An adaptive control method for the openness of AR-HUD display area

Abstract

This invention relates to the field of vehicle control technology, and specifically to an adaptive control method for the openness of an AR-HUD display area. The method involves: determining the road scene where the target vehicle is located based on map information; determining the basic layout template of the AR-HUD display area based on the road scene; determining the complexity of the external environment based on environmental perception information; determining the vehicle's operating status correction result based on vehicle operating status information; determining the display shrinkage level of the AR-HUD display area based on the external environment complexity and the vehicle's operating status correction result; determining the openness of the basic layout template based on the display shrinkage level; determining the final load of the AR-HUD system based on the external environment complexity, AR-HUD display information, vehicle operating status correction result, and driver status information; and displaying information for the current control cycle on the basic layout template with the determined openness based on the final load.

Description

Technical Field

[0001] This specification relates to the field of vehicle control technology, and in particular to an adaptive control method for the openness of AR-HUD display area. Background Technology

[0002] With the rapid development of intelligent connected vehicles and autonomous driving technologies, Augmented Reality Head-Up Displays (AR-HUDs), as an important carrier of human-computer interaction, have been widely used in mass-produced vehicles. AR-HUDs can integrate and display key information such as navigation guidance, safety prompts, driver assistance status, and vehicle status within the driver's forward field of vision, which is of great significance for improving driving safety and driving experience.

[0003] However, existing mass-produced AR-HUD products typically employ fixed display areas and layout rules, or only support users to manually select display modes in advance. During vehicle operation, the display layout generally does not dynamically adjust in sync with road structure, traffic conditions, weather visibility, vehicle operating status, and driver status. This static or semi-static display strategy is ill-suited to complex and ever-changing driving scenarios. For example, in densely interactive scenarios such as urban intersections and merging points, or in high-speed continuous traffic scenarios, using the same layout for extended periods can lead to non-critical information occupying excessive display space, unstable areas for critical information, or forcing drivers to repeatedly search for information locations, increasing cognitive load.

[0004] Therefore, this specification provides an adaptive control method for the openness of the AR-HUD display area. Summary of the Invention

[0005] This specification provides an adaptive control method for the openness of the AR-HUD display area to partially solve the aforementioned problems existing in the prior art.

[0006] The following technical solution is adopted in this specification: This specification provides an adaptive control method for the openness of the AR-HUD display area, including: S1. Acquire environmental perception information, vehicle operating status information, driver status information, map information, and AR-HUD display information for the target vehicle in the current control cycle; S2. Based on the map information, determine the road structure-related features of the road where the target vehicle is located, and based on the road structure-related features, determine the road scene where the target vehicle is located; S3. Determine the basic layout template of the AR-HUD display area based on the road scene where the target vehicle is located; S4. Based on the environmental perception information, determine the complexity of the external environment of the target vehicle; and based on the vehicle operating status information, determine the vehicle operating status correction result of the target vehicle. S5. Determine the display shrinkage level of the AR-HUD display area based on the complexity of the external environment and the correction results of the vehicle's operating status; S6. Determine the openness of the basic layout template based on the displayed shrinkage level; S7. Determine the visual cognitive load based on the complexity of the external environment and the AR-HUD display information; S8. Determine the base load of the AR-HUD system based on the visual cognitive load and the vehicle operating status correction results; S9. Determine the final load of the AR-HUD system based on the basic load and the driver status information; S10. Based on the final load, display the information of the current control cycle on the basic layout template with the determined openness.

[0007] Based on the aforementioned technical means, this solution achieves dynamic closed-loop adjustment of the AR-HUD display area's openness and information load by integrating multi-dimensional information such as road scene, environmental complexity, vehicle status, and driver status. It dynamically selects a basic layout template based on scene recognition using maps and road structures. This allows the arrangement of AR virtual images to match the current visual focus of the road, avoiding the rigidity of display area openness caused by fixed layouts, and improving the adaptability of AR-HUD display control to different driving tasks through scene-based display layout. The display shrinkage level is dynamically calculated based on the complexity of the external environment and vehicle operating status, adjusting the openness of the basic template accordingly. Under complex conditions, the display range is automatically reduced, decreasing the area of ​​non-critical information. This effectively reduces the occlusion of the real road surface and visual noise by AR graphics when the driver needs to be highly focused, significantly improving driving safety. Adaptive display area shrinkage reduces visual interference. The "visual cognitive load" is estimated through environmental information and the content to be displayed, and then the final load that the driver can withstand is assessed in conjunction with vehicle status and driver status. This allows the system to proactively reduce decorative and non-urgent information when the driver is under high load, retaining only the most critical driving data. It can provide rich information under low load, realizing the leap from displaying the full amount to supplying according to processing capacity, avoiding the response delay caused by cognitive overload, and providing intelligent information filtering based on cognitive load to prevent information overload.

[0008] Furthermore, the map information includes the road scene orientation of the target vehicle's location, the number of intersection nodes, the number of intersection node connection directions, the number of fork nodes, the number of merging nodes, the number of consecutive fork-merging nodes, the number of lane number changes, the number of lane function changes, and the statistics of ramp entry and exit connections; the road scene orientation includes urban road orientation and highway orientation. S2 specifically includes: The characteristics of the intersection nodes and their connection directions are determined based on the number of intersection nodes and the number of connection directions of the intersection nodes. The bifurcation and merging characteristics are determined based on the number of bifurcation nodes, the number of merging nodes, and the number of consecutive bifurcation and merging nodes. Based on the statistics of ramp entry and exit connections, the ramp connection characteristics are determined; Based on the number of changes in the number of lanes and the number of changes in lane function, lane change characteristics are determined; The road scene where the target vehicle is located is determined based on the intersection node and connection direction features, the bifurcation and merging features, the ramp connection features, the lane change features, and the road scene orientation.

[0009] Furthermore, the basic layout template for the AR-HUD display area includes the basic display area attributes, the importance level of the area display, and the order of collapsible areas for each display area; the basic display area attributes include the reference anchor area, the allowed area, the restricted area, and the disabled area.

[0010] Furthermore, the environmental perception information includes the number of identified targets, the number of traffic participant categories, environmental visibility, the frequency of entry events, and the frequency of lateral movement changes of targets; S4 determines the complexity of the external environment of the target vehicle based on the environmental perception information, specifically including: Based on the number of identified targets, determine the target density ahead; Determine the target category diversity based on the number of traffic participant categories; The complexity of traffic interaction is determined based on the frequency of the intrusion events and the frequency of the target's lateral movement changes. The complexity of the external environment of the target vehicle is determined based on the density of the target ahead, the diversity of the target categories, the environmental visibility, and the complexity of the traffic interaction.

[0011] Furthermore, the vehicle operating status information includes vehicle speed data, braking input data, steering wheel angle change rate, and steering wheel correction times; S4 determines the vehicle operating status correction result of the target vehicle based on the vehicle operating status information, specifically including: Based on the vehicle speed data, determine the speed change result; Based on the braking input data, determine the braking activity level; The steering correction activity is determined based on the steering wheel angle change rate and the number of steering wheel corrections. Based on the speed change result, the braking activity, and the steering correction activity, the vehicle operating state correction result of the target vehicle is determined.

[0012] Furthermore, the complexity of the external environment includes low complexity, medium complexity, and high complexity; the vehicle operating state correction result includes stable state, slightly corrected state, and enhanced corrected state; the display shrinkage level includes low shrinkage level, medium shrinkage level, and high shrinkage level.

[0013] Furthermore, S6 specifically includes: The displayable area of ​​each display area in the basic layout template is determined based on the display shrinkage level, the region display importance level, and the order of the shrinkable regions.

[0014] Furthermore, the AR-HUD display information includes the number of information items to be displayed, the number of information categories, and the information distribution density in the open area; S7 specifically includes: The display information density result is determined based on the quantity of information to be displayed, the quantity of information categories, and the information distribution density of the open area; The visual cognitive load is determined based on the displayed information density results and the complexity of the external environment.

[0015] Furthermore, S10 specifically includes: Determine the information priority of each piece of information to be displayed; Information is displayed in the displayable area according to the information priority of each piece of information to be displayed and the final load.

[0016] Furthermore, S2 also includes step S21: Determine whether the current control cycle is consistent with the road scene of the target vehicle determined in the previous preset number of control cycles; If so, then S3 is executed based on the road scenario where the target vehicle is located, as determined in the current control cycle; If not, then S3 is executed based on the road scenario where the target vehicle is located in the previous control cycle of the current control cycle.

[0017] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This solution achieves dynamic closed-loop adjustment of the AR-HUD display area's openness and information load by integrating multi-dimensional information such as road scene, environmental complexity, vehicle status, and driver status. Based on map and road structure recognition, a basic layout template is dynamically selected. This allows the arrangement of AR virtual images to match the current visual focus of the road, avoiding the rigidity of display area openness caused by fixed layouts. Contextualized display layout improves the adaptability of AR-HUD display control to different driving tasks. The display shrinkage level is dynamically calculated based on the complexity of the external environment and vehicle operating status, adjusting the openness of the basic template accordingly. Under complex conditions, the display range is automatically reduced, decreasing the area of ​​non-critical information. This effectively reduces AR graphics occlusion and visual noise on the real road surface when the driver needs to be highly focused, significantly improving driving safety. Adaptive display area shrinkage reduces visual interference. The "visual cognitive load" is estimated through environmental information and the content to be displayed, and then the final load that the driver can withstand is assessed by combining vehicle status and driver status. This allows the system to proactively reduce decorative and non-urgent information when the driver is under high load, retaining only the most critical driving data. It can provide rich information under low load, realizing the leap from displaying the full amount to supplying according to processing capacity, avoiding the response delay caused by cognitive overload, and providing intelligent information filtering based on cognitive load to prevent information overload. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating an adaptive control method for the openness of an AR-HUD display area provided in an embodiment of this specification; Figure 2 This specification provides a corresponding Figure 1 A schematic diagram of the structure of an electronic device. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0020] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0021] With the rapid development of intelligent connected vehicles and autonomous driving technology, AR-HUD, as an important carrier of human-computer interaction, is widely used in mass-produced vehicles.

[0022] Current mass-produced AR-HUD products typically employ fixed display areas and layout rules, or only support users to manually select display modes in advance. During vehicle operation, the display layout generally does not dynamically adjust in sync with road structure, traffic conditions, weather visibility, vehicle operating status, and driver status. This static or semi-static display strategy has the following main drawbacks: First, fixed layouts are difficult to adapt to complex and ever-changing driving scenarios. For example, in densely interactive scenarios such as urban intersections, merging intersections, and high-speed continuous traffic, using the same layout for a long time can easily lead to non-critical information occupying too much display space, unstable areas for critical information, or forcing drivers to repeatedly search for information locations, increasing cognitive load. Second, relying solely on manual user switching cannot respond promptly to real-time changes in operating conditions. When the vehicle enters congested areas, ramps, construction zones, or low-visibility environments, if the openness of the display space and the information intensity remain unchanged, the displayed content will change according to the current driving task. Third, although some solutions can adjust information based on a single environmental parameter, they usually do not decouple and distinguish between the scene layer, display space layer, and information layer, which easily leads to confusion between "available area" and "information display method". It is difficult to form a stable layout change path and cannot ensure that the information layer control always obeys the display space boundary. Fourth, due to the differences in the update frequency and effectiveness of multi-source data, and the lack of constraint mechanisms such as continuous confirmation, minimum holding time, and gradual recovery, scene determination and layout switching are easily affected by map lag, perception fluctuations, or repeated triggering of critical thresholds, resulting in frequent changes in display layout, which seriously affects visual stability and driving comfort.

[0023] Therefore, this specification provides an adaptive control method for the openness of the AR-HUD display area.

[0024] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0025] Figure 1A flowchart illustrating an adaptive control method for the openness of an AR-HUD display area, provided in an embodiment of this specification, includes the following steps: S1: Acquire environmental perception information, vehicle operating status information, driver status information, map information, and AR-HUD display information for the target vehicle during the current control cycle.

[0026] This specification describes the process of adaptive control of the AR-HUD display area openness. In the embodiments described herein, this AR-HUD system can execute this process. However, this specification does not limit the type of device or platform used to implement this AR-HUD display area adaptive control process; for example, a personal computer, mobile terminal, or other devices or platforms can also be used. For ease of description, the AR-HUD system will be used as the executing entity in the following description.

[0027] In one or more embodiments of this specification, the AR-HUD system can acquire environmental perception information, vehicle operating status information, driver status information, map information, and AR-HUD display information for the target vehicle's current control cycle.

[0028] Among them, environmental perception information can be provided by cameras, millimeter-wave radar, lidar or other environmental perception devices on the target vehicle, including the number of targets ahead, target categories, target positions and relative motions, lane lines, drivable areas, road boundaries, intersection recognition and environmental visibility, etc., which can be used to form dynamic environmental features and can assist in correcting the scene judgment on the map side.

[0029] Vehicle operating status information can be provided by the target vehicle's chassis system, motion status acquisition module, or on-board bus, including vehicle speed, acceleration and deceleration, braking input, steering wheel angle and rate of change, lateral and longitudinal motion status, and operating stability, which can be used to form vehicle operating characteristics.

[0030] Driver status information can be provided by the driver monitoring system, including pupil changes, gaze deviation, gaze stability, head posture, and facial tension, which can be used to form driver status-related characteristics.

[0031] Map information, also known as high-precision map information, can be provided by navigation map modules, high-precision map modules, or positioning matching modules. It includes road types, number of lanes, intersections, merging and branching, ramp connections, road curvature, speed limits, and road network topology, which can be used to form road structure-related features.

[0032] AR-HUD display information may include the number of information items to be displayed in the current control cycle, the number of information categories, etc.

[0033] Furthermore, the AR-HUD system can use the current control cycle as a unified processing benchmark to acquire high-precision map information, environmental perception information, vehicle operation status information, and driver status information, and then perform time alignment, feature extraction, classification and organization, and validity processing.

[0034] S2: Based on the map information, determine the road structure-related features of the road where the target vehicle is located, and based on the road structure-related features, determine the road scene where the target vehicle is located.

[0035] In one or more embodiments of this specification, the AR-HUD system (hereinafter referred to as the system) can determine the road structure-related features of the road where the target vehicle is located based on map information, and determine the road scene where the target vehicle is located based on the road structure-related features.

[0036] Specifically, map information may include the road scene orientation of the target vehicle within the preset forward road range, the number of intersection nodes, the number of intersection node connection directions, the number of fork nodes, the number of merging nodes, the number of consecutive fork-merging nodes, the number of lane number changes, the number of lane function changes, and statistics on ramp entry and exit connections. Road scene orientation includes urban road orientation and highway orientation. Whether the road scene orientation points to an urban road or a highway can be determined directly from the map or location matching results. An urban scene orientation is generated when the current road is a regular urban road, and a highway scene orientation is generated when it is a highway, urban expressway, or elevated expressway. For cases where the road type cannot be clearly categorized, this feature can be marked as having no clear orientation, rather than being forcibly assigned to any scene.

[0037] The characteristics of intersection nodes and their connecting directions can be determined based on the number of intersection nodes and the number of connecting directions. These two indicators can be mapped to level values ​​based on the threshold ranges corresponding to the number of intersection nodes and the number of connecting directions, respectively. Then, statistical values ​​are generated according to a preset level combination mapping relationship. The level combination mapping relationship can be achieved through table lookup, taking the higher level, weighting the level values ​​and segmenting them, or triggering specific levels. When the statistical value reaches or exceeds the node density threshold, an urban scene orientation is formed. When the statistical value is below the threshold, and the road type characteristics or other road structure-related characteristics simultaneously indicate that the current road has a continuous forward traffic structure, a highway scene orientation is formed. If none of the above conditions are met, no clear orientation is formed.

[0038] The bifurcation and merging characteristics are determined based on the number of bifurcation nodes, merging nodes, and consecutive bifurcation and merging nodes. Consecutive bifurcation and merging nodes indicate continuous changes in connectivity along the current travel path over a short distance. These three indicators can be mapped to level values ​​based on the threshold ranges corresponding to the number of bifurcation nodes, merging nodes, and consecutive bifurcation and merging nodes, respectively. Then, statistical values ​​are formed based on a preset level combination mapping relationship. The level combination mapping relationship can be achieved through table lookup, taking the higher level, weighting the level values ​​and then segmenting, or triggering specific levels. This same technical method will not be repeated hereafter; it will be abbreviated as "mapping" in subsequent sections. When the statistical value reaches the corresponding threshold, an urban scene orientation is formed. When the statistical value is below the threshold, and the road type characteristics or other road structure-related characteristics simultaneously indicate that the current road has a continuous forward traffic structure, a highway scene orientation is formed. If none of the above conditions are met, no clear orientation is formed. This feature describes changes in general road or lane connectivity, and does not rely solely on the presence of ramps.

[0039] Based on the statistics of ramp entry and exit connections, ramp connection features are determined. These statistics are mapped to the ramp entry and exit connections and used as ramp connection features. When the statistical values ​​reach a ramp connection threshold, a highway scene indication is formed. If the threshold is not reached, this feature does not independently form an urban scene indication, but is used in conjunction with other road structure features for judgment.

[0040] Lane change characteristics are determined based on the number of changes in lane quantity and lane function. Lane change characteristics can be statistically analyzed by counting the number of changes in lane quantity and lane function. Lane function changes include changes in turning lanes, exit lanes, merging lanes, dedicated lanes, or other lane usage. These can be first categorized separately before generating lane change statistics. When the statistical value reaches a lane change threshold, an urban scenario orientation is generated. When the statistical value is below this threshold, and road type characteristics or other road structure-related characteristics simultaneously indicate that the current road has a continuous forward traffic structure, a highway scenario orientation is generated. If none of the above conditions are met, no clear orientation is generated.

[0041] Finally, based on intersection node and connection direction features, bifurcation and merging features, ramp connection features, lane change features, and road scene orientation, the road scene where the target vehicle is located is determined. The above five types of road structure-related features output one of the following results: urban scene orientation, highway scene orientation, or no explicit orientation. Urban scene orientation and highway scene orientation are counted in the orientation count of their respective scene layers. No explicit orientation is not counted in the orientation count of any scene layer, but this feature is still counted in the number of valid features when the data is valid. Each type of feature is counted as a maximum of one orientation value within a control cycle, and the system performs majority judgment based on the orientation count of each type of feature. Judgment can be based on Table 1 below.

[0042] Table 1 Scene Layer Judgment

[0043] It is worth noting that the preset forward road range can be dynamically set according to vehicle speed. A shorter range is used at lower speeds to promptly identify urban intersections and lane changes; a longer range is used at higher speeds to identify ramps, exits, and continuous road structures in advance. Regardless of the range change, all five feature types are statistically analyzed based on the same forward path determined in the current cycle.

[0044] When the candidate scenario layer differs from the previous control cycle, switching is only allowed if the new candidate result remains consistent for N consecutive control cycles; otherwise, the scenario layer of the previous control cycle is maintained. N is a preset positive integer, which can be calibrated according to the control cycle, response requirements, and stability requirements.

[0045] Specifically, it determines whether the road scenario of the target vehicle determined in the current control cycle is consistent with that of the previous preset number of control cycles. If yes, then S3 is executed based on the road scenario of the target vehicle determined in the current control cycle. If no, then S3 is executed based on the road scenario of the target vehicle in the previous control cycle.

[0046] In one alternative implementation, environmental perception information can also be used to assist in the correction of map-side road structure-related features. The road structure-assisted identification results in the environmental perception information may include one or more of the following: lane line status, drivable area status, road boundary status, intersection identification results, and bifurcation / merging identification results.

[0047] Among them, lane line status is used to characterize the number, location, direction, and continuity of lane lines identified by the perception system; drivable area status is used to characterize the range, boundaries, and occupancy of the drivable area ahead of the vehicle; road boundary status is used to characterize the location and direction of road edges, guardrails, medians, or the outer boundary of the road; intersection recognition results are used to characterize whether there are intersections, stop lines, cross roads, turning lanes, or traffic lights ahead; and bifurcation and merging recognition results are used to characterize whether there are branching, merging, merging, exit bifurcation, or changes in connection relationships on the road or lane ahead. The system compares the above results with the road structure information on the map side to help correct road structure-related features.

[0048] When the difference between the road structure information on the map and the perceived result that meets the preset validity conditions reaches the preset inconsistency condition, and this inconsistency remains true for M consecutive control cycles, the system confirms that there is an inconsistency between the road structure information on the map and the perceived result. The preset inconsistency condition may include one or more of the following: differences in the number of road nodes, differences in the number of lanes or lane functions, differences in road boundary locations, differences in drivable area coverage, or differences in traffic status; M is a preset positive integer used to avoid frequent corrections of map features due to short-term perception fluctuations.

[0049] After confirming inconsistencies, for road structure-related features that can be clearly verified by valid perception results, the system can use the perception results to correct the feature and re-establish its urban scene orientation, highway scene orientation, or no clear orientation. For features whose perception results only indicate that the map-side results may be inaccurate but are insufficient to form a clear scene orientation, the system marks the feature as having no clear orientation. The corrected features still participate in the formation of scene layer candidate results according to the number of valid features and the vote threshold, and continue to perform scene layer switching confirmation for N consecutive control cycles.

[0050] S3: Determine the basic layout template of the AR-HUD display area based on the road scene where the target vehicle is located.

[0051] In one or more embodiments of this specification, after the scene layer, i.e., the road scene where the target vehicle is located, is confirmed, the system can call the basic layout template of the AR-HUD display area corresponding to the scene layer. Different basic layout templates can be formed based on the same set of display areas, but different basic display area attributes, area display importance levels, and shrinkable area order can be configured for the same area.

[0052] The basic layout template for the AR-HUD display area includes the basic display area attributes, area display importance level, and shrinkable area order for each display area. Basic display area attributes include the reference anchor area, allowed areas, restricted areas, and disabled areas.

[0053] S4: Based on the environmental perception information, determine the complexity of the external environment of the target vehicle; and based on the vehicle operating status information, determine the vehicle operating status correction result of the target vehicle.

[0054] S5: Determine the display shrinkage level of the AR-HUD display area based on the complexity of the external environment and the correction result of the vehicle's operating status.

[0055] In one or more embodiments of this specification, the system can determine the complexity of the external environment of the target vehicle based on environmental perception information, and determine the vehicle operating status correction result of the target vehicle based on vehicle operating status information.

[0056] Specifically, in this specification, environmental perception information may include the number of identified targets (i.e., traffic participants), the number of traffic participant categories, environmental visibility, the frequency of intrusion events, and the frequency of lateral movement changes of targets.

[0057] The system can determine the density of targets ahead by mapping based on the number of identified targets. The target density statistics can be performed within a fixed length or an observation range that varies with vehicle speed within the effective detection range of the sensing system. Identified targets may include motor vehicles, non-motor vehicles, pedestrians, and road targets that affect current traffic. When the number of targets falls within the first, second, or third threshold range, low, medium, or high density levels are generated, respectively.

[0058] Target category diversity is determined by mapping based on the number of traffic participant categories. Target category diversity is used to supplement the interaction complexity that the number of targets cannot reflect. For example, scenarios with the same number of targets, where pedestrians, two-wheeled vehicles, and motor vehicles coexist, typically have higher recognition and interaction requirements than scenarios with a single target category. When multiple targets appear in the same category, that category is counted only once to avoid double-counting category diversity and target density.

[0059] Traffic interaction complexity is determined by mapping based on the frequency of entry events and the frequency of lateral movement changes of targets. The complexity is based on dynamic events within a preset time window. Entry events indicate that a target from an adjacent lane enters the current lane or the area adjacent to the current path; lateral movement changes indicate that the lateral speed, lateral position, or movement trend of a target ahead changes to meet a threshold. Both types of events are first counted or ranked separately before the traffic interaction complexity result is formed.

[0060] Finally, the system can determine the external environmental complexity of a target vehicle based on the density of targets ahead, the diversity of target categories, environmental visibility, and the complexity of traffic interactions. When determining the external traffic environment complexity, a maximum-level trigger rule can be used, for example, directly resulting in high complexity if any key environmental sub-result is of a high level; alternatively, a majority-decision or lookup table rule can be used, for example, resulting in medium complexity if at least two are of medium level and no high level is found. Specific rules are determined by product calibration, but should be based on the grading results rather than directly mixing the original physical quantities. The system uniformly maps the above environmental sub-results to level values ​​and combines them to form low, medium, or high complexity external traffic environment complexity results. Combination rules can highlight the maximum-level trigger for any high-risk sub-result, or can use lookup tables, piecewise functions, or calibrated weighted combinations.

[0061] In this manual, vehicle operating status information includes vehicle speed data, braking input data, steering wheel angle change rate, and steering wheel correction times.

[0062] The system determines speed change results based on vehicle speed data through mapping. This can be based on the speed difference at the start and end of a time window, the maximum and minimum speed difference within the window, the maximum absolute value of acceleration, the maximum absolute value of deceleration, or the duration exceeding a set acceleration / deceleration threshold. Different vehicle platforms can select one or more statistical methods and map the results to a unified level value.

[0063] Braking activity is determined through mapping based on braking input data. Braking input data includes braking trigger frequency and braking input intensity. Braking trigger frequency can be determined by the number of events where the brake pedal opening exceeds a threshold, the brake pressure exceeds a threshold, or the vehicle deceleration meets the braking criteria. To prevent the same continuous braking event from being counted repeatedly, an event merging time interval can be set; sampling points that continuously meet the braking conditions within this interval are counted as one braking event. When using a single signal for braking input intensity, the maximum value of the brake pedal opening, the maximum value of the brake pressure, or the maximum absolute value of the braking deceleration within the time window can be taken. When using multiple signals, they are mapped to level values ​​respectively, and then the braking input intensity level is formed through the maximum level, table lookup, or combination rules. The average value, root mean square value, and duration exceeding the threshold can be used as substitute statistics.

[0064] Steering correction activity is determined by mapping the rate of change of steering wheel angle and the number of steering wheel corrections. The rate of change of steering wheel angle represents the speed of change of steering wheel angle per unit time; the number of steering corrections represents the number of events where the steering angle amplitude, rate of change of steering angle, or lateral deviation of the vehicle meets the steering correction conditions. The system can first generate level values ​​for each, and then combine the two to form the steering correction activity.

[0065] Finally, the system can determine the vehicle operating state correction result of the target vehicle through mapping based on the speed change result, braking activity, and steering correction activity.

[0066] The system unifies speed changes, braking activity, and steering correction activity into level values, forming a vehicle operating state correction score or rule result, which is then mapped to a stable state, a slightly corrected state, or a more significantly corrected state. This result only represents the corrective effect of the vehicle's operating side on the demand for reduced display space, and does not separately characterize the driver's full workload.

[0067] In this specification, the complexity of the external environment is categorized as low, medium, and high. Vehicle operating status correction results include stable state, slightly corrected state, and enhanced corrected state. The displayed shrinkage level includes low, medium, and high shrinkage levels.

[0068] Subsequently, the system determines the display shrinkage level of the AR-HUD display area based on the complexity of the external environment and the vehicle's operating status.

[0069] The vehicle operating status correction results can adopt a conservative upward adjustment rule. The stable state does not change the base contraction level determined by environmental complexity. The mild correction state primarily adjusts the low contraction level to the medium contraction level. The enhanced correction state can adjust the low contraction level to the medium contraction level or the medium contraction level to the high contraction level, but the high contraction level will not be further increased. The contraction level determination can be found in Table 2.

[0070] Table 2 shows the determination of shrinkage level.

[0071] S6: Determine the openness of the basic layout template based on the displayed shrinkage level.

[0072] In one or more embodiments of this specification, the system can determine the openness of the basic layout template based on the determined display shrinkage level.

[0073] Specifically, the system can determine the displayable area of ​​each display area in the basic layout template based on the display shrinkage level, the importance level of the display area, and the order of shrinkable areas.

[0074] The basic layout template pre-configures three aspects: the basic display area attributes of each display area; the display importance level of each area; and the order of collapsible areas for each area. The display importance level indicates the priority of retaining the area itself, and the order of collapsible areas indicates the order in which the openness decreases when collapsing. Both can be marked in the offline stage and invoked in the online stage.

[0075] The basic display area attributes include the baseline anchor area, allowed area, restricted area, and disabled area. The disabled area includes the preset disabled area and the currently disabled area. The preset disabled area refers to the disabled area preset in the basic layout template, while the currently disabled area refers to the display area attributes temporarily changed according to the display shrinkage level. See Table 3 below for details.

[0076] Table 3 shows the regional attribute table.

[0077] The rules for adjusting the openness of the display area based on the display shrinkage level are shown in Table 4 below.

[0078] Table 4 shows the rules for adjusting the degree of openness of the area.

[0079] The contraction process prioritizes reducing the openness of areas with lower importance that are prioritized in the order of contractible areas. Baseline anchor areas or areas carrying safety-critical information are, in principle, contracted last or not included in regular contraction. Lower area importance does not automatically mean disabling; a currently disabled area may only be created after the system enters the corresponding contraction level and performs adjustments sequentially.

[0080] The display area can be a regular grid, an irregular partition, a driving task-related partition, or a product calibration partition. There is no inherent superiority or inferiority relationship between different areas determined solely by their geometric location. This step does not limit the number, numbering, area, or geometric boundaries of the areas.

[0081] The importance level of a region is determined by comprehensively considering factors such as its suitability for providing safety information, navigation guidance, and driving task information, its potential impact on the observation of forward road targets and lane lines, and the boundaries of the AR-HUD imaging and the driver's primary viewing direction. This level only describes the priority of preserving the region, and does not specify the actual information priority of the region.

[0082] The order in which shrinkable areas can be determined based on the importance level of each area, the information carrying capacity of the scene, and the evaluation of the display scheme. Generally, less important areas are shrunk first, and more important areas are shrunk later; however, the specific order may vary depending on the scene template and is not simply equivalent to geometric position from the outside to the inside or from top to bottom.

[0083] Reducing the openness of a region can be achieved through either discrete attribute changes or continuous changes in the displayable area. Discrete changes include converting an allowed region to a restricted region, or a restricted region to a currently disabled region; continuous changes include reducing the displayable area, the amount of information it can hold, or the proportion of available sub-regions while keeping the attribute category unchanged.

[0084] S7: Determine the visual cognitive load based on the complexity of the external environment and the AR-HUD display information.

[0085] In one or more embodiments of this specification, the system can determine the visual cognitive load based on the complexity of the external environment and the AR-HUD display information.

[0086] Specifically, the information displayed by AR-HUD includes the number of information items to be displayed, the number of information categories, and the information distribution density in open areas.

[0087] The system determines the display information density result by mapping based on the number of information to be displayed, the number of information categories, and the information distribution density in open areas.

[0088] The displayed information density result can be formed by statistics on the number of information items to be displayed, the number of information categories, and the information distribution density in open areas. Each statistic is first mapped to a level value, and then a unified combination rule is used to form a low-density, medium-density, or high-density displayed information density result. The statistical value of the number of information items to be displayed represents the number of candidate information items in the current period, and the number of information categories represents the complexity of the categories involved in the candidate information. Both are compared with their respective threshold intervals and then mapped to level values ​​(statistics) to avoid directly mixing the number of items and the number of categories.

[0089] The statistical value (statistic) of information distribution density in open areas can be expressed as: the estimated area occupied by the information to be displayed ÷ the currently available display area. The currently available display area includes the displayable area of ​​the baseline anchor area, the allowed area, and the restricted area that meets the display conditions. This statistical value can also be formed based on the actual area occupied by the information already displayed in the current period and used for correction in the next control period. The statistical value of information distribution density in open areas can be expressed as the ratio between the estimated area occupied by the information to be displayed and the currently available display area. The estimated area occupied by the information to be displayed can be estimated based on the external display area, number of characters, icon size, or template space occupied by each piece of information to be displayed under the current display style; the currently available display area includes the displayable area of ​​the baseline anchor area, the allowed area, and the restricted area that meets the spatial and priority conditions. Preset disabled areas and currently disabled areas are not included in the currently available display area.

[0090] Finally, the system determines the visual cognitive load through mapping based on the displayed information density and the complexity of the external environment. The mapping rules for visual cognitive load can be found in Table 5 below.

[0091] Table 5 Visual Cognitive Load Mapping Rules

[0092] S8: Determine the base load of the AR-HUD system based on the visual cognitive load and the vehicle operating status correction results.

[0093] In one or more embodiments of this specification, the system can determine the basic load of the AR-HUD system based on the visual cognitive load and the vehicle operating status correction results, as shown in Table 6 below.

[0094] Table 6 Basic Load Mapping Table

[0095] S9: Determine the final load of the AR-HUD system based on the basic load and the driver status information.

[0096] In one or more embodiments of this specification, the system can determine the final load of the AR-HUD system based on the base load and driver status information.

[0097] Specifically, the driver monitoring system outputs driver status information. This information includes normal, slightly abnormal, and significantly abnormal states, corresponding to low, medium, and high correction states, respectively. Driver status information characterizes whether the driver's current information tolerance has decreased and serves as a correction input for the baseline load results. Driver status information is only used to maintain or increase the baseline comprehensive results; it is not used to decrease the baseline load results formed by external traffic conditions, displayed information density, and vehicle operating status. The final load determination can be referenced in Table 7 below.

[0098] Table 7 Final Load Determination

[0099] S10: Based on the final load, display the information of the current control cycle on the basic layout template with the determined openness.

[0100] In one or more embodiments of this specification, the system can display information about the current control cycle on a basic layout template with a determined degree of openness, based on the final load.

[0101] Specifically, the information priority of each piece of information to be displayed is determined. Based on the information priority and final load of each piece of information to be displayed, the information is displayed within the displayable area.

[0102] Information priorities are divided into first priority, second priority, and third priority. The information scope of each priority is shown in Table 8.

[0103] Table 8 Information Priority and Information Scope

[0104] When the final load is a low base load, it means that the current external traffic environment, the density of displayed information, and the vehicle operating status put less pressure on the driver's information processing. The system can maintain complete display of information of each priority within the displayable area of ​​each display area in the basic layout template.

[0105] When the final load is medium-base load, it indicates that the current working condition has generated a certain visual understanding pressure or vehicle operation pressure. The system needs to simplify, weaken, collapse or delay the display of the second and third priority information within the displayable area of ​​each display area in the basic layout template.

[0106] When the final load is a high base load, it means that the current operating conditions place high demands on driver information processing and vehicle operation control. The system should reserve the display area of ​​each display area in the basic layout template to display the first priority information, and restrict, delay or temporarily exclude the display of information that is not of other information priority.

[0107] In one or more embodiments of this specification, the display information must meet three display conditions simultaneously to enter the current basic layout template display queue: space allows, that is, it must not exceed the current display area attributes of each display area in the basic layout template; information priority allows, that is, the information priority conforms to the admission rules under the current final load; density allows, that is, it must not exceed the displayable area of ​​each display area in the basic layout template, and the currently available display area or number of areas can still accommodate the information.

[0108] Information that does not meet the display criteria will be weakened, collapsed, simplified, delayed, or temporarily excluded from the current layout display queue, and may be moved to the background pending display state. The input stage is collectively referred to as the set of information to be displayed; after being filtered by display criteria, the current layout display queue is formed; information that has not entered the queue but is still pending processing is in the background pending display state.

[0109] In one or more embodiments of this specification, normal display means displaying in the conventional style specified by the current basic layout template; simplified display means reducing unnecessary text, graphic details, or additional status; weakened display means reducing visual prominence, such as reducing brightness, contrast, transparency, or refresh rate; collapsed display means merging multiple similar pieces of information into a summary, icon, or status indicator.

[0110] Delayed display indicates that the information is not presented in the current control cycle, but will enter the candidate set of the subsequent control cycle when the recovery conditions are met or the information is still valid; not included in the current layout display queue indicates that the information does not participate in space allocation in the current control cycle; background pending display status is used to save information that still has an expiration date and can be restored later.

[0111] Priority information should, in principle, remain continuously accessible, but must still conform to spatial boundaries. For example, if existing priority information within the currently disabled area needs to continue to be displayed, it should be moved to the reference anchor area, the allowed area, or a restricted area that meets the display conditions, rather than continuing to occupy the currently disabled area.

[0112] Second-priority information can be simplified, weakened, folded, or delayed under medium load conditions based on remaining space and information density; under high load conditions, only the necessary parts directly related to the current driving task are retained. Third-priority information is prioritized for delay or moved to the background when space or density is insufficient.

[0113] When multiple pieces of information with the same priority compete for the same available area, the display order can be determined based on timeliness, relevance to the current driving task, remaining effective time, and information conflict. This sorting is an internal rule of the information layer and does not change the regional display importance level of the displayed area.

[0114] It is worth noting that, in this specification, when multiple indicators from different sources, with different physical meanings, or with different evaluation objects participate in the formation of the same result, the system preferably first converts each indicator into a unified level value or a unified evaluation scale, and then uses the corresponding combination rules to form the result based on the control purpose corresponding to the result and the actual role of each indicator.

[0115] The above combination rules do not require all indicators to use the same fixed algorithm, nor do they require each indicator to have the same weight. Different results can be formed according to their control objectives using methods such as table lookup mapping, maximum level triggering, priority triggering of specific indicators, weighted segmentation after level value, rule mapping, or model output.

[0116] The road structure-related features are not simply formed by adding up various road statistics. Instead, they are determined by different factors such as road type, intersection connection, bifurcation and merging, ramp connection, and lane changes, each contributing to the scene-level determination. After each feature outputs a city scene direction, a highway scene direction, or no explicit direction, the system then determines the scene-level result based on the number of valid features and the number of scene directions.

[0117] The complexity of the external traffic environment is not determined by mechanically averaging all environmental sub-features, but rather by determining the combination rules based on how each sub-feature contributes to the current driving observation difficulty and traffic interaction risk. Specifically, the density of targets ahead and the diversity of target categories characterize the amount of environmental information, environmental visibility status characterizes visibility constraints, and traffic interaction complexity characterizes dynamic interaction risks. The system can generate the result of the external traffic environment complexity using maximum level triggering, table lookup mapping, priority triggering of specific sub-features, or calibrated combination rules, based on the risk meaning of different sub-features.

[0118] The vehicle operating status correction result is not formed by mechanically averaging speed changes, braking activity, and steering correction activity. Instead, it is determined by separately determining the combination rules based on the impact of longitudinal motion fluctuations, braking control activity, and lateral correction frequency on the display space shrinkage requirement. When braking activity or steering correction activity reaches a high level, it can be used as an enhanced correction trigger; when speed changes are at a medium level but braking and steering are stable, only a slight correction state can be formed.

[0119] The visual cognitive load result is not a simple average of the complexity of the external traffic environment and the display information density, but is determined by the combined effect of the observation pressure of the external environment and the information presentation pressure of the AR-HUD. When the complexity of the external traffic environment reaches a high level, even if the display information density is low, a high visual cognitive load can still be formed; when the complexity of the external environment is low but the display information density reaches a high level, the visual cognitive load result can also be improved, so as to avoid the information processing pressure caused by the display information itself.

[0120] The baseline load result is not simply the sum or mechanical average of the visual cognitive load result and the vehicle operating status correction result, but is determined based on the superposition of visual information processing pressure and vehicle operation control pressure under the current operating conditions. When either the visual cognitive load or the vehicle operating status correction result reaches a high level, the baseline load result can be increased; when both are at a medium level, a medium or high baseline load can be formed according to the product calibration rules.

[0121] The aforementioned technical methods belong to a defined unified level mapping principle, namely mapping.

[0122] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 An adaptive control method for the openness of the AR-HUD display area is provided.

[0123] This instruction manual also provides Figure 2 The diagram shows a schematic structural representation of the electronic device. Figure 2 As shown, at the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above. Figure 1 An adaptive control method for the openness of the AR-HUD display area is provided.

[0124] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0125] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0126] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. An adaptive control method for the openness of an AR-HUD display area, characterized in that, include: S1. Acquire environmental perception information, vehicle operating status information, driver status information, map information, and AR-HUD display information for the target vehicle in the current control cycle; S2. Based on the map information, determine the road structure-related features of the road where the target vehicle is located, and based on the road structure-related features, determine the road scene where the target vehicle is located; S3. Determine the basic layout template of the AR-HUD display area based on the road scene where the target vehicle is located; S4. Determine the complexity of the external environment of the target vehicle based on the environmental perception information; And, based on the vehicle operating status information, determine the vehicle operating status correction result for the target vehicle; S5. Determine the display shrinkage level of the AR-HUD display area based on the complexity of the external environment and the correction results of the vehicle's operating status; S6. Determine the openness of the basic layout template based on the displayed shrinkage level; S7. Determine the visual cognitive load based on the complexity of the external environment and the AR-HUD display information; S8. Determine the base load of the AR-HUD system based on the visual cognitive load and the vehicle operating status correction results; S9. Determine the final load of the AR-HUD system based on the basic load and the driver status information; S10. Based on the final load, display the information of the current control cycle on the basic layout template with the determined openness.

2. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, The map information includes the road scene orientation of the target vehicle, the number of intersection nodes, the number of intersection node connection directions, the number of fork nodes, the number of merging nodes, the number of consecutive fork-merging nodes, the number of lane number changes, the number of lane function changes, and the statistics of ramp entry and exit connections; the road scene orientation includes urban road orientation and highway orientation. S2 specifically includes: The characteristics of the intersection nodes and their connection directions are determined based on the number of intersection nodes and the number of connection directions of the intersection nodes. The bifurcation and merging characteristics are determined based on the number of bifurcation nodes, the number of merging nodes, and the number of consecutive bifurcation and merging nodes. Based on the statistics of ramp entry and exit connections, the ramp connection characteristics are determined; Based on the number of changes in the number of lanes and the number of changes in lane function, lane change characteristics are determined; The road scene where the target vehicle is located is determined based on the intersection node and connection direction features, the bifurcation and merging features, the ramp connection features, the lane change features, and the road scene orientation.

3. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, The basic layout template for the AR-HUD display area includes the basic display area attributes, the importance level of each display area, and the order of collapsible areas; the basic display area attributes include the reference anchor area, allowed area, restricted area, and disabled area.

4. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, The environmental perception information includes the number of identified targets, the number of traffic participant categories, environmental visibility, the frequency of entry events, and the frequency of lateral movement changes of targets; S4 determines the complexity of the external environment of the target vehicle based on the environmental perception information, specifically including: Based on the number of identified targets, determine the target density ahead; Determine the target category diversity based on the number of traffic participant categories; The complexity of traffic interaction is determined based on the frequency of the intrusion events and the frequency of the target's lateral movement changes. The complexity of the external environment of the target vehicle is determined based on the density of the target ahead, the diversity of the target categories, the environmental visibility, and the complexity of the traffic interaction.

5. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, The vehicle operating status information includes vehicle speed data, braking input data, steering wheel angle change rate, and steering wheel correction times; S4 determines the vehicle operating status correction result of the target vehicle based on the vehicle operating status information, specifically including: Based on the vehicle speed data, determine the speed change result; Based on the braking input data, determine the braking activity level; The steering correction activity is determined based on the steering wheel angle change rate and the number of steering wheel corrections. Based on the speed change result, the braking activity, and the steering correction activity, the vehicle operating state correction result of the target vehicle is determined.

6. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, The complexity of the external environment includes low complexity, medium complexity, and high complexity; the vehicle operating state correction results include stable state, slightly corrected state, and enhanced corrected state. The displayed shrinkage level includes low shrinkage level, medium shrinkage level, and high shrinkage level.

7. The AR-HUD display area openness adaptive control method as described in claim 3, characterized in that, S6 specifically includes: The displayable area of ​​each display area in the basic layout template is determined based on the display shrinkage level, the region display importance level, and the order of the shrinkable regions.

8. The AR-HUD display area openness adaptive control method as described in claim 4, characterized in that, The AR-HUD display information includes the number of information to be displayed, the number of information categories, and the information distribution density in the open area; S7 specifically includes: The display information density result is determined based on the quantity of information to be displayed, the quantity of information categories, and the information distribution density of the open area; The visual cognitive load is determined based on the displayed information density results and the complexity of the external environment.

9. The AR-HUD display area openness adaptive control method as described in claim 7, characterized in that, S10 specifically includes: Determine the information priority of each piece of information to be displayed; Information is displayed in the displayable area according to the information priority of each piece of information to be displayed and the final load.

10. The AR-HUD display area openness adaptive control method as described in claim 1, characterized in that, S2 also includes step S21: Determine whether the current control cycle is consistent with the road scene of the target vehicle determined in the previous preset number of control cycles; If so, then S3 is executed based on the road scenario where the target vehicle is located, as determined in the current control cycle; If not, then S3 is executed based on the road scenario where the target vehicle is located in the previous control cycle of the current control cycle.

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