired simultaneously to provide a sufficient and reliable data foundation for the subsequent road speed limit information decision-making process.
[0043] Specifically, to obtain road speed limit information, images of road speed limit signs ahead of the vehicle can be captured by a forward-facing vehicle camera. For example, images of the road within a range of 10 to 100 meters ahead of the vehicle can be continuously captured at a pre-set frame rate (e.g., 30 frames per second). Furthermore, image recognition algorithms are used to identify the road speed limit signs in the acquired multi-frame images, extracting the numbers and symbols from the road signs to determine the road speed limit information.
[0044] The in-vehicle navigation system pre-stores high-precision map data containing road speed limit attributes. It obtains the vehicle's current location information (e.g., geographic coordinates) in real time through positioning technology, and then matches the obtained vehicle location information with the pre-stored map data to determine the road segment where the vehicle is currently located, so as to obtain the speed limit information of the road ahead of the vehicle based on the high-precision data.
[0045] The vehicle-to-everything (V2X) communication module can receive road speed limit information about the road ahead from external sources via V2X communication technology. For example, it can receive road speed limit information from other vehicles, roadside units, or cloud control platforms via V2X communication technology.
[0046] In some embodiments, the road speed limit information received via V2X communication technology may include temporary speed limit instructions broadcast by the Road Side Unit (RSU), such as speed limits in construction or school zones; it may also include speed limit warnings issued by traffic signals or smart intersections, and forward speed limit change warnings shared by vehicles ahead via vehicle-to-vehicle (V2V) communication technology, such as when the vehicle ahead triggers emergency deceleration, or when the traffic management center pushes out regional speed limit policies, such as speed limit adjustments for severe weather.
[0047] It should be noted that the types of independent data sources listed in the above embodiments are merely examples. Any other device that can obtain road speed limit information can be used as a data source. This application does not specifically limit the types of independent data sources.
[0048] By acquiring road speed limit information from multiple independent data sources, a multi-data fusion framework is constructed. Through collaborative processing and cross-validation of multiple data sources, comprehensive identification and judgment of road speed limit information are achieved, providing a more comprehensive and reliable data foundation for subsequent road speed limit information decision-making processes, thereby improving the accuracy of road speed limit information decisions.
[0049] In some embodiments, road speed limit information may be static road speed limit information. For example, static road speed limit information may include standard road segment speed limit information, i.e., constant speed limits specified according to road design, such as 120 km / h for highways and 60 km / h for urban roads; it may also include lane-specific speed limits, i.e., speed limits for different lanes on the same road segment, such as 110 km / h for express lanes and 80 km / h for truck lanes; and it may also include speed limit information for different vehicle types, such as 100 km / h for cars, 80 km / h for buses, and 60 km / h for trucks.
[0050] In some embodiments, the road speed limit information can also be dynamic (variable) speed limit information, depending on the road scenario. For example, the road speed limit information can be traffic flow control speed limit information. For instance, to alleviate congestion, the road speed limit can be adjusted according to real-time traffic flow. For example, during congested periods, the road speed limit can be reduced from 100 km / h to 80 km / h. Alternatively, the road speed limit can be temporarily set according to weather conditions or road conditions. For example, in rainy or foggy weather, the road speed limit can be set to 60 km / h, and in icy road conditions, the road speed limit can be set to 40 km / h.
[0051] It should be noted that the road speed limit information described in the above embodiments is merely an example, and this application does not specifically limit the specific type of road speed limit information.
[0052] In some embodiments, after collecting data related to road speed limit information from different data sources, preprocessing is required. For example, road images acquired by vehicle-mounted cameras can first undergo noise reduction processing. For instance, Gaussian filtering, median filtering, and bilateral filtering can be used to remove noise from the image. After noise removal, further contrast enhancement and color correction operations are performed to improve the recognizability of target areas in the image and adapt to varying lighting conditions. Finally, the candidate target areas (road speed limit sign areas) are selected to prepare data for subsequent recognition of road speed limit information.
[0053] As the distance between the vehicle-mounted camera and the speed limit sign changes continuously during vehicle movement, significant differences in pixel size occur in consecutive image frames captured on the same road segment for the same speed limit sign. To address this issue, some embodiments employ a multi-scale feature extraction model to perform hierarchical feature extraction on the image frames captured by the vehicle-mounted camera. Leveraging the inherent multi-scale representation capabilities of the multi-scale model, road speed limit information in the images can be identified. This method effectively overcomes the interference of target size variations on the recognition process through the scale invariance of the multi-scale model, thereby improving the accuracy and reliability of road speed limit sign recognition.
[0054] To ensure the accuracy of the acquired road speed limit information, in some embodiments, the high-precision map data can be updated and the latest high-precision map data can be loaded each time the vehicle is started.
[0055] In some embodiments, a difference threshold can also be set. When the difference between road speed limit information obtained from any two independent data sources exceeds the difference threshold, the latest high-precision navigation map data is loaded, and the high-precision navigation map data is dynamically updated to detect changes in road speed limit information. For example, the difference threshold can be set to 10 km / h. For instance, if the road speed limit information identified by the vehicle camera is 40 km / h, and the corresponding road segment's speed limit information obtained from the navigation map data is 20 km / h, the difference between the road speed limit information obtained from the two different independent data sources is 20 km / h, exceeding the difference threshold of 10 km / h. At this time, the navigation map update mechanism is triggered, and the latest high-precision navigation map data is loaded to achieve dynamic updates of the high-precision navigation map data.
[0056] S220. Evaluate the credibility of each road speed limit information from at least two independent data sources from at least two dimensions, and output the credibility evaluation results.
[0057] S230. Based on the credibility assessment results of each road speed limit information, determine the final road speed limit information to be used for vehicles.
[0058] S240. Control the vehicle speed based on the road speed limit information ultimately used for the vehicle.
[0059] Credibility assessment refers to evaluating the reliability of road speed limit information from different independent data sources, i.e., the probability of it being close to the actual speed limit value of the current road. This is used to provide a basis for decision-making in subsequent multi-source information fusion and cross-validation processes, thereby improving the accuracy of road speed limit information decisions.
[0060] To improve the accuracy of credibility assessment of road speed limit information, in some embodiments, the credibility assessment of road speed limit information can be implemented from multiple dimensions. For example, based on road speed limit information obtained in S210 from at least two independent data sources, the credibility of each road speed limit information can be assessed from at least two dimensions: source data quality, spatiotemporal consistency, and logical rationality. This improves the accuracy of the credibility assessment of road speed limit information, thereby enhancing the accuracy and reliability of the final road speed limit information determined for vehicles.
[0061] In some embodiments, such as Figure 3 As shown, the credibility assessment of each road speed limit information from the perspective of source data quality can be achieved through the following steps: S310. Obtain additional information related to the quality of the source data for road speed limit information.
[0062] S320. Based on the additional information, calculate the confidence score of each of the at least two road speed limit information to achieve a credibility assessment of the road speed limit information.
[0063] Additional information can refer to data associated with road speed limit information, used to help determine the accuracy, or reliability, of at least two road speed limit information sets. Different data sources require different additional information.
[0064] For example, for road speed limit information obtained through image recognition from an in-vehicle camera, the corresponding additional information may include the image quality of the road speed limit sign, the completeness of the road speed limit sign in the image, and the consistency of consecutive image frames; for road speed limit information obtained through navigation map data, the corresponding additional information may include the update time of the navigation map data, the source of the map data, and the historical accuracy of the current version of the navigation data; for road speed limit information received through an in-vehicle communication module, the corresponding additional information may include the strength of the communication signal when receiving the road speed limit information (e.g., the power and signal-to-noise ratio of the received signal, the continuity of message reception, and the bit error rate and packet loss rate, etc.) and the credibility and legitimacy of the communication terminal that sent the road speed limit information.
[0065] In some embodiments, a confidence score for each road speed limit information can be calculated using a confidence function based on the additional information corresponding to each data source. This quantifies the reliability of road speed limit information provided by different data sources, thereby assessing the credibility of the road speed limit information. The confidence function characterizes the mapping relationship between the additional information from different data sources and the confidence level of the road speed limit information.
[0066] In some embodiments, the calculated confidence scores are normalized and mapped to the interval [0,1] to establish a unified data standard, which facilitates subsequent decision-making on road speed limit information.
[0067] In some embodiments, when the confidence score of road speed limit information obtained from any data source is greater than or equal to 0.9 and is consistent with the road speed limit information obtained from other data sources, the road speed limit information is directly used as the final road speed limit information for vehicles, so as to improve the efficiency of overall road speed limit information decision-making.
[0068] In some embodiments, if the road speed limit information obtained from different independent data sources is inconsistent and conflicting, the road speed limit information is weighted and fused according to the confidence scores of each road speed limit information to calculate the total confidence score of the road speed limit information from different data sources from the data source dimension. Based on the total confidence score, the credibility of each road speed limit information is evaluated to determine the final road speed limit information used for vehicles.
[0069] Specifically, the weighted fusion process can be implemented using the following formula: in, This represents the overall confidence score for evaluating road speed limit information from the perspective of data source. The confidence score represents the confidence level of road speed limit information derived from navigation map data. This represents the confidence score of road speed limit information identified through vehicle-mounted cameras. This represents the confidence score of road speed limit information obtained through external communication. This represents environmental factors, such as weather and sunlight. Correspondingly, The weighting coefficient represents the road speed limit information obtained through external communication. The weighting coefficients represent road speed limit information derived from navigation map data. The weighting coefficient represents the road speed limit information identified by the vehicle's onboard camera. The weighting coefficients representing environmental factors, where, .
[0070] In some embodiments, a primary reference data source may be determined based on the reliability of each data source.
[0071] For example, because road speed limit information received via V2X communication technology has strong real-time performance and high reliability, V2X communication data is set as the highest initial priority and used as the primary reference source. Specifically, V2X communication data > navigation map data > visual perception (vehicle camera image data) > environmental factor data. Correspondingly, , , and These can be set to 0.5, 0.3, 0.15, and 0.05 respectively; alternatively, environmental factors can be disregarded. Set to 0, , , They were set to 0.5, 0.3, and 0.2 respectively.
[0072] In some embodiments, the priority of each data source can be adjusted according to the actual situation. For example, when the signal strength during V2X communication is low or the timeliness of road speed limit information obtained through V2X communication technology is low (e.g., the road speed limit information for the corresponding road segment was received a week ago), the priority of V2X communication data is reduced, and navigation map data is adjusted to the highest priority as the primary reference data source.
[0073] It should be noted that the types and quantities of data sources in the credibility assessment process and the weighting coefficients set for each data source in the weighted fusion process described in the above embodiments are merely examples. The types and quantities of reference data sources, the priority of each data source, and the main reference source can be flexibly adjusted according to the actual situation. This application does not impose any specific limitations on this.
[0074] In some embodiments, spatiotemporal consistency verification of road speed limit information can be performed based on spatial and temporal consistency to assess the credibility of the road speed limit information. Specifically, spatial consistency verification can refer to verifying the spatial rationality between road speed limit signs and vehicle positions. For example, for visual perception data from vehicle-mounted cameras, the positions of the vehicle-mounted camera and the road speed limit signs captured by the camera can be obtained; for V2X communication data, geofence information broadcast by V2X communication can be determined using spatiotemporal fencing technology; then, the errors between the position of the road speed limit sign, the geofence broadcast by V2X communication, and the geographical coordinates of the corresponding road segment in the navigation map data can be verified. If the error is less than the error threshold, the positions coincide within the error threshold (e.g., 5 meters), and spatial consistency is determined. Spatiotemporal consistency verification can include verifying the rationality of the temporal change sequence of road speed limit information. For example, if the change sequence of road speed limit information obtained by the vehicle-mounted camera shows that the road speed limit information suddenly changes from 80 km / h to 30 km / h within 1 second, it is obviously illogical, and spatiotemporal inconsistency is determined.
[0075] In some embodiments, the logical rationality of road speed limit information can also be verified to assess its credibility. For example, the type of road can be further determined to assess the rationality of the acquired road speed limit information for that road segment. For instance, road type recognition technology might identify the current road as a highway, and the corresponding road speed limit information might be 140 km / h. However, according to the Road Traffic Safety Law, the maximum speed limit for highways is 120 km / h, although in some areas or special sections the speed limit may be 100 km / h or 80 km / h. 140 km / h is clearly outside the prescribed highway speed limit range, and therefore deemed unreasonable.
[0076] By verifying the spatiotemporal consistency and logical rationality of road speed limit information from multiple data sources, misidentified and obviously incorrect road speed limit information is effectively filtered out, significantly improving the accuracy of road speed limit information recognition and vehicle driving safety.
[0077] In some embodiments, after obtaining road speed limit information from multiple data sources, the spatiotemporal consistency and logical rationality of the road speed limit information can be verified first to remove obviously problematic and erroneous road speed limit information, thereby reducing the amount of data that needs to be processed in the subsequent road speed limit information decision-making process. Then, the credibility of the filtered road speed limit information is evaluated from the perspective of source data quality, realizing multi-data source collaborative cross-validation, and improving the reliability and accuracy of the road speed limit information used for vehicle final decision.
[0078] In summary, by acquiring road speed limit information from multiple independent data sources and constructing a multi-source data fusion decision-making architecture with credibility assessment and spatiotemporal consistency, the system enables decision-making on road speed limit information ultimately used for vehicles. This achieves collaboration and cross-validation among multiple data sources, improving the accuracy and reliability of road speed limit information. Furthermore, a navigation map data change detection mechanism is introduced to dynamically update navigation map data, forming a self-correcting and continuously optimizing information enhancement closed loop. This ensures the accuracy of road speed limit information decisions, and the dynamic updating of data sources enhances the long-term reliability and environmental adaptability of the decision-making system, thereby improving vehicle driving safety.
[0079] In some embodiments, a multi-level caching architecture is employed to achieve hierarchical storage and management of road speed limit information. Specifically, the timeliness and reliability of the road speed limit information can be determined first, and the level of the road speed limit information can be determined based on the timeliness and reliability of the road speed limit information; further, based on the level of the road speed limit information, the road speed limit information is stored in the corresponding level of the cache space, wherein the higher the level of the cache space, the lower the timeliness and reliability of the stored road speed limit information.
[0080] For example, the caching architecture can include three levels: Level 1 cache (L1 cache), Level 2 cache (L2 cache), and Level 3 cache (L3 cache). Based on the timeliness, reliability, and frequency of use of road speed limit information, the information is distributed and stored in the three levels of cache, achieving hierarchical storage of road speed limit information. This ensures efficient caching, fast response, and long-term memory of road speed limit information while maintaining its real-time nature and accuracy.
[0081] The Level 1 cache (L1 cache) serves as an instantaneous response layer, caching the most recently determined high-reliability road speed limit information for vehicles based on reliability assessments. This enables the fastest possible response to changes in the external road environment (millisecond-level response), meeting the real-time requirement of immediately updating vehicle road speed limit information after a decision is made. It is the core guarantee for dynamically updating vehicle road speed limit information. Data stored in the L1 cache has a short lifespan. For example, the lifespan of data stored in the L1 cache can be set to 30 seconds, after which it automatically expires. Once data in the L1 cache (e.g., road speed limit information) expires, it is automatically downgraded and migrated to the Level 2 cache.
[0082] The Level 2 cache (L2 cache) serves as a short-term memory layer, storing recently valid but outdated road speed limit information or road speed limit information that was not used by vehicles after a reliability assessment. It acts as a safety net when data in the L1 cache becomes invalid. For example, the L2 cache can store invalidated data from the Level 1 cache, historical versions of navigation map data, and temporary speed limit information received from other vehicles via V2X communication technology, such as "Construction ahead 500 meters, please pay attention to speed." The lifespan of data stored in the L2 cache can be set to 24 hours, after which it is automatically cleared. The L2 cache provides basic data when data in the Level 1 cache becomes invalid or when no road speed limit information is identified, improving the continuity and stability of road speed limit information provided in complex environments.
[0083] The Level 3 cache serves as a long-term reserve layer, storing highly reliable static speed limit rules and historically acquired road speed limit information. This acts as the ultimate baseline data for system initialization, navigation map update failures, or extreme environments. For example, the Level 3 cache may include legally mandated road speed limits embedded in the high-precision navigation map, historically stable road speed limits that have undergone multiple reliability assessments (e.g., a road segment with a speed limit of 60 km / h for 30 consecutive days without change), and a road regulation library built based on traffic laws (e.g., speed limits of 60 km / h on urban arterial roads and 120 km / h on highways). Data in the Level 3 cache can be persistently stored and retained long-term, only being updated when the navigation map version is upgraded or manually reset by the user.
[0084] In some embodiments, the third-level cache has the lowest access priority in a multi-level caching architecture. The third-level cache is accessed only to query road speed limit information when the corresponding road speed limit information is not found in the first-level cache and the second-level cache.
[0085] It should be noted that the number of levels in the multi-level caching architecture described in the above embodiments and the type of data stored in the corresponding level cache are only examples. This application does not specifically limit the number of levels in the multi-level caching architecture and the type of data stored in the corresponding level cache.
[0086] Considering that in practical applications there may be special road scenarios that require temporary speed limits or have special speed limit requirements, in some embodiments, this application provides a specific road speed limit requirement decision method for special road scenarios to improve adaptability to complex road scenarios.
[0087] For example, a rule base for special road scenarios can be created. Based on this rule base, decisions regarding road speed limit information in special road scenarios can be made, thereby improving the performance of road speed limit information recognition in complex road scenarios. For detailed information on determining road speed limit information for special road scenarios, please refer to [link to relevant documentation]. Figure 5 The relevant descriptions in the embodiments thereof.
[0088] In some embodiments, a speeding warning can be issued to the user based on the vehicle's current driving status and the determined speed limit information of the current road.
[0089] Specifically, the system can acquire the vehicle's current speed and the speed limit information of the road the vehicle is currently traveling on, calculate the difference between the vehicle's speed and the road speed limit, and determine whether to issue a speeding warning and, if so, the level of the speeding warning. The speeding warning level includes at least two levels. For example, a three-level speeding warning system can be set. A Level 1 speeding warning is issued when the vehicle's speed exceeds 90% of the corresponding road speed limit; a Level 2 speeding warning is issued when the difference between the vehicle's speed and the corresponding road speed limit is greater than 5 km / h (i.e., the speed exceeds the road speed limit by 5 km / h); and a Level 3 speeding warning is issued when the difference between the vehicle's speed and the corresponding road speed limit is greater than 15 km / h (i.e., the speed exceeds the road speed limit by 15 km / h).
[0090] In some embodiments, different levels of overspeed warnings can be implemented by displaying different colors and outputting text prompts on the vehicle's dashboard or HUD. For example, such as... Figure 4a As shown, when a Level 1 overspeed warning is triggered, the vehicle's instrument panel or HUD will be adjusted to a blue background and the current vehicle speed will be displayed; Figure 4b As shown, when a Level 2 speeding warning is triggered, the vehicle's instrument panel or HUD will be adjusted to a yellow background, and the current road speed limit and current speed will be displayed separately in red text. The font size will be enlarged, and the message "Minor Speeding" will be displayed below the text indicating the current road speed limit and current speed. Figure 4c As shown, when a Level 3 overspeed warning is triggered, the vehicle's dashboard or HUD will be adjusted to a red background, and the current road speed limit, current vehicle speed, and the message "Severe speeding, please slow down immediately" will be displayed in red-bordered white text.
[0091] It should be noted that the classification of speeding warning levels and the prompting methods described in the above embodiments are merely examples and can be flexibly adjusted according to actual circumstances.
[0092] In some embodiments, road speed limit information in the first-level cache can be read as the source and basis for speeding warning information.
[0093] In some embodiments, by deeply collaborating with driving assistance systems such as adaptive cruise control and lane keeping assist, real-time road speed limit information determined for the vehicle is integrated into the decision and control loop of the ADAS system, thereby realizing the real-time application of road speed limit information and the linkage of driving assistance functions.
[0094] Exemplary method for determining road speed limit information in special road scenarios To further illustrate the specific process of determining road speed limit information for special road scenarios, this application provides a flowchart illustrating a method for determining road speed limit information for special road scenarios. Figure 5 ).
[0095] In some embodiments, such as Figure 5 As shown, the control system 114 can perform the following steps: S510 can acquire real-time vehicle status information and information about the surrounding environment.
[0096] In some embodiments, the vehicle's state information includes at least the vehicle's speed, and the vehicle's surrounding environment information includes at least the road information in front of the vehicle and the weather information of the road where the vehicle is located, so as to provide a data basis for subsequent identification of special road scenes.
[0097] S520. Identify special road scenarios based on status and environmental information.
[0098] S530. When a special road scene is identified, the road speed limit information of the special road scene is determined according to the pre-built special road scene rule base.
[0099] Special road scenarios refer to situations where specific speed limits are required due to particular factors, including road structure, environmental conditions, or temporary events. Examples of special road scenarios include temporary road construction, speed limits in school zones, tunnels, and inclement weather.
[0100] In some embodiments, the identification of different special road scenarios can be achieved based on the vehicle's state information and the environmental information surrounding the vehicle.
[0101] For example, in the case of temporary construction road scenarios, a multi-feature fusion algorithm can be used to analyze the acquired image of the road in front of the vehicle, and to identify target objects such as construction signs, cones, and notice boards in the image to determine whether it is a temporary construction section.
[0102] For school zones, spatiotemporal fencing technology can be used to determine speed-limited areas. For example, based on navigation map data, the geographical coordinates of schools can be determined. A virtual fence with a radius of 500 meters can be created centered on these school coordinates, and each fence can be bound to the corresponding school information, with speed limit rules pre-set for the school area. Since schools may require speed limits during specific times for school arrival and dismissal, time rules can be configured for each school fence. For example, on weekdays (Monday to Friday), the effective times could be 7:00 AM to 8:30 AM and 3:30 PM to 6:00 PM, thus achieving precise time-based speed limits for school areas.
[0103] For road scenarios with severe weather, the current road conditions can be comprehensively analyzed by using environmental sensor components to obtain temperature information, rainfall information, road image data, and weather data released by meteorological departments from the cloud. The vehicle speed can then be adjusted accordingly based on the degree of weather impact on the road.
[0104] Considering that some special road scenarios may not have explicit speed limit signs, in order to achieve intelligent decision-making on road speed limits in special road scenarios where effective speed limit perception data is unavailable, some embodiments can pre-build a special road scenario rule base. Based on this rule base, the road speed limit information for special road scenarios can be intelligently determined, thereby improving the ability to make decisions on road speed limit information in complex road scenarios. For example, the special road scenario rule base may include special road scenario types and road speed limit information associated with those special road scenarios. Specifically, it may include default speed limits, suggested maximum speeds, warning strategies, and activation conditions (e.g., geographical range, time interval, and weather conditions). For example, for school zones: during the period from 7:00 AM to 7:00 PM, if a vehicle is located within 500 meters of the school, the default speed limit is 30 km / h; for temporary construction sections: the default speed limit is 30 km / h; for tunnels or bridges, when there are no road speed limit signs, the default speed limit is 60 km / h–80 km / h (depending on the road conditions). (Speed ratings) For severe weather, when the current weather is confirmed to be rainy, foggy, or snowy, and visibility is less than 100 meters, the default speed limit is 10km / h–20km / h; For nighttime hours, such as between 10 pm and 6 am the next day, the default speed limit is 60km / h (urban main roads) on roads without speed limit signs; For roads with sharp bends or steep slopes: these can be marked as dangerous sections on the navigation map, and the default speed limit is 30km / h–40km / h when there are no speed limit signs.
[0105] To more intuitively alert users to special road conditions, enhanced warnings can be provided via the dashboard or HUD. For example, for school zones, when entering a school zone between 7:00 AM and 7:00 PM, a flashing yellow background with black text "School Zone, Speed Limit 30" can be displayed on the dashboard or HUD, accompanied by a short, sharp audio prompt. For temporary construction zones, the construction zone boundary can be marked on the navigation map with dynamic orange wavy lines, and the HUD can overlay a "Construction 50m Ahead, Speed Limit 40" icon, along with the estimated completion time pushed by the RSU (Roadside Unit). For severe weather, a semi-transparent weather warning (e.g., heavy fog, visibility <80m) can be overlaid on the central control screen, with the speed limit highlighted in red and the frequency of voice reminders increased. Furthermore, the brightness of the HUD and central control screen can be dynamically adjusted based on ambient light sensor data: automatic brightness reduction at night with high-contrast fonts, and enhanced brightness in strong light with an anti-glare border to ensure clear visibility of information.
[0106] Considering that in practical applications, some extreme road scenarios, such as heavy rain, dense fog, strong backlight, and obscured road speed limit signs, may lead to unreliable environmental perception data, such as road speed limit information, when assessing the credibility of various data sources, the confidence score can be lowered, high-risk perception results can be avoided, and the preset speed limit value can be used instead, thus preventing erroneous speed limits and ensuring driving safety in extreme weather conditions.
[0107] To further illustrate the overall application process of the vehicle control method, the following describes the application process of the vehicle control method provided in this application dynamically through a specific driving scenario.
[0108] A vehicle equipped with the road speed limit decision system described in this application is traveling on the Beijing-Tibet Expressway. According to map data, the speed limit ahead is 120 km / h, and the confidence level of this speed limit value in the map data is 0.7, so the vehicle maintains a cruising speed of 110 km / h. Shortly after, the system identifies a temporary speed limit sign of 80 km / h placed ahead due to construction (confidence level 0.85) via the onboard camera. Simultaneously, the V2X communication module also receives the same 80 km / h speed limit information broadcast by the roadside unit (confidence level 0.9). Through consistency verification by the fusion decision unit, the final speed limit value for this section is determined to be 80 km / h within 300 milliseconds. Since the difference between the newly determined speed limit of 80 km / h and the corresponding speed limit of 120 km / h in the current map data is 40 km / h, which is much greater than the preset difference threshold (5 km / h), the system immediately triggers a map update. The new speed limit of 80 km / h is stored in the Level 1 cache and sent to the human-machine interface module. The speed limit display in the center of the vehicle's dashboard changes from 120 km / h to 80 km / h. Since the current speed is 110 km / h, exceeding the newly determined speed limit (80 km / h) by 30 km / h and exceeding the preset speed limit by 15 km / h, a Level 3 overspeed warning (severe speeding) is instantly triggered. The 80 km / h number is displayed in red, enlarged, and flashing rapidly, while a voice prompt and a continuous beeping sound are issued: "Please note, construction ahead, speed limit 80 km / h." Throughout this process, the Level 2 cache records the speed limit change data from 120 km / h to 80 km / h and its geographical location. This scenario example demonstrates how this application dynamically, accurately, and promptly responds to changes in road speed limits, and significantly improves the driver's awareness and response to speeding risks through effective tiered warnings, thereby enhancing driving safety in complex road conditions.
[0109] Exemplary cross-device vehicle control device The above text combined Figures 1-5 The method embodiments of this application have been described in detail. The vehicle control device embodiments of this application are described in detail below. It should be understood that the descriptions of the method embodiments correspond to the descriptions of the device embodiments; therefore, any parts not described in detail can be referred to the foregoing method embodiments. Figure 6 This is a schematic diagram of a system module of a vehicle control device according to some embodiments of this application.
[0110] like Figure 6 As shown, the vehicle control device 600 may include a speed limit information acquisition module 610, a speed limit information evaluation module 620, a speed limit information decision module 630, and a vehicle speed control module 640.
[0111] The speed limit information acquisition module 610 is configured to acquire road speed limit information for the same road segment from at least two independent data sources.
[0112] The speed limit information evaluation module 620 is configured to evaluate the credibility of each road speed limit information from at least two independent data sources.
[0113] The speed limit information decision module 630 is configured to determine the final road speed limit information for vehicles based on the results of a credibility assessment of each road speed limit information.
[0114] The vehicle speed control module 640 is configured to control the vehicle speed based on road speed limit information determined for the final use of the vehicle.
[0115] In some embodiments, the speed limit information evaluation module 620 may also be configured to evaluate the credibility of each road speed limit information based on at least two dimensions of source data quality, spatiotemporal consistency, and logical rationality, based on road speed limit information from at least two independent data sources.
[0116] In some embodiments, the speed limit information evaluation module 620 may also be configured to acquire additional information associated with the source data quality of the road speed limit information, wherein the additional information is used to assist in determining the accuracy of the road speed limit information from at least two independent data sources; based on the additional information, a confidence score is calculated for each road speed limit information from at least two independent data sources to achieve a confidence assessment of the road speed limit information.
[0117] In some embodiments, a road speed limit information caching module is also included, which is configured to store road speed limit information in a pre-built cache space including at least two different cache levels, wherein the timeliness and reliability of the road speed limit information stored in the cache spaces of different cache levels are different.
[0118] In some embodiments, the road speed limit information caching module is further configured to determine the timeliness and reliability of the road speed limit information, wherein the timeliness is determined based on the interval between the time when the road speed limit information is generated and the current time, and the longer the interval, the lower the timeliness; determine the level of the road speed limit information based on the timeliness and reliability of the road speed limit information; and store the road speed limit information in a cache space of the corresponding level based on the level of the road speed limit information, wherein the higher the level of the cache space, the lower the timeliness and reliability of the stored road speed limit information.
[0119] In some embodiments, a data acquisition module is also included, which is configured to acquire vehicle status information and environmental information around the vehicle in real time.
[0120] In some embodiments, a special road scene recognition module is further included, configured to recognize special road scenes based on state information and environmental information, wherein a special road scene refers to a scene that requires specific speed limit management due to specific factors, including road structure, environmental conditions or temporary events; when a special road scene is recognized, the road speed limit information of the special road scene is determined according to a pre-built special road scene rule base, wherein the special road scene rule base includes speed limit information of at least one special road scene.
[0121] In some embodiments, a warning module is further included, which is configured to acquire the current status information of the vehicle; compare the current status information of the vehicle with road speed limit information determined for the vehicle, and output the comparison result; determine the level of the overspeed warning based on the comparison result, and control the vehicle to execute the corresponding level of the overspeed warning, wherein the level of the overspeed warning includes at least two levels.
[0122] In some embodiments, a data update module is also included, configured to load the latest navigation map data after the vehicle is started or when the difference between road speed limit information obtained from at least two independent data sources exceeds a preset difference threshold.
[0123] It should be understood that specific limitations regarding the apparatus can be found in the limitations regarding the method described above, and will not be repeated here. Each module in the aforementioned apparatus can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the operations corresponding to each module.
[0124] Exemplary electronic devices and computer-readable storage media This application also provides an electronic device, such as Figure 7 As shown. The electronic device 700 provided in this application includes a memory 710, a processor 720, and an input / output interface 730. The memory 710, processor 720, and input / output interface 730 are connected via internal connection paths. The memory 710 stores instructions, and the processor 720 executes the instructions stored in the memory 710 to control the input / output interface 730 to receive input data and information, and output operation results and other data.
[0125] It should be understood that in the embodiments of this application, the processor 720 may be a general-purpose central processing unit (CPU), GPU, FPGA, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute related programs in order to implement the technical solutions provided in the embodiments of this application.
[0126] The memory 710 may include read-only memory and random access memory, and provides instructions and data to the processor 720. A portion of the processor 720 may also include non-volatile random access memory. For example, the processor 720 may also store device type information.
[0127] In implementation, each step of the above method can be completed by the integrated logic circuits in the hardware of the processor 720 or by instructions in software form. The vehicle control method disclosed in the embodiments of this application can be directly implemented by the hardware processor, or by a combination of hardware and software modules in the processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 710, and the processor 720 reads the information in memory 710 and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here. This application also provides a computer program product, including a computer program / instructions. When the computer program / instructions in the computer program product provided in this application are executed by the processor, the vehicle control method provided in this application can be implemented.
[0128] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.
[0129] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0130] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0131] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0133] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0134] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0135] It should be noted that in the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0136] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for controlling a vehicle, characterized in that, The control method includes: Obtain road speed limit information for the same road segment from at least two independent data sources; The credibility of each road speed limit information from at least two independent data sources is evaluated from at least two dimensions, and the credibility evaluation result is output. Based on the credibility assessment results of each road speed limit information, the final road speed limit information used for the vehicle is determined; The vehicle speed is controlled based on the road speed limit information ultimately used for the vehicle.
2. The vehicle control method according to claim 1, characterized in that, The credibility assessment of each road speed limit information from at least two independent data sources from at least two dimensions includes: Based on the road speed limit information from at least two independent data sources, the credibility of each road speed limit information is evaluated from at least two dimensions: source data quality, spatiotemporal consistency, and logical rationality.
3. The vehicle control method according to claim 2, characterized in that, Based on the road speed limit information from at least two independent data sources, the credibility assessment of each road speed limit information from the perspective of source data quality includes: Obtain additional information related to the quality of the source data for the road speed limit information, wherein the additional information is used to assist in determining the accuracy of the road speed limit information from at least two independent data sources; Based on the additional information, the confidence score of each road speed limit information from at least two independent data sources is calculated to assess the credibility of the road speed limit information.
4. The vehicle control method according to claim 1, characterized in that, The method further includes: The road speed limit information is stored in a pre-built cache space that includes at least two different cache levels, wherein the timeliness and reliability of the road speed limit information stored in the cache spaces of different cache levels are different.
5. The vehicle control method according to claim 4, characterized in that, The step of storing the road speed limit information into a pre-built cache space comprising at least two different cache levels includes: The timeliness and reliability of the road speed limit information are determined, wherein the timeliness is determined based on the interval between the time when the road speed limit information is generated and the current time; the longer the interval, the lower the timeliness. The level of the road speed limit information is determined based on its timeliness and reliability. Based on the level of the road speed limit information, the road speed limit information is stored in a cache space of the corresponding level. The higher the level of the cache space, the lower the timeliness and reliability of the stored road speed limit information.
6. The vehicle control method according to claim 1, characterized in that, The method further includes: The vehicle's status information and the surrounding environment information are acquired in real time. Based on the state information and the environmental information, special road scenarios are identified, wherein the special road scenario refers to a scenario that requires specific speed limit management due to specific factors, including road structure, environmental conditions or temporary events. When the special road scene is identified, the road speed limit information of the special road scene is determined according to the pre-built special road scene rule base, wherein the special road scene rule base includes speed limit information of at least one special road scene.
7. The vehicle control method according to claim 1, characterized in that, The method further includes: Obtain the current status information of the vehicle; The current status information of the vehicle is compared with the road speed limit information determined for the vehicle, and the comparison result is output. Based on the comparison results, the level of the speeding warning is determined, and the vehicle is controlled to execute the corresponding level of speeding warning. The speeding warning level includes at least two levels.
8. The vehicle control method according to claim 1, characterized in that, The method further includes: After the vehicle is started or when the difference between road speed limit information obtained from at least two independent data sources exceeds a preset difference threshold, the latest navigation map data is loaded.
9. The vehicle control method according to any one of claims 1 to 8, characterized in that, The independent data source includes at least two of the following: vehicle camera, vehicle navigation system, and vehicle communication module.
10. A vehicle, characterized in that, The vehicles include: processor; Memory used to store the processor's executable instructions. The processor is used to execute the vehicle control method according to any one of claims 1 to 9.