A multi-scene adaptive automobile instrument display control method and system

Abstract

The application discloses a kind of multi-scene adaptive automobile instrument display control method and system, belong to big data management field, including the following steps: through vehicle-mounted side data source and human-computer interaction side real-time acquisition in the running data of vehicle running process. After multi-source data are aligned, scene data stream is output, scene evidence stream is input scene potential evolution module, and different elements are mapped to different page function area.The application establishes unified decision time by time series alignment to multi-source data, identifies current driving scene, further priority calculation is carried out to information element, reaches that key driving information can be more timely presented under different scenes, solves the problem that interface layout cannot be adaptively adjusted in time according to vehicle running state and driving task demand in prior art.

Description

Technical Field

[0001] This invention relates to the field of big data management technology, and in particular to a multi-scenario adaptive automotive instrument display control method and system. Background Technology

[0002] With the continuous development of the automotive industry and the constant improvement of vehicle intelligence, the information content carried by in-vehicle display systems is becoming increasingly rich. Modern automotive web pages not only display basic vehicle information such as speed, range, tire pressure, gear position, and door status, but also enable automotive scene flow design, scene card configuration, scene publishing, and operation management, allowing users to customize the creation and management of automotive scenes. As scenes change, drivers' focus on information content, display location, display intensity, and presentation timing varies significantly. This places higher demands on the adaptability, real-time performance, and scene matching capabilities of information presentation methods during driving.

[0003] For example, Chinese Invention Patent CN116594529A discloses a human-machine interface display system for engineering machinery products. This system includes defining and classifying the display interaction functions of a truck crane based on user cognition and operational habits; optimizing the display system's operation logic and interface layout; and redesigning the interface style. Ultimately, it aims to provide a human-machine interface display system that is easy for users to operate, improves product operation efficiency, and reduces user cognitive load. The content involves adjustments to the interface functional structure, layout, and navigation methods of the truck crane display device. It also redesigns page visual graphics, button icons, drop-down menus, navigation, and other page interaction elements, ultimately improving the operational efficiency of the crane display device's human-machine interface and conforming to user operating habits.

[0004] For example, Chinese invention patent CN116909550A discloses an H5-based automotive scene customization system, which includes: a scene basic information creation module, a scene page design module, a scene card layout editing module, a scene flow design module, a scene card configuration module, a scene publishing module, a scene saving module, a scene information browsing module, a scene list management module, a scene operation manager, and a scene interaction engine. This invention provides users with the ability to customize automotive scenes. Scene definitions can trigger different vehicle atomic-level functions according to user needs and certain conditions, and execute the corresponding functions step-by-step in a set order, allowing for self-service creation of scene flow information and realizing the customization of automotive scenes.

[0005] The above-mentioned technology has at least the following technical problems: In existing technologies, one type of solution mainly optimizes the functional structure and navigation method of the display interface, lacking the ability to dynamically filter information content based on real-time driving scenarios. Another type of solution primarily improves the flexibility of scenario configuration and user customization capabilities, but lacks the ability to automatically identify the dominant scenario during vehicle operation and adaptively schedule and differentiate the display of instrument panel information accordingly. For example, vehicle speed, navigation, and auxiliary information are all fixed in their respective areas, and the positions, sizes, and display priorities of various information elements are usually preset. However, drivers' focus on information varies significantly across different driving scenarios. For instance, in high-speed driving scenarios, drivers typically pay more attention to vehicle speed, speed limit warnings, and safety alerts; in navigation-enabled driving scenarios, drivers focus more on navigation routes and turn prompts; in low-speed urban driving scenarios, drivers pay more attention to surrounding auxiliary information; and when parked, drivers focus more on obstacle distribution, vehicle status, and menu interactions. When the vehicle switches from one driving scenario to another, the fixed page layout often fails to highlight the more critical information of the current scenario, resulting in insufficient emphasis on key driving information and the display of a large amount of secondary information, thus increasing the driver's visual cognitive burden. Especially in complex traffic environments and special driving scenarios, drivers have higher requirements for the real-time performance, hierarchical structure, and adaptability of information displays. Existing vehicle instrument display systems lack a dynamic interface mapping mechanism based on driving scenarios, making it difficult to adaptively adjust the interface layout in a timely manner according to the vehicle's operating status and driving task requirements, thus affecting the recognition efficiency of driving information and the overall display effect. Summary of the Invention

[0006] To address the technical problem of existing technologies that struggle to adaptively adjust the interface layout based on vehicle operating status and driving task requirements, this invention provides a multi-scenario adaptive automotive instrument display control method and system. The technical solution is as follows: On the one hand, a multi-scenario adaptive automotive instrument display control method is provided, which includes: S1 collects real-time operating data during vehicle operation through on-board data sources and human-machine interaction, and records the data collection time, current value, and data confidence level.

[0007] S2 constructs a unified decision time based on the current time, manages big data, projects each data point to the unified decision time, and obtains a time-aligned data set.

[0008] S3 generates evidence streams for each candidate scenario based on the time-aligned data set.

[0009] S4 performs scene potential energy evolution on the generated candidate scene evidence streams and outputs the dominant scene.

[0010] S5. Establish an information element library, configure attributes for each information element, and calculate the priority of each information element according to the current dominant scenario.

[0011] S6 generates execution results based on priority calculations, maps information elements to different areas, and uses different visual enhancement methods for different types of information.

[0012] S7: When multiple high-priority events occur simultaneously and the main field of view resources are insufficient, conflict arbitration is performed. S8 extracts feedback from the running results and adjusts the parameters of the preceding stage.

[0013] On the other hand, a multi-scenario adaptive automotive instrument display control system is provided, which includes: a data acquisition module, a unified decision-making moment construction module, a scene evidence flow generation module, a scene potential energy evolution module, an information element priority calculation module, a layout adjustment module, a conflict arbitration module, and a feedback adjustment module.

[0014] The data acquisition module is used to collect real-time operating data of the vehicle during operation through the on-board data source and the human-machine interaction side, and to record the data acquisition time, current value and data confidence level.

[0015] The unified decision-making moment construction module is used to construct a unified decision-making moment based on the current moment, manage big data, project each data point to the unified decision-making moment, and obtain a time-aligned data set.

[0016] The scene evidence stream generation module is used to generate each candidate scene evidence stream based on the time-aligned data set.

[0017] The Scene Potential Energy Evolution module is used to perform scene potential energy evolution on the generated candidate scene evidence streams and output the dominant scene.

[0018] The information element priority calculation module is used to establish an information element library, configure attributes for each information element, and calculate the priority of each information element according to the current dominant scenario.

[0019] The layout adjustment module is used to generate running results based on priority calculations, map information elements to different areas, and use different visual enhancement methods for different types of information.

[0020] The conflict arbitration module is used to perform conflict arbitration when multiple high-priority events occur simultaneously and the main view area resources are insufficient.

[0021] The feedback adjustment module is used to extract feedback quantities from the running results and adjust the parameters of the preceding stage.

[0022] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. The present invention provides a multi-scenario adaptive automotive instrument display control method and system method, which constructs a unified decision time based on the current system time, manages big data, projects each data to the unified decision time, thereby obtaining a data set with consistent time sequence, and realizes the unified mapping of input information of various data types to the unified decision time, effectively solving the problem in the prior art that the system merges information from different time points as information from the same time, thus causing erroneous judgments.

[0023] 2. This invention generates multiple candidate scene evidences by using a time-aligned data set, inputs them into the scene potential energy evolution module, and outputs the dominant scene and driving task. This enables continuous accumulation, decay, and maintenance of scene evidence, allowing scene entry and exit to have a continuous change process, avoiding the system being too sensitive to short-term disturbances, and improving the continuity of scene switching.

[0024] 3. By extracting feedback from the system operation results, the data alignment and scene potential energy parameters are adjusted in reverse, thereby achieving a synergistic technical effect of reducing false handovers and handover jitter, enabling the entire system to be continuously updated. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of a multi-scenario adaptive automotive instrument display control method provided in this application embodiment; Figure 2 A structural diagram of a multi-scenario adaptive automotive instrument display control system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the automotive instrument display control provided in an embodiment of this application; Figure 4 This is a schematic diagram of the arbitration feedback process for instrument display control provided in an embodiment of this application. Detailed Implementation

[0027] The following provides explanations of some terms used in this application. It should be noted that these explanations are for the convenience of those skilled in the art and do not constitute a limitation on the scope of protection claimed in this application.

[0028] The embodiments of this application involve at least one, including one or more; where "multiple" means two or more. Furthermore, it should be understood that in the description of this specification, terms such as "first," "second," and "third" are used only for descriptive purposes and should not be construed as indicating relative importance or order. For example, "first device" and "second device" do not represent the degree of importance of the two or their order, but are merely for descriptive distinction. In the embodiments of this application, "and / or" merely describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] The directional terms mentioned in the embodiments of this application, such as "up", "down", "left", "right", "inner", and "outer", are only for reference to the directions in the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation and understanding of the embodiments of this application, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0030] References to "one embodiment," "in some examples," or "some embodiments" as described in the embodiments of this application mean that one or more embodiments of this specification include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in some examples," "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0031] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0032] like Figure 1 The diagram shown is a flowchart of a dynamic interface mapping mechanism method for a vehicle instrument display system based on a driving scenario, provided in Embodiment 1 of this application. This embodiment of the application provides a multi-scenario adaptive vehicle instrument display control method, which includes the following steps: S1, as Figure 3The diagram shown is a structural schematic of the automotive instrument display control provided in this embodiment of the application. The structure mainly includes an on-board data source, a control and computing layer, a display screen, and a human-machine interface (HMI) side. The on-board data source is used to acquire vehicle operation-related information in real time; the HMI side is used to acquire driver interaction information; the control and computing layer is used to process various input data uniformly and generate page control results; and the display screen is used to present the corresponding instrument display page.

[0033] The vehicle-side data sources include vehicle status sensors, environmental perception sensors, and a navigation and map module. Vehicle status sensors collect vehicle operating status data, including vehicle speed and lateral velocity; environmental perception sensors collect environmental perception data, including the distance and relative speed between the current vehicle and the vehicle in front; the navigation and map module obtains navigation data, including remaining lateral distance and remaining turning distance; and the human-machine interface acquires driver interaction data, including touch interaction data and button interaction data. Each data type i and the collection time are recorded. The current value and data confidence level are stored in a unified cache and processed using the control and computational layer. , where n is the total number of data types.

[0034] S2, when processing vehicle operating status data, environmental perception data, navigation data, and driver interaction data, suffers from different sampling decision cycles and processing delays. Therefore, the system does not directly use the latest arriving data as the current state, but instead constructs a unified decision timeline. Perform time-series alignment on the data. First, obtain the latency information of each data source, and then align the data according to the recorded collection time. Calculate its time delay relative to the current time.

[0035] , in For the current system time, This represents the time delay relative to the current moment, and k represents the decision period, k=1,2,3 X, where X is the total number of cycles. Based on the distance between the current vehicle and the vehicle in front collected by S1. Relative velocity Horizontal remaining distance lateral velocity Remaining turning distance and vehicle speed Calculate the forward collision time TTC(k), lane departure time remaining TLC(k), and navigation steering time remaining. If the denominator is close to 0, then use Use positive numbers instead to avoid zero errors.

[0036] , , , Further take the most pressing time scale at present : , Based on the latency of multi-source data and the current risk timescale, determine the review duration corresponding to the unified decision-making moment.

[0037] =max( ), , , in This represents the review time required to align multi-source data. 'c' is a safety ratio coefficient, which adds an upper limit constraint to the alignment time; 'c' is greater than 0 and less than 1. To standardize the review duration for each decision-making moment, To ensure consistent decision-making, This is the current system time.

[0038] S3. The system first maps multi-source asynchronous data to the same time plane based on a unified decision time to obtain a time-aligned data set, which serves as the basic input for generating candidate scene evidence streams. The system then extracts forward collision time, lane departure time remaining time, and navigation steering time remaining time from the time-aligned data set to form a criterion set, which is used to make a preliminary judgment on the scene.

[0039] , in Let be the set of criteria for the j-th candidate scenario within the k-th decision period, where j represents different candidate scenarios, j=1,2,3...N, and N is the total number of candidate scenarios. For the first One criterion, =1, 2, 3...m, where m is the total number of criteria. Normalize each criterion, denoted as . .

[0040] Calculate the scene evidence strength for the j-th candidate scene: , in Let J be the scene evidence strength of the j-th candidate scene within the k-th decision period. For the first The weights of each criterion for candidate scenario j are set according to the importance of the criterion, and the sum of the criterion weights is equal to one.

[0041] , , in Let be the confidence level of the scenario evidence for the j-th candidate scenario within the k-th decision period. As a saturation function, the result is restricted to [0,1]. The preset consistency penalty coefficient, Let the strength of evidence for the j-th candidate scene be denoted as . This represents the degree of inconsistency among multiple criteria for the j-th candidate scenario within the k-th decision period. The larger the value, the greater the difference in the criteria within the scenario, indicating that the scenario criteria are unstable; The smaller the value, the more consistent the various criteria will be in supporting the scenario. The number of key inputs or criteria corresponding to the j-th candidate scenario.

[0042] , , , in Let be the time freshness of the scene evidence for the j-th candidate scene within the k-th decision period. For the j-th candidate scenario The data collection time corresponding to each criterion To collect the j-th candidate scene The time deviation of each criterion For the j-th candidate scenario The freshness of a criterion For the j-th candidate scenario The time decay coefficient of the criterion, based on the j-th candidate scenario. Each criterion sets a sensitivity to time lag. The more sensitive the criterion is to time lag, the larger the corresponding time decay coefficient, and the faster the freshness decreases.

[0043] = , , in This represents the trend of scene evidence changes for the j-th candidate scene within the k-th decision period. When the value is greater than 0, the scene is enhanced. When the value is less than 0, the effect of this scenario is diminishing. When the value is approximately 0, the scenario is basically stable; Let be the set of scene evidence streams for the j-th candidate scene.

[0044] S4, the system inputs the scene evidence stream set into the scene potential energy evolution module, and defines the scene potential energy for each candidate scene j. ∈[0,1], using a first-order inertial discretization model: , in Let τ be the potential energy value of the j-th candidate scenario within the k-th decision period. j Let be the evolution time constant of the j-th candidate scenario, which is the time it takes for the non-scenario state to enter the scenario-established state. The interval between two adjacent decision cycles. This is the comprehensive driving force calculated based on the scene evidence flow. , , , Weights are assigned to the scene evidence strength, scene evidence confidence, scene evidence freshness, and scene evidence change trend, proportionally based on the data confidence identifier, with a sum of 1. If the potential energy value of the j-th candidate scene in the current decision cycle is greater than or equal to the preset entry threshold for the j-th candidate scene and its duration exceeds the preset confirmation time, the scene is entered and designated as the dominant scene. If the potential energy value of the j-th candidate scene at the current moment is greater than or equal to the preset entry threshold for the j-th candidate scene but its duration does not exceed the preset confirmation time, or if the potential energy value of the j-th candidate scene at the current moment is less than the preset exit threshold for the j-th candidate scene but has met the preset minimum dwell time, the current scene is maintained. If the potential energy value of the j-th candidate scene at the current moment is less than the preset exit threshold for the j-th candidate scene and has met the preset minimum dwell time, the scene is exited, and the threshold is set within the potential energy range corresponding to the boundary. The system outputs the current dominant scene.

[0045] The system establishes an information element library: vehicle speed, speed limit, forward collision warning, rear collision warning, navigation route, remaining distance, turn arrow, lane-level prompts, surrounding obstacle prompts, pedestrian or non-motorized vehicle prompts, following distance, range, energy consumption, tire pressure, door or trunk status, interactive menu, and non-critical reminders. The system configures for each information element: safety level, scene relevance, task relevance, timeliness level, recommended display area, and minimum display area.

[0046] S5 calculates the priority of each information element based on the current scenario.

[0047] , , , , , in Let be the comprehensive priority value of the s-th information element in the k-th decision cycle. For security levels and for the inherent importance of information elements, Let the relevance of the s-th information element in the k-th decision cycle be denoted as . Let the s-th information element be the task urgency in the k-th decision cycle. Let the timeliness level of the s-th information element be determined in the k-th decision cycle. Let the s-th information element be the current risk level in the k-th decision cycle. As a weight for security level, As a weight for scene relevance, As a weight for the urgency of the task, As a weight for timeliness level, The weights are for the current risk level, where each item is normalized to [0,1]. The weights are set according to the current displayed priority; the higher the priority, the higher the weight. It is the preset relevance of the s-th information element to the j-th candidate scene; This is the current driving task in the k-th decision cycle. It is the current task relevance of the s-th information element, and its preset relevance relative to the current driving task q is taken; It represents the remaining valid time of the event corresponding to the s-th information element. It is the reference time threshold for the s-th information element; This involves uniformly mapping the risk physical quantity corresponding to the s-th information element to a risk level ranging from 0 to 1. 0 indicates that there is currently no risk or very low risk. 1 indicates that the current risk is high, s=1, 2, 3...M, where M is the total number of information elements.

[0048] Based on the calculation results, information priority results for the current scenario are generated. If the comprehensive priority value of the s-th information element in the k-th decision period is greater than or equal to the preset high-priority threshold, then the information element is determined to be of high priority in that decision period. If the comprehensive priority value of the s-th information element in the k-th decision period is less than the preset high-priority threshold but greater than the preset low-priority threshold, then the information element is determined to be of medium priority in that decision period. If the comprehensive priority value of the s-th information element in the k-th decision period is less than or equal to the preset low-priority threshold, then the information element is determined to be of low priority in that decision period. The preset high-priority threshold is greater than the preset low-priority threshold, and both are set based on historical driving data statistics, information element priority distribution, or system preset strategies.

[0049] S6, In this embodiment, the vehicle instrument display area includes a main view core area, a task assistance area, a status monitoring area, a temporary enhanced prompt area, and a secondary hidden area. Information elements are placed within the functional areas according to the perceived intensity of the elements and the needs of each functional area.

[0050] First, calculate the intensity of the perceived demand for information elements. ,in It is the minimum display area required for an information element. Let be the comprehensive priority of the s-th information element in the k-th decision cycle. This represents the remaining valid time for the event corresponding to the s-th information element. Higher priority means greater demand; larger required area means greater demand; shorter remaining time means greater demand. Next, if the visual access time for a functional area exceeds the remaining valid time for the event corresponding to the s-th information element, then that area cannot be placed there. Finally, based on the sum of the perceived demand intensity of information elements within each functional area, the demand for each functional area is calculated. Then, the total display area is allocated to each zone as needed. When the remaining effective time of the event corresponding to the s-th information element is less than the short-term emergency threshold, the position is no longer allocated gradually, but a strong reminder is given directly. The main view core area displays key information that the driver must prioritize in the current scenario; the task assistance area displays information that is closely related to the current driving task but has a slightly lower urgency; the status monitoring area displays general status information such as range, tire pressure, energy consumption, and door status; the temporary enhanced reminder area displays short-term strong reminder information such as warnings and navigation approaching a turn; and the secondary hidden area contains information that can be collapsed or weakened in the current scenario.

[0051] S7. Different types of information employ different visual enhancement methods. Based on the perceptual demand intensity of the computational element, the higher the priority, the more enhancement is required; the larger the recognition area requirement, the more enhancement is required; and the shorter the remaining available time, the more enhancement is required.

[0052] S8, such as Figure 4The diagram shown illustrates the flow chart of the instrument display control arbitration feedback provided in this application embodiment. When multiple high-priority messages appear simultaneously, and the area of ​​the main viewing area is smaller than the sum of the minimum display area required to clearly see the s-th message, conflict arbitration is performed. This is based on the security risk level. urgency of the task Remaining effective time window Relevance to the scene To conduct arbitration, a conflict arbitration scoring model is established. Specifically, this model involves... Where a1, a2, a3, and a4 are weighting coefficients corresponding to safety risk level, task urgency, remaining effective time window, and scene relevance, respectively. Each weighting coefficient is greater than 0, and the sum of the weighting coefficients is 1. The final output is an arbitration score, with higher scores indicating higher priority. In this embodiment, when a forward collision warning and a navigation steering prompt appear simultaneously, the forward collision warning is displayed first; when a navigation steering prompt and a battery life reminder appear simultaneously, the navigation steering prompt is displayed first.

[0053] S9 extracts feedback from the execution results and adjusts the safety review time. It also extracts alignment errors from the execution log. .according to Formula callback safe review time, where It involves adjusting the step size. A large alignment error indicates that different sources have not yet been pulled to the same time plane, so the review time should be increased appropriately. If the alignment error is small, it can be maintained or reduced.

[0054] This embodiment also provides a multi-scenario adaptive automotive instrument display control system, such as... Figure 2 The diagram shown is a structural diagram of a multi-scenario adaptive automotive instrument display control system. The system includes: a data acquisition module, a unified decision-making moment construction module, a scene evidence flow generation module, a scene potential energy evolution module, an information element priority calculation module, a layout adjustment module, a conflict arbitration module, and a feedback adjustment module.

[0055] The data acquisition module is used to collect real-time operating data of the vehicle during operation through the on-board data source and the human-machine interaction side, and to record the data acquisition time, current value and data confidence level.

[0056] The unified decision-making moment construction module is used to construct a unified decision-making moment based on the current moment, manage big data, project each data point to the unified decision-making moment, and obtain a time-aligned data set.

[0057] The scene evidence stream generation module is used to generate each candidate scene evidence stream based on the time-aligned data set.

[0058] The Scene Potential Energy Evolution module is used to perform scene potential energy evolution on the generated candidate scene evidence streams and output the dominant scene.

[0059] The information element priority calculation module is used to establish an information element library, configure attributes for each information element, and calculate the priority of each information element according to the current dominant scenario.

[0060] The layout adjustment module is used to generate running results based on priority calculations, map information elements to different areas, and use different visual enhancement methods for different types of information.

[0061] The conflict arbitration module is used to perform conflict arbitration when multiple high-priority events occur simultaneously and the main view area resources are insufficient.

[0062] The feedback adjustment module is used to extract feedback quantities from the running results and adjust the parameters of the preceding stage.

[0063] Example 2: Based on Example 1, prioritize local expansion, followed by overall rearrangement. Many conflicts are instantaneous events, not a complete change to the entire driving scenario, and overall rearrangement is more likely to cause jitter, so frequent template changes for the entire interface should be avoided. Local expansion can prioritize highlighting urgent or critical information while maintaining background layout stability, thereby shortening the display response chain for high-risk events. Only when a local event persists and causes a substantial change in the current driving task or background scenario will the system further perform overall rearrangement, preventing resource waste and ensuring timely display of high-risk events while improving overall page stability and driving information recognition efficiency. Main view occupancy rate: , in This represents the minimum area required for high-priority information. This determines the minimum display area required to clearly see the s-th piece of information. When the field of view occupancy is less than 1, normal mapping occurs; when the field of view occupancy is greater than 1 and the duration is less than the short-term duration threshold, it can be determined that the current short-term field of view is congested, rather than the long-term main field of view is insufficient, and the temporary enhanced prompt area is expanded first; when the field of view occupancy is greater than 1 and the duration is long, the overall partition ratio is allowed to be adjusted; if the urgency is high, low priority elements are compressed first, rather than full-screen rearrangement.

[0064] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)). Where there is no conflict, the solutions in the above embodiments can be used in combination.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope and intent of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. A multi-scenario adaptive automotive instrument display control method, characterized in that, Includes the following steps: S1 collects real-time operating data of the vehicle during operation through the vehicle-side data source and the human-machine interaction side, and records the data collection time, current value and data confidence level. S2, construct a unified decision time based on the current time, manage big data, project each data point to the unified decision time, and obtain a time-aligned data set; S3, generate evidence streams for each candidate scenario based on the time-aligned data set; S4, perform scene potential energy evolution on the generated candidate scene evidence streams, and output the dominant scene; S5. Establish an information element library, configure attributes for each information element, and calculate the priority of each information element according to the current dominant scenario. S6 generates execution results based on priority calculations, maps information elements to different areas, and uses different visual enhancement methods for different types of information. S7: When multiple high-priority events occur simultaneously and the main field of view resources are insufficient, conflict arbitration is performed. S8 extracts feedback from the running results and adjusts the parameters of the preceding stage.

2. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: The process involves real-time collection of vehicle operation data from both the on-board data source and the human-machine interface, recording the data collection time, current value, and data confidence level. The specific process is as follows: The operational data includes vehicle operational status data, environmental perception data, navigation data, and driver interaction data. Each data type, collection time, current value, and data confidence level are recorded and stored in a unified cache area for processing using the control and computing layer. The vehicle operating status data includes vehicle speed and lateral speed; The environmental perception data includes the current distance and relative speed between the vehicle and the vehicle in front; The navigation data includes the remaining lateral distance and the remaining turning distance; The driver's interaction data includes touch interaction data and button interaction data.

3. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: The process of constructing a unified decision-making timeline based on the current time, managing big data, and projecting each data point onto the unified decision-making timeline to obtain a time-aligned data set is as follows: Based on the recorded collection time, the time delay relative to the current moment is calculated. Based on the collected vehicle operation status data, environmental perception data, navigation data, and driver interaction data, the forward collision time, lane departure time, and navigation steering time are calculated. The most urgent time scale is selected. Based on the time delay of multi-source data relative to the current moment and the current risk time scale, the review duration corresponding to the unified decision moment is determined.

4. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: The process of generating evidence streams for each candidate scenario based on the time-aligned data set is as follows: The time-aligned dataset serves as the basic input for generating candidate scene evidence streams. Forward collision time, lane departure time remaining time, and navigation steering time remaining time are extracted from the time-aligned dataset to form intermediate criteria. Each criterion is normalized, and scene evidence streams are generated based on the criteria. The scene evidence stream includes at least the scene evidence strength, scene evidence confidence, scene evidence time freshness, and scene evidence change trend.

5. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: The process of establishing an information element library, configuring attributes for each information element, and calculating the priority of each information element based on the current dominant scenario is as follows: Within a decision-making cycle, for an information element, the security level is calculated based on static basic weights; Calculate scene relevance based on scene confidence level; Calculate the urgency of the task based on the current driving task; The timeliness level is calculated based on the remaining valid time and a reference time threshold; The current risk level is calculated based on the mapping function, normalized, and assigned weights to determine the overall priority of the information element in the current decision-making cycle. Iterate through each information element to obtain the comprehensive priority of each information element in the current decision-making cycle; By continuously monitoring according to a preset decision cycle, the comprehensive priority of each information element in each decision cycle can be obtained.

6. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: When multiple high-priority events occur simultaneously and the main field of view resources are insufficient, conflict arbitration is performed, and the specific process is as follows: If the overall priority of a certain information element in a certain decision-making cycle is greater than or equal to the preset high priority threshold, then the information element is determined to be of high priority in that decision-making cycle. If the overall priority of a certain information element in a certain decision-making cycle is less than the preset high priority threshold but greater than the preset low priority threshold, then the information element is determined to be of medium priority in that decision-making cycle. If the overall priority of a certain information element in a certain decision-making cycle is less than or equal to the preset low priority threshold, then the information element is determined to be of low priority in that decision-making cycle.

7. The multi-scenario adaptive automotive instrument display control method as described in claim 6, characterized in that: The provision regarding conflict arbitration when multiple high-priority events occur simultaneously and the main view area resources are insufficient also includes: If multiple high-priority elements appear simultaneously within a certain decision-making cycle and the area of ​​the main visual field is less than the sum of the minimum display areas required for the information elements, conflict arbitration shall be performed. The arbitration result is obtained by normalizing the safety risk level, task urgency, remaining effective time window, current driving scenario, and driver attention area and assigning their respective weights.

8. The multi-scenario adaptive automotive instrument display control method as described in claim 1, characterized in that: The specific process of extracting feedback from the running results and adjusting the preceding parameters is as follows: Read the current running log data stream, and accurately capture and extract the alignment error at the current moment by parsing the data in the log; After extracting the alignment error, the callback algorithm is activated, and the adjustment step size is introduced as the gain coefficient. The safe playback time is updated according to the recursive formula.

9. A multi-scenario adaptive automotive instrument display control method as described in any one of claims 1-8, characterized in that: The main view occupancy rate is introduced as a core decision indicator. When the main view occupancy rate is at a safe threshold and the duration is short, the overall layout structure is not disrupted. When the main view occupancy rate is at a safe threshold and the duration is long, the temporary enhanced prompt area is expanded first. When the main view occupancy rate is greater than the safe threshold and the duration is long, the overall rearrangement is further executed.

10. A system applying the multi-scenario adaptive automotive instrument display control method as described in any one of claims 1-8, characterized in that, include: The module includes: data acquisition module, unified decision-making moment construction module, scene evidence flow generation module, scene potential energy evolution module, information element priority calculation module, layout adjustment module, conflict arbitration module, and feedback adjustment module. The data acquisition module is used to collect real-time operating data of the vehicle during operation through the vehicle-side data source and the human-machine interaction side, and record the data acquisition time, current value and data confidence level identifier. The unified decision-making moment construction module is used to construct a unified decision-making moment based on the current moment, manage big data, project each data point to the unified decision-making moment, and obtain a time-aligned data set. The scene evidence stream generation module is used to generate each candidate scene evidence stream based on the time-aligned data set. The scene potential energy evolution module is used to perform scene potential energy evolution on the generated candidate scene evidence streams and output the dominant scene. The information element priority calculation module is used to establish an information element library, configure attributes for each information element, and calculate the priority of each information element according to the current dominant scenario. The layout adjustment module is used to generate running results based on priority calculation, map information elements to different areas, and use different visual enhancement methods for different types of information. The conflict arbitration module is used to perform conflict arbitration when multiple high-priority events occur simultaneously and the main view area resources are insufficient. The feedback adjustment module is used to extract feedback quantities from the running results and adjust the preceding parameters.

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