Task display method and vehicle

Abstract

The application discloses a task display method and a vehicle, and is applied to the technical field of vehicle-mounted equipment. The task display method comprehensively obtains a driver attention state reflecting a driver concentration degree and a driving state risk level representing a current driving scene risk level by collecting multi-dimensional data of a vehicle running state, a surrounding environment and driver behavior monitoring. Based on the real-time evaluation of the driver attention state and the driving state risk level, a task display control strategy is dynamically generated and executed, so that the display form and content of the notification bar on the vehicle-mounted screen are automatically adjusted, which can adapt to the complex environment and diversified needs of the vehicle-mounted device, and balance the information accessibility and driving safety.

Description

Technical Field

[0001] This application relates to the field of vehicle-mounted equipment technology, specifically to a task display method and a vehicle. Background Technology

[0002] With the deepening development of automotive intelligence, in-vehicle cockpit systems have evolved into comprehensive information platforms integrating navigation, communication, entertainment, and vehicle control. In this environment, it has become commonplace for multiple background tasks to run in parallel and need to deliver status updates and prompts to the driver. Currently, in-vehicle cockpit information notification methods mainly follow the interaction paradigm of mobile terminals such as smartphones. This design has achieved good results in mobile terminal scenarios; however, when directly transplanted to the in-vehicle environment, a profound contradiction arises between its inherent design logic and the core needs of the in-vehicle scenario. Mobile terminal notification mechanisms are based on a user-centric, relatively singular scenario assumption. Their core logic lies in proactively pushing information to gain the user's attention and assuming that the user has the autonomy to respond to and handle this information at any time. However, the in-vehicle scenario is a dynamic and comprehensive environment that integrates multiple complex needs such as driving safety, vehicle control, and environmental perception. The driver's attention resources need to prioritize serving safe driving tasks. The fixed, generic notification model of mobile terminals, based on the order or type of information arrival, cannot cope with the complexity and diversity of needs in the in-vehicle environment, leading to a mismatch between information supply and actual driving needs. This could cause unnecessary interference with safe driving procedures due to non-critical information, or it could result in critical information not being effectively received by the driver.

[0003] Therefore, how to overcome the limitations of fixed task notification display has become an urgent technical problem to be solved in the field of vehicle equipment technology. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a task display method and a vehicle that dynamically adjusts the display strategy of multi-task notifications based on the vehicle's real-time driving status and the driver's attention state, thereby improving the recognition efficiency and interactive rationality of key information while ensuring driving safety.

[0005] According to a first aspect of this application, a task display method is provided, applied to an in-vehicle display screen. The task display method includes: acquiring vehicle status data, environmental data, and driver monitoring system data; obtaining driver attention state and driving state risk level based on the vehicle status data, environmental data, and driver monitoring system data; generating a task display strategy based on the driver attention state and the driving state risk level; and adjusting the task display on the in-vehicle display screen based on the task display strategy.

[0006] As one possible implementation, a task display strategy is generated based on the driver's attention state and the driving state risk level, including: generating a form transition strategy and / or a display strategy based on the driver's attention state and the driving state risk level; the execution of the form transition strategy includes the transition duration of the notification bar form change, and the transition duration is positively correlated with the driver's attention concentration; the display strategy includes the visible number of notification bars corresponding to the task, and the driving state risk level is negatively correlated with the visible number of notification bars; wherein, based on the task display strategy, the task display on the in-vehicle display screen is adjusted, including: adjusting the transition form of the notification bar and / or the task display on the in-vehicle display screen based on the form transition strategy and / or the display strategy.

[0007] The driver's attention level determines the way the notification bar transitions, while the driving safety level determines the number of notifications displayed, i.e., whether they are displayed at all. Based on the driver's attention level, the execution strategy of the notification transition animation is dynamically adjusted to avoid executing invalid animations when the driver is not paying attention or using overly simplified display methods when the driver is highly attentive. The display of the notification bar is adaptively adjusted according to the driving safety level to avoid distracting the driver and improve driving safety.

[0008] As one possible implementation, a form transition strategy and / or display strategy is generated based on the driver's attention state and the driving state risk level, including: determining a state combination according to the current driver's attention state and the current driving state risk level; obtaining at least one of the form transition strategy and display strategy corresponding to the state combination according to a preset two-dimensional decision matrix, as the task display strategy; wherein, the preset two-dimensional decision matrix defines the correspondence between different state combinations and task display strategies.

[0009] By combining two key dimensions—driver attention state and driving state risk level—to form a unique state combination, and directly mapping the corresponding morphological transition strategy and / or display strategy based on a preset two-dimensional decision matrix, the complex, real-time computational dynamic decision-making process can be transformed into an efficient, deterministic lookup operation. This reduces the computational complexity and processing latency of the strategy at each state change, ensuring the real-time nature of strategy decisions and system response speed. Simultaneously, the matrix-based preset relationships allow for refined and forward-looking customization and optimization of strategies under different state combinations based on safety regulations and human-computer interaction experience, ensuring that the mapping from state recognition to strategy execution is both scientific, reasonable, stable, and reliable. Furthermore, the structured design makes the maintenance and adjustment of the strategy set clear and convenient; only the configuration at the corresponding position in the matrix needs to be modified, improving the system's maintainability and adaptability.

[0010] As one possible implementation, a display strategy is generated based on the driver's attention state and the driving state risk level, including: displaying a first preset number of notification bars when the driving state risk level is less than or equal to a first preset level; and displaying a second preset number of notification bars when the driving state risk level is greater than the first preset level but less than or equal to a second preset level; wherein the second preset number is less than the first preset number.

[0011] By linking the risk level of the driving condition with the specific number of notifications displayed in the notification bar, a clear and enforceable quantitative control rule can be established. When the risk level is low, a larger number of notifications can be displayed, making full use of relatively safe driving scenarios to deliver information and improving the richness and efficiency of information services. When the risk level increases, the number of notifications allowed to be displayed is reduced, ensuring that the information load of the notification system can be gradually reduced as the risk of the driving environment increases. This proactively and gradually reduces visual information sources that may distract the driver as the risk increases, guiding the driver's cognitive resources more towards the driving task itself and improving driving safety.

[0012] As one possible implementation, a form transition strategy is generated based on the driver's attention state and the driving state risk level, including: when the driver's attention state is in a first state, determining the form transition duration of the notification bar to a first preset duration, and setting the task interaction mode of the notification bar to an immediate response mode; when the driver's attention state is in a second state, determining the form transition duration of the notification bar to a second preset duration, and setting the task interaction mode of the notification bar to a jump-only interaction mode; when the driver's attention state is in a third state, determining the form transition duration of the notification bar to a third preset duration, and setting the task interaction mode of the notification bar to a restricted interaction mode; when the driver's attention state is in a fourth state, determining the form transition duration of the notification bar to a fourth preset duration, and setting the task interaction mode of the notification bar to a disabled interaction mode; wherein, the first preset duration is longer than the second preset duration, the second preset duration is longer than the third preset duration, and the third preset duration is longer than the fourth preset duration.

[0013] Notification animations are usually fixed animations. In the in-vehicle scenario, due to the frequent changes in the underlying state (foreground interface, status bar visibility, AVM mode, etc.), simple animation stacking can cause interface jitter, affecting the driver's visual stability and attention allocation. To solve this problem, the execution strategy of notification transition animations can be dynamically adjusted according to the driver's gaze direction and attention concentration, avoiding the execution of invalid animations when the driver is not paying attention, or the use of overly simplified display methods when the driver is highly attentive.

[0014] As one possible implementation, the task display method further includes: sorting the tasks in the candidate task set in descending order of priority based on a preset task priority rule to determine the tasks to be displayed; wherein the task display strategy is generated based on the tasks to be displayed, and the preset task priority includes: the priority of safety and intervention tasks is higher than that of navigation and driving tasks, and the priority of navigation and driving tasks is higher than that of entertainment and general tasks.

[0015] Setting priorities for different tasks ensures that drivers can focus their attention on the road ahead and critical driving tasks, eliminating numerous sources of interference and reducing unnecessary distractions. Furthermore, in complex traffic environments, critical information must be received and processed by the driver immediately; prioritization ensures that this information is perceived promptly and effectively.

[0016] As one possible implementation, the driver's attention state is obtained based on vehicle status data, environmental data, and driver monitoring system data, including: calculating a driving state risk coefficient based on the vehicle status data and a first preset weight, the environmental data and a second preset weight, and the driver monitoring system data and a third preset weight; and determining the driving state risk level based on the driving state risk coefficient.

[0017] Vehicle status data, from the vehicle's perspective, is used to determine whether the vehicle is in a controllable and safe physical state. Environmental data is used to determine whether the current external environment is complex and risky; the same driving behavior has different risk levels in different environments. Environmental data defines the probability and severity of risk occurrence, making the assessment of driving status risk level more realistic. Driver monitoring system data is used to determine whether the driver is focused and in a controllable state. Each of these three dimensions has limitations when judged individually; however, their integration can create an accurate risk profile and improve the accuracy of driving status risk level assessment. The first, second, and third preset weights are not fixed and can be dynamically adjusted based on environmental data, making the model flexible. By assigning higher weights to data with high reliability and low noise, interference from unreliable data can be suppressed. When different data sources provide conflicting signals, the weights help the system make the optimal judgment.

[0018] As one possible implementation, the driver's attention state is obtained based on vehicle status data, environmental data, and driver monitoring system data, including: obtaining the driver's gaze direction and attention concentration based on the driver monitoring system data; and obtaining the driver's attention state based on the driver's gaze direction and attention concentration.

[0019] Eye direction reflects where the driver's eyes are looking, serving as the physical channel for information acquisition. By calculating the proportion of time the driver's gaze lingers on the in-vehicle display screen, the amount of cognitive bandwidth used for task acquisition can be precisely quantified. Attention level reflects whether the driver's brain is processing information, addressing the cognitive processing aspect of information acquisition. Therefore, by monitoring the driver's attention state, their information processing capabilities can be assessed, allowing for the selection of appropriate task delivery methods to ensure timely access to critical information in various situations, thereby improving driving safety.

[0020] As one possible implementation, adjusting the task display on the vehicle-mounted display screen includes: acquiring the visibility signal of the foreground component, the visibility signal of the status bar, and the panoramic image forced preview signal; if the visibility signal of the foreground component indicates that the target component corresponding to the task is already visible in the foreground, then tasks without an ignore task conflict flag are filtered out; if the visibility signal of the status bar indicates that the status bar is not visible, then tasks without an ignore status bar constraint flag are controlled not to be displayed; if the panoramic image forced preview signal indicates that the vehicle-mounted display screen is in a panoramic image forced preview state, then tasks without an ignore panoramic image flag are marked as prohibited from clicking.

[0021] It can filter scenarios based on the usage scenarios of in-vehicle display screens such as front-end page, panoramic image mode, and status bar, and use in-vehicle proprietary signals such as the visibility of front-end components, the visibility of status bar, and AVM (panoramic image) forced preview as control inputs for notification display and jump, thereby taking into account both driving safety and information accessibility, and is more suitable for information push in in-vehicle scenarios.

[0022] According to a second aspect of this application, a vehicle is provided, comprising: an in-vehicle display screen having a notification bar; and a task display device communicatively connected to the in-vehicle display screen, the task display device being used to execute the task display method described in the first aspect or any implementation thereof, to adjust the task display of the notification bar.

[0023] According to a third aspect of this application, a task display device is provided, applied to an in-vehicle display screen. The task display device includes: an acquisition module for acquiring vehicle status data, environmental data, and driver monitoring system data; an obtaining module for obtaining driver attention status and driving state risk level based on the vehicle status data, environmental data, and driver monitoring system data; a generation module for generating a task display strategy based on the driver attention status and the driving state risk level; and an adjustment module for adjusting the task display on the in-vehicle display screen based on the task display strategy.

[0024] According to a fourth aspect of this application, a computer device is provided, the computer device comprising: one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect or any implementation thereof.

[0025] According to a fifth aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the method as described in the first aspect or any implementation thereof.

[0026] According to a sixth aspect of this application, an electronic device is provided, including a module for performing the method as described in the first aspect or any implementation thereof.

[0027] According to a seventh aspect of this application, a computer program product is provided, comprising program code for performing the method as described in the first aspect or any implementation thereof.

[0028] The task display method and vehicle provided in this application first acquire vehicle status data, environmental data, and driver monitoring system data. Based on the fusion of these multi-source data, the driver's attention state and driving state risk level are obtained. Then, a task display strategy is dynamically generated and executed based on the driver's attention state and driving state risk level to adjust the task display on the in-vehicle display screen. This transforms the task display logic from static preset to dynamic control based on safety requirements, establishing a correlation between task display and driving scenario safety requirements. This allows the task display to adaptively match driving safety scenarios, adapting to complex in-vehicle environments and diverse needs, balancing information accessibility and driving safety, and effectively improving the scene perception capability and human-machine interaction safety of the in-vehicle system. Attached Figure Description

[0029] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0030] Figure 1 This is a flowchart illustrating a task display method provided in an exemplary embodiment of this application.

[0031] Figure 2 This is a schematic diagram of the structure of a task display device provided in an exemplary embodiment of this application.

[0032] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0033] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0034] In existing in-vehicle cockpit platforms, multiple background tasks such as navigation, phone calls, music, vehicle system status, and AVM (Around View Monitor) often run simultaneously, requiring the display screen to display critical information to the user. Currently, the information notification mechanism of in-vehicle systems mainly borrows from mobile terminal designs, presenting information in static forms such as lists, pop-ups, or icons. This generic solution is increasingly showing its limitations in complex in-vehicle scenarios. On the one hand, the in-vehicle display area has a unique shape and driving information has extremely high priority. Traditional stacked notification interfaces easily create visual conflicts with core driving information (such as navigation and AVM), encroaching on limited screen resources and potentially interfering with driving safety. On the other hand, and more importantly, existing notification systems, as a passive and homogeneous signal dissemination channel, have fixed and universal display logic, failing to deeply integrate with the dynamic operating environment of the vehicle and the driver's state perception.

[0035] For example, in-vehicle displays are typically long horizontally and narrow vertically, and the top must also accommodate a status bar, camera / sensor openings, and vehicle logos, leaving limited effective space for notification display. When multiple background tasks simultaneously update, traditional list or multi-bubble displays occupy excessive vertical space, obscuring crucial information such as navigation conditions and reversing camera feeds, thus impacting driving safety. Furthermore, existing solutions often arrange notifications by time or simple business type, rarely considering factors such as relevance to driving safety or whether the application is currently in use. When multiple events occur simultaneously, such as incoming calls, navigation inflection point alerts, AVM image updates, and music switching, users struggle to promptly and accurately identify the task that requires their immediate attention.

[0036] Furthermore, existing notification systems mostly employ a static display strategy. Regardless of whether the vehicle is traveling at high speed or parked, and regardless of whether the driver is paying attention to the in-vehicle display screen, the display duration, animation complexity, and interactive response method remain consistent. This fixed display method may excessively distract the driver's attention and increase driving safety hazards in high-risk driving scenarios such as high-speed driving, rainy or foggy weather, and sharp turns; while in parked or low-speed scenarios, it fails to make full use of the user's available cognitive resources, resulting in low information delivery efficiency.

[0037] The reason for these problems is that existing multitasking notification systems are mostly designed based on the design concepts of general-purpose terminals such as mobile phones and tablets, primarily targeting large-screen, non-driving scenarios, and are not specifically designed for in-vehicle environments. Therefore, the standardized information push logic of mobile terminals for notifications themselves appears rigid and inflexible in the in-vehicle environment, failing to achieve a dynamic balance between the intrusiveness of information prompts and the needs of driving safety and the cognitive load of the driver.

[0038] To address the aforementioned problems, this application proposes a task display method and vehicle that can intelligently decide and dynamically adjust the timing, method, and intensity of notification presentation based on the vehicle's driving status and the driver's attention level, thereby enabling the driver to read key information while ensuring driving safety.

[0039] Currently, in-vehicle cockpit information notifications primarily follow the interaction paradigm of mobile terminals such as smartphones, employing standardized static display mechanisms such as lists, pop-ups, and icons. To adapt to the dynamic needs of in-vehicle driving scenarios for task display, the notification bar can be designed as a combined view containing a first and a second notification bar. The first and second notification bars can adopt a capsule-shaped notification bar (AliveBar) to centrally display the one or two most important background tasks on the in-vehicle display screen, serving as a container for these tasks. The capsule-shaped notification bar can be positioned at the top center of the screen or near the status bar area, constructed by combining a background container and a content area. The first notification bar can act as the primary capsule, and the second notification bar as the secondary capsule. This dual-capsule layout allows for the simultaneous display of the top two tasks from the candidate task set within a limited width, effectively carrying key prompts for multiple tasks without obstructing navigation and AVM (Autonomous View Monitor) displays, thus solving the problem of limited display space for multiple background task notifications in the in-vehicle cockpit.

[0040] In some embodiments, the overall width, height, and internal content height of the capsule-shaped notification bar can be defined by a set of constants, such as a total width of approximately 300dp, a total height of approximately 48dp, and a content area height of approximately 37dp, ensuring that text and icons are clearly readable at normal viewing distances. Since the dual capsules of the capsule-shaped notification bar can adjust their display shape in different display states, the content area width can be divided: initial width for the contracted state before transition, middle width for the width of the main capsule in the split state, large width for the full-width display of the main capsule in single-task mode, and min width for the secondary capsule to display more concise content.

[0041] In some embodiments, the shapes of the primary and secondary capsules can be defined using a uniform fillet radius constant (such as contentCornerRadius) to maintain capsule characteristics during deformation in different states. In certain compatible states, the fillet radius is dynamically related to the height; for example, in the transition state, the fillet radius can be set to half the height to achieve a continuous shape transformation from the contracted state to the expanded state.

[0042] In other embodiments, the capsule shape can also be replaced with other shapes such as rectangle, rounded rectangle, ellipse, etc., as long as the visual hierarchy and spatial allocation relationship between the main task and the secondary task remain consistent.

[0043] In other embodiments, the left-right arrangement of the main capsule and the secondary capsule can be replaced by a top-bottom arrangement, a diagonal arrangement, or other layouts, as long as the main task is highlighted and the secondary task is readable within the visible area of ​​the vehicle's infotainment system.

[0044] After completing the capsule-shaped notification bar design, the notification bar does not display all tasks sequentially. Instead, it selectively displays tasks based on multiple factors and can change its display format. Figure 1 This is a flowchart illustrating a task display method provided in an exemplary embodiment of this application, which can be applied to the capsule-shaped notification bar provided above. Figure 1 For example, firstly, acquire vehicle status data, environmental data, and driver monitoring system data (see...). Figure 1 (S110). These three dimensions of data can reflect driving risks from multiple perspectives, thus providing a reference for the demonstration of subsequent tasks. Secondly, based on vehicle status data, environmental data, and driver monitoring system data, the driver's attention state and driving state risk level are obtained (see S110). Figure 1 (S120). By introducing real-time, multi-dimensional data from the vehicle's underlying sensor network (vehicle status, environment) and driver monitoring system as input, the decision-making basis for the task display strategy can be systematically changed. Then, based on the driver's attention state and driving state risk level, a task display strategy is generated (see S120). Figure 1 (S130). By integrating multi-dimensional data, the task display strategy shifts from static preset to dynamic perception-driven, thereby eliminating the contradiction between the static display strategy and dynamic safety requirements. Finally, based on the task display strategy, the task display on the in-vehicle display screen is adjusted (see S130). Figure 1 (S140). The task display is adjusted based on the task display strategy to improve the recognition efficiency and interaction rationality of key information while ensuring driving safety.

[0045] The above task display method can be implemented using a unidirectional data flow: business data → control layer → state container → rendering layer. The business layer is responsible for maintaining the task set (addition, deletion, foreground / background switching), task attributes (business type, security level, etc.), and scene signals (foreground components, status bar, AVM status). The control layer calculates task priorities and candidate task rankings based on business data and scene signals (including driving status safety level from sensor fusion and attention status from DMS), and advances the state machine accordingly. The state container stores the current display state and the animation parameter configuration for attention perception. The rendering layer renders only based on the state and parameters, and returns the completed state after the animation is finished.

[0046] In a unidirectional data flow structure, consistency and de-jitter mechanisms can be achieved. All display state progression is handled by the control layer, and the rendering layer only relies on the passed-in state for drawing, without writing back to the business state, thus avoiding race conditions caused by bidirectional dependencies. Each time a state transition occurs, the control layer records the target stable state, and the rendering layer sends the actual completed state back to the control layer upon animation completion. Only when both are consistent is the next state allowed, ensuring sequential execution of the state machine. Frequently changing signals such as the visible component list, status bar visibility, sensor data, and attention status are merged using throttling or rate limiting mechanisms to reduce interface jitter caused by short-cycle back-and-forth oscillations. Degraded display due to an increase in driving safety level or a decrease in attention status has higher priority than state transitions triggered by task changes, ensuring that driving safety always takes precedence over information display.

[0047] The following text combines Figure 1 The task display method provided in the embodiments of this application will be described in more detail.

[0048] The notification system is often loosely coupled with modules such as the vehicle's front-end page management, AVM mode, and status bar control, lacking a unified scene-aware control layer. This results in a failure to comprehensively consider driving safety and the user's current focus when deciding whether to display notifications, how to display them, and whether to allow navigation. Therefore, preset tasks can be filtered by scene to obtain a set of candidate tasks. Scene filtering can be performed based on the usage scenarios of the vehicle's display screen, such as the front-end page, AVM mode, and status bar. Vehicle-specific signals such as the visibility of front-end components, the visibility of the status bar, and forced preview of AVM can be used as control inputs for notification display and navigation, thus balancing driving safety and information accessibility. The set of candidate tasks after scene filtering is then combined with the driver's attention state and driving risk level to adjust the task display on the vehicle's display screen.

[0049] For example, adjusting the task display on the vehicle's in-vehicle display screen includes: obtaining the visibility signals of foreground components, status bar visibility signals, and panoramic image forced preview signals; if the foreground component visibility signal indicates that the target component corresponding to the task is already visible in the foreground, then tasks without an ignore task conflict flag are filtered out; if the status bar visibility signal indicates that the status bar is not visible, then tasks without an ignore status bar constraint flag are prevented from being displayed; if the panoramic image forced preview signal indicates that the vehicle's in-vehicle display screen is in panoramic image forced preview mode, then tasks without an ignore panoramic image flag are marked as prohibited from being clicked.

[0050] In some embodiments, in addition to tracking currently visible components, the control layer also maintains a list of currently visible components on the in-vehicle display screen and compares it with a historical list. The list of currently visible components is provided by the vehicle's window manager and reflects the page components currently being displayed in the foreground. To avoid the same task being displayed repeatedly on the main interface and in the capsule bar, for each task, the target component of the preset task is parsed or the target package name in the extended field is read to determine whether the task has been fully displayed on the main interface. If the target component or target package name is already in the currently visible list and the preset task does not have an ignore task conflict flag set, the preset task is filtered, and this task will not enter the first or second notification bar to avoid duplicate task display. If the preset task has an ignore task conflict flag set, it will still be displayed in the first or second notification bar even if it is in the currently visible list.

[0051] In some embodiments, the underlying layer reports the visibility of the status bar via callbacks, and the control layer saves this information as a state variable after debounce. When deciding whether to display a task, if the status bar is invisible and the preset task does not have an "ignore status bar constraint" flag set, the preset task is filtered out, and neither the first nor the second notification bar will display the preset task, thus avoiding unnecessary prompts when the status bar is completely collapsed or in special driving modes. If the "ignore status bar constraint" flag is set, the preset task can be displayed in either the first or second notification bar regardless of the status bar state, ensuring the complete communication of important tasks.

[0052] In some embodiments, when the AVM (Aspect Rim Video) is in forced preview mode, some prompts can be displayed alongside the AVM. However, for security reasons, users need to be prevented from clicking on these prompts to exit the AVM screen. Therefore, the control layer receives the AVM forced preview state and combines it with the "ignore panoramic image" flag on the task for judgment. When the panoramic image is in forced preview mode and the preset task does not have the "ignore panoramic image" flag set, the visual prompts for this preset task are retained but marked as "do not click to jump". When the preset task has the "ignore panoramic image" flag set, even in forced preview mode, display and jumping are still possible to ensure the interactivity of important tasks.

[0053] It is understandable that in the filtering process of the above three scenarios, the three scenarios can be filtered simultaneously or sequentially. The filtering order can be adjusted arbitrarily, and the task markings and scenario are considered to determine whether they can be displayed. The preset tasks that are not filtered are integrated into a set of candidate tasks.

[0054] To address the issue of unclear task priorities when multiple tasks are running in parallel, tasks in the candidate task set can be sorted from highest to lowest priority based on preset task priority rules to determine the tasks to be displayed. The task display strategy is generated based on the tasks to be displayed, and the preset task priorities include: safety and intervention tasks have higher priority than navigation and driving tasks, and navigation and driving tasks have higher priority than entertainment and general tasks.

[0055] In some embodiments, the preset task priority rules are determined by a combination of multiple factors and encapsulated as numerical fields. To meet the usage requirements of driving scenarios, the preset task priority rules can be set as follows: safety and intervention tasks have the highest priority, such as incoming calls, online calls, and emergency alarms. These tasks are directly related to driving safety and human-machine interaction intervention and are represented by fixed high-priority constants. Navigation and driving-related tasks have the next highest priority, such as navigation turn prompts, lane departure warnings, and speed limit change prompts. These tasks are highly related to driving scenarios and need to provide drivers with real-time readable information to assist driving decisions, and are prioritized over entertainment tasks such as music. Entertainment and general system notifications have the lowest priority, such as music track switching, new message arrivals, and download progress. These tasks only enter the first or second notification bar when there are no high-priority tasks or when the first two tasks are vacant. These tasks are less related to driving safety and are mainly used to meet the entertainment needs of drivers or passengers, so they can be set to a lower priority. Therefore, when prioritizing tasks, the priority of safety and intervention tasks is higher than that of navigation and driving tasks, and the priority of navigation and driving tasks is higher than that of entertainment and general tasks. The candidate tasks in the candidate task set are then sorted from high to low.

[0056] Driving requires continuous visual, cognitive, and manual operation resources, which are preempted by irrelevant or low-priority notifications. By prioritizing tasks, the system can proactively block or silence most non-critical information, ensuring that the driver's attention is focused on the road ahead and critical information, eliminating numerous sources of interference at the source. In complex traffic environments, critical information must be received and processed by the driver immediately. High-priority notifications can be given the highest authority and delivered through strong audible alerts, full-screen or head-up display (HUD) coverage, or even haptic feedback (steering wheel / seat vibration), ensuring they are detected even in noisy environments or when the driver is slightly distracted. Without prioritization, the driver would have to process multiple dimensions of information simultaneously, potentially leading to the omission or delayed reception of critical information, impacting driving safety.

[0057] Understandably, the priority settings can be dynamically adjusted to meet users' personalized needs. For example, navigation and driving-related tasks can be given the highest priority to provide reliable information prompts to drivers in unfamiliar road environments, while incoming calls and online calls can be given lower priority to prevent interference with road information prompts.

[0058] After sorting the candidate tasks, taking the setting of the first and second notification bars as an example, the first task in the sort can be mapped to the first notification bar, and the second task in the sort can be mapped to the second notification bar. The first and second notification bars have fixed display formats and flexible switching strategies. For example, through a unified capsule notification bar format and state machine, multi-source tasks are standardized into a consistent display and interaction framework, ensuring uniformity in display methods and facilitating information reading for the driver.

[0059] In S110, vehicle status data, environmental data, and driver monitoring system data are acquired.

[0060] Vehicle status data, from the vehicle's perspective, is used to determine whether the vehicle is in a controllable and safe physical state. Environmental data is used to determine whether the current external environment is complex and risky. The same driving behavior has different risk levels in different environments. Environmental data defines the probability and severity of risks, making the assessment of driving status risk level more consistent with reality. Driver monitoring system data is used to determine whether the driver is in a focused and controllable state.

[0061] Data used to determine driver attention and the risk level of driving conditions can be acquired through the vehicle's own sensors. For example, the control layer subscribes to the following sensor data through the onboard HAL (Hardware Abstraction Layer): CAN bus vehicle status data, environmental perception sensor data, and Driver Monitoring System (DMS) data.

[0062] For example, the CAN bus vehicle status data mentioned above includes: vehicle speed (Speed), used to determine the real-time vehicle speed, in km / h; steering angle (SteeringAngle), used to determine the steering wheel angle of the vehicle, in degrees; steering angle change rate (SteeringRate), used to determine the rate of change of the steering angle of the vehicle per unit time; gear position (GearPosition), used to determine the vehicle's P / R / N / D / S gear information; and braking status (BrakeStatus), used to determine whether the vehicle's brake pedal is depressed.

[0063] For example, the environmental perception sensor data mentioned above includes: WiperStatus, used to determine the wiper speed (off / intermittent / low speed / high speed) to reflect weather conditions; LightStatus, used to determine the status of headlights and fog lights to assist in determining visibility; and AmbientLight, used to determine the light intensity inside and outside the vehicle.

[0064] For example, the Driver Monitoring System (DMS) data mentioned above includes: Gaze Direction, used to determine the driver's gaze direction vector or area classification (road surface / instrument panel / center console screen / side window, etc.); Attention Level, used to determine the attention score (0-1 floating-point value) calculated based on blink frequency, head posture, etc.; and Fatigue Level, used to assess the driver's fatigue level.

[0065] Understandably, CAN bus data can be replaced with equivalent data provided by other in-vehicle communication protocols (such as SOME / IP, DDS, etc.), and DMS gaze orientation data can be replaced with attention data indirectly inferred from touchscreen heatmaps, gyroscope attitude, etc., or with more advanced eye-tracking technology. Environmental sensor data can be replaced with weather condition judgments based on camera visual recognition, or real-time weather information obtained through vehicle networking.

[0066] In S120, the driver's attention level and driving risk level are obtained based on vehicle status data, environmental data, and driver monitoring system data.

[0067] Driver attention status is a classification assessment of the driver's current level of attention and cognitive resource availability, derived from DMS data analysis. Driving state risk level is a quantitative characterization of the safety level of the current driving environment, calculated based on the fusion of multi-source sensor data.

[0068] In some embodiments, driving status risk levels include low-risk, medium-risk, and high-risk scenarios. Each of these three dimensions has limitations when judged individually; however, their integration creates an accurate risk profile, improving the accuracy of driving status risk levels. Driving status risk levels provide a safety boundary for task display. Even if a task has been selected, if the driving status risk level is high, some information is forcibly hidden or its display is delayed until the risk decreases, ensuring the driver focuses on key information and avoiding distractions from irrelevant information. Driver attention status provides visual optimization for task display, offering richer information when the driver is viewing the notification bar and a brief display when the driver is not viewing it, ensuring effective information display, improving the driver's viewing experience, and also optimizing resource allocation.

[0069] In some embodiments, a driving state risk coefficient is calculated based on vehicle state data and a first preset weight, environmental data and a second preset weight, and driver monitoring system data and a third preset weight; the driving state risk level is then determined based on the driving state risk coefficient. Assigning different weights to different data can improve the accuracy and realism of the model, as different data contribute differently to the risk, and the weight values ​​reflect this difference in contribution. The first, second, and third preset weights are not fixed and can be dynamically adjusted based on environmental data, making the model more flexible. For example, in congested urban areas, the weights of DMS distraction monitoring (such as looking at a mobile phone) and vehicle following distance / sudden braking data are increased because rear-end collisions and pedestrian collisions are the main risks. In free-flowing highway sections, the weights of lane keeping, driver fatigue, and speeding data are increased because the risks mainly stem from inattention and loss of control at high speeds. Furthermore, assigning higher weights to data with high reliability and low noise can suppress interference from unreliable data. When different data sources provide conflicting signals, the weights help the system make the optimal judgment. For example, the vehicle may be stable, but the DMS may falsely report that the driver is closing their eyes due to a sudden change in light. Because vehicle data is more stable and reliable, and DMS has low confidence in this scenario, the system will give vehicle data higher weight and continuously monitor it. Finally, the weighting system can learn and adjust. For example, different base weights can be set for cautious and aggressive drivers. By collecting a large amount of real-world driving data (especially accident or near-miss event data), the weighting allocation can be verified and optimized in reverse.

[0070] By combining data acquisition and weight allocation, a quantitative model of the driving state risk coefficient I can be defined using Formula 1, based on CAN bus vehicle status data, environmental perception sensor data, and driver monitoring system (DMS) data: I=w1×f_speed(Speed)+w2×f_steering(SteeringRate)+w3×f_weather(WiperStatus,LightStatus)+w4×f_attention(AttentionLevel)+w5×f_gear(GearPosition) Formula 1; In Formula 1, I represents the driving state risk coefficient, f_speed(Speed) represents the speed risk function, w1 represents the preset weight value of the speed risk function, f_steering(SteeringRate) represents the steering risk function, w2 represents the preset weight value of the steering risk function, f_weather(WiperStatus,LightStatus) represents the weather risk function, w3 represents the preset weight value of the weather risk function, f_attention(AttentionLevel) represents the attention risk function, w4 represents the preset weight value of the attention risk function, f_gear(GearPosition) represents the gear risk function, and w5 represents the preset weight value of the gear risk function.

[0071] As one possible implementation, the weighting coefficients w1-w5 can be calibrated based on actual vehicle testing and ergonomic data. Example values: w1=0.4, w2=0.25, w3=0.15, w4=0.15, w5=0.05.

[0072] As one possible implementation, the speed risk function f_speed(Speed) can be divided into: Speed ​​< 10km / h: Risk weight = 0 (stopping or very low speed); 10≤Speed<40km / h: Risk weight = 0.2 (low speed in urban areas); 40≤Speed<80km / h: Risk weight = 0.5 (urban expressway); 80≤Speed<120km / h: Risk weight = 0.8 (highway); Speed ​​≥ 120km / h: Risk weight = 1.0 (ultra-high speed).

[0073] As one possible implementation, the steering risk function f_steering(SteeringRate) can be divided into: |SteeringRate|<5° / s: Risk weight = 0 (straight ahead or slow turn); 5 ≤ |SteeringRate| < 15° / s: Risk weight = 0.3 (normal turn); 15 ≤ |SteeringRate| < 30° / s: Risk weight = 0.7 (sharp turn); |SteeringRate| ≥ 30° / s: Risk weight = 1.0 (emergency avoidance).

[0074] As a possible implementation, the weather risk function f_weather(WiperStatus, LightStatus) can be divided into: Wiper off + lights normal: Risk weight = 0 (clear sky); Wiper intermittent or fog lights on: Risk weight = 0.4 (light rain or foggy day); Wiper on high speed or multiple fog lights on: Risk weight = 0.8 (heavy rain or thick fog).

[0075] As a possible implementation, the attention risk function f_attention(AttentionLevel) can be divided into: AttentionLevel > 0.8: Risk weight = 0 (highly concentrated attention); 0.5 < AttentionLevel ≤ 0.8: Risk weight = 0.3 (normal attention); 0.2 < AttentionLevel ≤ 0.5: Risk weight = 0.6 (attention starting to disperse); AttentionLevel ≤ 0.2: Risk weight = 1.0 (severely dispersed attention or fatigue).

[0076] As a possible implementation, the gear risk function f_gear(GearPosition) can be divided into: P gear (parking): Risk weight = 0; N gear (neutral): Risk weight = 0.1; R gear (reverse): Risk weight = 0.3 (need to pay attention to the rear, but low speed); D / S gear (driving): Determined by other functions according to speed.

[0077] Therefore, combining with the driving state risk coefficient I calculated by Formula 1, the driving state risk level classification is: I < 0.3: Low-risk scenario (e.g., parking, low-speed straight driving, clear sky, concentrated attention); 0.3 ≤ I < 0.7: Medium-risk scenario (e.g., normal driving in the city, normal weather, normal attention); I ≥ 0.7: High-risk scenario (e.g., high-speed driving, sharp turn, bad weather, dispersed attention).

[0078] It is understandable that the above model can also be replaced by more complex machine learning models (such as neural networks, decision trees, etc.), and more accurate risk assessments can be obtained through training with a large amount of real vehicle data. The three-level classification of safety levels (high / medium / low) can be extended to a five-level or more fine-grained classification, corresponding to a more refined display strategy.

[0079] In some embodiments, based on the data of the driver monitoring system, the driver's line of sight orientation and attention concentration can be obtained; based on the driver's line of sight orientation and attention concentration, the driver's attention state can be obtained. The driver's attention state can include a high attention state, a medium attention state, a low attention state, and a distracted state. The line of sight orientation can reflect where the driver's eyes are looking, which is the physical channel for the driver to obtain information. By calculating the proportion of the time the driver's line of sight stays on the in-vehicle display screen, it is possible to accurately quantify how much of the cognitive bandwidth used by the driver to obtain tasks is occupied. The attention concentration can reflect whether the driver's brain is processing information, which is a cognitive processing problem of information acquisition. The driver's eyes may be looking at the in-vehicle display screen, but in a distracted state with a vacant look in the eyes. At this time, although visual signals enter the eyes, they are not processed by the brain. The system can judge this internal blockage of the information processing channel by analyzing dull stares and slow blinks. A vigilant driver will actively scan the front, rearview mirror, and dashboard, which is an active and purposeful information collection. While a fatigued driver may have their head forward, but their line of sight is fixed and the scanning pattern disappears, and the information acquisition is passive and inefficient. Therefore, by obtaining the driver's attention state, it is possible to know the driver's information processing ability, so as to select a suitable task push method for them to ensure that the driver can timely obtain key information in different states and improve driving safety.

[0080] As a possible implementation, based on the driver's line of sight orientation (GazeDirection) and attention concentration (AttentionLevel) provided by the DMS system, the control layer can classify the driver's attention state into the following categories: High attention state (HighAttention), the determination condition is that the line of sight is directed towards the in-vehicle display screen area and AttentionLevel > 0.7. The high attention state indicates that the driver is actively paying attention to the in-vehicle display screen and has a relatively high information reception ability; Medium attention state (MediumAttention), the determination condition is that the line of sight is directed towards the in-vehicle display screen area and 0.4 < AttentionLevel ≤ 0.7, or the line of sight is in the dashboard area. The medium attention state indicates that although the driver's line of sight is not fully focused on the in-vehicle display screen, it is still inside the cockpit and has a certain ability to sense with peripheral vision; Low attention is defined as a state where the driver's gaze is directed toward the road or outside the vehicle and the AttentionLevel is greater than 0.5. Low attention means that the driver's attention is mainly focused on the driving task itself and should not be excessively distracted by information from the in-vehicle display screen. Distracted attention is defined as an attention level ≤ 0.4 or the detection of driver fatigue. Distracted attention indicates that the driver's attention is severely insufficient and all non-critical information should be minimized.

[0081] Understandably, the four-level classification of attention states can be simplified to two levels (focused / unfocused) or expanded to more levels. The threshold for attention concentration (0.7, 0.4, etc.) can be personalized based on different population characteristics (age, driving experience).

[0082] Therefore, considering the combined impact of driver attention status and driving risk level on task notifications as discussed above, a task display strategy is generated in S130 based on these two factors. The driving safety level and driver attention status work together; the driver attention level determines the transition of the notification bar's appearance, while the driving safety level determines how the notification bar is displayed—whether it is displayed at all.

[0083] In S140, the task display on the in-vehicle display screen is adjusted based on the task display strategy.

[0084] One possible implementation of S130 could be: generating a form transition strategy and / or display strategy based on the driver's attention level and driving risk level; the execution of the form transition strategy includes the transition duration of the notification bar's form change, and the transition duration is positively correlated with the driver's attention level; the display strategy includes the visible number of notification bars corresponding to the task, and the driving risk level is negatively correlated with the visible number of notification bars. The corresponding S140 can be adjusted to adjust the transition form of the notification bar and / or task display on the in-vehicle display screen based on the form transition strategy and / or display strategy. Therefore, not only can the task display strategy be adjusted, but different transition forms can also be used when the task changes, providing the driver with appropriate animation displays and selecting reliable display strategies to ensure the delivery of key information in different situations and guarantee driving safety.

[0085] In some embodiments, the display strategy can be set as follows: when the driving state risk level is less than or equal to a first preset level, a first preset number of notification bars are displayed; when the driving state risk level is greater than the first preset level and less than or equal to a second preset level, a second preset number of notification bars are displayed; wherein the second preset number is less than the first preset number.

[0086] By linking the risk level of the driving condition with the specific number of notifications displayed in the notification bar, a clear and enforceable quantitative control rule can be established. When the risk level is low, a larger number of notifications can be displayed, making full use of relatively safe driving scenarios to deliver information and improving the richness and efficiency of information services. When the risk level increases, the number of notifications allowed to be displayed is reduced, ensuring that the information load of the notification system can be gradually reduced as the risk of the driving environment increases. This proactively and gradually reduces visual information sources that may distract the driver as the risk increases, guiding the driver's cognitive resources more towards the driving task itself and improving driving safety.

[0087] Taking a first and second notification bar as an example, when the driving risk level is less than or equal to a first preset level, tasks in both the first and second notification bars are displayed simultaneously; when the driving risk level is greater than the first preset level but less than or equal to a second preset level, only tasks in the first notification bar are displayed; when the driving risk level is greater than the second preset level, both the first and second notification bars are hidden. Based on the real-time calculated driving risk level, the number of notifications displayed is automatically adjusted, achieving a differentiated strategy of displaying only emergency notifications in high-risk scenarios and fully displaying all types of notifications in low-risk scenarios. In other words, the lower the driving risk level, the more notifications can be displayed, and the higher the driving risk level, the fewer notifications can be displayed.

[0088] As one possible implementation, the driving risk level determines whether the first and second notification bars are displayed. The following strategies can be set: In high-risk scenarios, only emergency / safety notifications (such as incoming calls, emergency alarms, collision warnings, lane departure warnings, and other tasks directly related to safety) are allowed to be displayed. Entertainment notifications (such as music switching, social media messages, system updates, and other non-critical tasks) are completely filtered out. Notification display time is shortened, with the automatic hiding time reduced from the default 8 seconds to 3 seconds. Finally, user interaction is prohibited or delayed; except for emergency calls, clicking to jump to other notifications is prohibited, or the jump operation is delayed until the risk is reduced. In medium-risk scenarios, the number of notifications is limited, and high-priority tasks in the first notification bar are prioritized. The second notification bar is only displayed when the priority of the second task is significantly higher than a preset threshold. The complexity of notification content is limited, multi-line text is simplified to a single-line summary, and dynamic icons are simplified to static icons. The display time is appropriately shortened, for example, the automatic hiding time is adjusted to 5-6 seconds. Basic interactive capabilities are retained, allowing clicking to jump to other notifications, but a secondary confirmation mechanism (such as long-press to jump) is added. In low-risk scenarios, all types of notifications can be displayed (such as entertainment, social, and system notifications, all of which can be included in the candidate set). The dual-capsule layout is fully utilized, with the first and second tasks displayed in their complete form. The standard display duration is used, and the automatic hiding time is 8-10 seconds. Rich interactions are allowed, such as support for click-to-jump, swipe operation, and expanded details.

[0089] In some embodiments, to avoid frequent switching of safety levels due to short-term fluctuations in sensor signals, the following stability measures can be adopted: First, time window filtering, applying moving average or median filtering to sensor data, with the filtering window set to 1-2 seconds; Second, safety level hysteresis switching, responding immediately when the safety level increases and adding a delay judgment when it decreases (e.g., only downgrading the level if it remains below the threshold for 3 seconds), ensuring the safety priority principle; Third, outlier removal, performing reasonableness verification on CAN bus and DMS data, and removing obviously abnormal sensor readings (e.g., sudden changes in vehicle speed exceeding physically possible values).

[0090] In some embodiments, to improve the readability of information in the first and second notification bars, in addition to setting the first and second notification bars to capsule shapes, different states can be set for them. For example, the initial state (initialState) sets the width of the main and secondary capsules to a small shrinkage value, with an opacity of 0, and the entire capsule is centered or hidden against the edge. The initial state indicates that there are currently no background tasks that need to be displayed. The normal state (normalState) sets the main capsule to a large width, occupying the main visible area, while the secondary capsule remains shrunken and has an opacity of 0. The normal state is used in scenarios where only one task needs to be displayed, maximizing the display area and visual weight of that task. The split state (splitState) sets the main capsule to a medium width, the secondary capsule to a mini width, and both to an opacity of 1. The main capsule is positioned on the left, and the secondary capsule on the right, displaying the first two tasks simultaneously in a left-right distribution. An exception to the split state is that when the driving safety level is high-risk, even if the total number of tasks is ≥2, only the main capsule can be forcibly displayed, while the secondary capsule remains hidden, achieving a dynamic downgraded display prioritizing safety. Setting the `toNormalTransformState` allows the capsule to deform from its split or initial state with an intermediate width, gradually changing its transparency. The `toNormalTransformState` is used when tasks converge from multiple to a single state, ensuring continuous changes in width and rounded corners. Setting the `toSplitTransformState` is used for a smooth transition from the normal or other states to the split state. The primary and secondary capsules expand or contract synchronously at a medium width. The `toSplitTransformState` is used when the number of tasks evolves from a single or none to multiple (≥2).

[0091] In other embodiments, the current state machine includes an initial state, a normal state, a split state, and two transitional states. The specific number of states can be expanded or reduced according to the business requirements, for example, by adding an alarm highlight state or merging the two transitional states.

[0092] As one possible implementation, a notification bar recalculation and status update should only be triggered whenever the task set changes (addition, deletion, foreground / background switching, priority change) or the scene perception result changes (foreground component, status bar, AVM mode change, driving status safety level switch). This reduces capsule bar jitter caused by rapid foreground switching or short-term sensor fluctuations from the source. When triggering recalculation, the control layer needs to perform the following steps: First, recalculate the candidate task list. From the currently displayable notification set, filter by application scenario (front-end visibility, status bar, AVM, driving status safety level, etc.), then sort by task priority field from high to low, and retain only the top few tasks as candidates (such as the top two). Second, the control layer maintains the task sequence that was actually displayed last time and compares it with the first and second tasks calculated this time. If the total number of tasks changes (0→1, 1→0, 1→2, 2→1, 2→≥3, etc.), it will definitely be regarded as a sequence change. Even if the total number of tasks is the same, as long as either the first or second task changes, it will also be regarded as a sequence change. When there are no more tasks to be displayed and there is no entry animation being executed, it is considered that a whole collapse transition is needed. Third, select the target state based on the changes in the total number of tasks and the top two tasks: When the total number of tasks is 0, the target state is the initial state, triggering the background invisible animation and window closing logic, and completely retracting the AliveBar; When the total number of tasks is 1, if the current state is split, it will first enter the transition state to the normal state and then stabilize to the normal state. If the current state is not split, it can directly enter the normal state according to the implementation. When the total number of tasks is ≥2, if the first two tasks have not changed and the current state is split, only the internal display content will be updated without triggering a new transition animation. If the first two tasks change or enter from a non-split state, the transition to the split state will be initiated and then stabilized in the split state. If the current driving state safety level is high risk, even if the total number of tasks is ≥2, only the first notification bar will be displayed (considered as quasi-normal), and the second notification bar will be forcibly hidden.

[0093] Fourth, if a task set change event or a driving safety level switch event is received again while the notification bar background or content is still in the animation process, the above steps are re-executed without waiting for the current animation to complete. If the new calculation result is still "display ≥ 2 and the first two tasks have changed", the target is updated on the current transition path. If it changes to "display = 1 or 0", or the safety level switches to high risk and requires forced downgrading of display, the current transition is seized, and the system directly switches to the corresponding transition state to normal or the initial state. Through this design, the transition state can be seized in real time according to the latest business scenario and driving safety requirements, avoiding the misalignment problem where the completed animation interface is inconsistent with the current task state or driving scenario.

[0094] In existing technologies, notification animations are usually fixed animations. In in-vehicle scenarios, due to the frequent changes in the underlying state (foreground interface, status bar visibility, AVM mode, etc.), simple animation stacking can cause interface jitter, affecting the driver's visual stability and attention allocation. To solve this problem, the execution strategy, duration and complexity of notification transition animations can be dynamically adjusted according to the driver's gaze direction and attention concentration, avoiding the execution of invalid animations when the driver is not paying attention, or the use of overly simplified display methods when the driver is highly attentive. For example, the form transition strategy includes: when the driver's attention state is in the first state, determining the form transition duration of the notification bar to a first preset duration, and setting the task interaction mode of the notification bar to an immediate response mode; when the driver's attention state is in the second state, determining the form transition duration of the notification bar to a second preset duration, and setting the task interaction mode of the notification bar to a jump-only interaction mode; when the driver's attention state is in the third state, determining the form transition duration of the notification bar to a third preset duration, and setting the task interaction mode of the notification bar to a restricted interaction mode; when the driver's attention state is in the fourth state, determining the form transition duration of the notification bar to a fourth preset duration, and setting the task interaction mode of the notification bar to a disabled interaction mode; wherein the first preset duration is longer than the second preset duration, the second preset duration is longer than the third preset duration, and the third preset duration is longer than the fourth preset duration. The first state can be a high-attention state, where user actions such as clicking and swiping take effect immediately without delay or secondary confirmation. The second state can be a medium-attention state, where some more refined operations are canceled. The third state can be a low-attention state, where the driver spends less time looking at the in-vehicle display screen, where animations can be disabled and operation requirements can be increased to reduce the possibility of accidental touches. The fourth state can be a distracted state, where clicking and jumping are completely prohibited to prevent accidental operation from causing interface switching and affecting driving safety.

[0095] In other words, the strategy for switching task animations is determined by the driver's attention level. For example, when the driver's eyes are on the road, a quick switching or delayed display strategy is used to avoid executing complex animations when no one is watching, thus saving GPU / CPU resources. When the driver is actively paying attention to the in-vehicle display screen, full animations and rich interactions are used to make full use of the user's available cognitive resources and improve information reception efficiency. When the driver's attention is sufficient and the scene is safe, smooth and attractive animations are provided. When the driver's attention is insufficient or the scene is high-risk, the display is automatically downgraded to a minimalist display to achieve the optimal balance between safety and user experience.

[0096] As a possible implementation of attention-based animation optimization, the control layer performs the following differentiated adjustments to the state transition animation based on the driver's attention state: Transition strategies under high attention states can include: using full standard animations with a transition duration of 250-350ms, including smooth interpolation animations of size, transparency, and position; allowing complex animation effects, such as adding slightly elastic effects, breathing light effects, and other attention-grabbing visual elements; responding to interactions immediately, with user actions such as clicks and swipes taking effect immediately without delay or secondary confirmation; applicable scenarios include users actively checking notifications while parked, or drivers actively paying attention to the in-vehicle display screen while driving at low speeds.

[0097] Transition strategies for medium attention states can include: using simplified standard animations, shortening the transition time to 150-200ms, removing elastic effects, and retaining only basic fade-in / fade-out and size changes; reducing animation complexity, such as disabling decorative animations that may distract attention, such as breathing light effects and particle effects; retaining basic interactions, such as ensuring that click-to-jump functionality works, but canceling interactions that require fine-grained operation, such as hover previews; applicable scenarios include normal driving on urban roads, when the driver's gaze switches between the dashboard and the road surface.

[0098] Transition strategies under low-attention conditions can include: using a fast switching mode, such as shortening the transition time to 50-100ms, approaching a direct switch, and retaining only the transparency gradient to avoid flickering; minimizing visual interference, such as disabling all decorative animations and presenting content statically; delaying or silently displaying notifications, such as not immediately triggering a state transition animation for new notifications, but waiting for the driver's gaze to return to the in-vehicle display screen area before execution, or only using a slight edge lighting effect to indicate the existence of a new notification; and limiting interactive responses, such as disabling click-to-jump or requiring a long press for confirmation (to prevent accidental touches). Applicable scenarios include highway driving, navigation in complex road conditions, and situations where the driver's gaze is mainly on the road.

[0099] Transition strategies for distracted driving include: using a silent mode or complete hiding; delaying the display of non-emergency notifications and triggering animations only after attention is restored; using a minimalist display for emergency notifications (incoming calls, collision warnings) and guiding attention through icon flashing and sound prompts, without executing complex animations; disabling all interactions and completely prohibiting click-to-jump functionality to prevent accidental interface switching that could affect driving safety; and triggering driver alerts. If frequent distractions are detected, a voice prompt can remind the driver to pay attention to driving safety. This is applicable in high-risk scenarios such as detecting fatigued driving, frequent distractions, and emergency obstacle avoidance. When the driver's gaze is primarily on the road, non-emergency notifications can be delayed or enter silent mode, triggering animations only after the driver's gaze returns to the in-vehicle display screen area to avoid distracting attention at inappropriate times.

[0100] Furthermore, the animation strategy can be dynamically adjusted to address changes in driver attention levels. For example, when attention increases (low attention → medium attention → high attention), if a notification to be displayed is silent or delayed, the corresponding animation is immediately triggered. If the capsule is already displayed but its content is simplified due to low attention, it is gradually restored to its full display form (e.g., transitioning from static icons to dynamic icons, or expanding from single-line text to double-line details). When attention decreases (high attention → medium attention → low attention), if a complex animation is currently in progress, it is immediately interrupted and switched to a fast mode to complete the remaining transition. If the capsule has already displayed its full content, it is gradually simplified (e.g., hiding sub-capsules, downgrading the main capsule content from details to a summary). If attention drops to a distracted state, all notifications except emergency notifications are hidden.

[0101] To prevent frequent animation switching in cases of attention fluctuations, time window filtering (1-2 seconds) can be applied to the DMS gaze data to avoid triggering a complete animation when the gaze briefly sweeps across the in-vehicle display screen. A hysteresis threshold for attention state switching is defined, requiring a duration of more than 1 second for switching from low attention to high attention, and immediate response for reverse switching (safety first principle).

[0102] As one possible implementation, a state combination can be determined based on the current driver's attention state and the current driving state risk level; based on a preset two-dimensional decision matrix, at least one of the form transition strategy and display strategy corresponding to the state combination can be obtained as a task display strategy; wherein, the preset two-dimensional decision matrix defines the correspondence between different state combinations and task display strategies.

[0103] By combining two key dimensions—driver attention state and driving state risk level—to form a unique state combination, and directly mapping the corresponding morphological transition strategy and / or display strategy based on a preset two-dimensional decision matrix, the complex, real-time computational dynamic decision-making process can be transformed into an efficient, deterministic lookup operation. This reduces the computational complexity and processing latency of the strategy at each state change, ensuring the real-time nature of strategy decisions and system response speed. Simultaneously, the matrix-based preset relationships allow for refined and forward-looking customization and optimization of strategies under different state combinations based on safety regulations and human-computer interaction experience, ensuring that the mapping from state recognition to strategy execution is both scientific, reasonable, stable, and reliable. Furthermore, the structured design makes the maintenance and adjustment of the strategy set clear and convenient; only the configuration at the corresponding position in the matrix needs to be modified, improving the system's maintainability and adaptability.

[0104] An example of a pre-defined two-dimensional decision matrix is ​​shown in Table 1: Table 1 For example, in low-risk and high-attention scenarios (such as when a user actively checks while parked), the first two tasks are fully displayed with a 250-350ms animation, supporting full interactivity. In medium-risk and low-attention scenarios (such as when driving on city roads with the user's eyes on the road), only the tasks in the first notification bar are displayed with a 50-100ms fast switching and click-to-jump functionality is disabled. In high-risk and distracted scenarios (such as when making sharp turns at high speeds or when the driver is fatigued), all notifications except for incoming call / collision warnings are hidden, and emergency notifications are announced via voice without any animation transition effects.

[0105] As a possible implementation of a form transition strategy, an animation system decoupled from the state machine can be implemented through a declarative UI framework at the rendering layer, supporting dynamic parameter adjustment based on attention awareness. First, the width and height of the main and secondary capsules are interpolated using an animable numerical type to smoothly transition from the current state size to the target state size. The animation duration parameters are dynamically adjusted according to the attention state: high attention state: 250-350ms (standard duration), medium attention state: 150-200ms (simplified duration), low attention state: 50-100ms (fast duration), and distracted attention state: size animation is disabled, and the capsules are switched directly. Second, floating-point animation is used to control the opacity of the main and secondary capsules: in the transition from no task to single task, the main capsule gradually increases from 0 to 1; in the transition from single task to dual task, the secondary capsule gradually increases from 0 to 1, while the main capsule remains at 1; in scenarios with fewer tasks, the transition is reversed. In low attention or distracted attention states, the opacity animation is still retained (to avoid flickering), but the duration is compressed to the shortest possible value.

[0106] It is understandable that, in addition to using a declarative UI framework, it can also be implemented using a traditional View system, an OpenGL layer, or other UI frameworks, as long as the same state machine decision and animation parameter strategy is followed.

[0107] When aligning the first and second notification bars, position offset animation can be used to control the horizontal starting position of the capsule relative to the parent container to ensure that it is displayed in the center or aligned left and right in different states. For example, in a high attention state, a slight elasticity or jitter animation is allowed to indicate changes in the number of tasks. In a medium / low attention state, the elasticity effect is disabled, and a linear or ease-out easing function is used to reduce visual interference. The amplitude and frequency are controlled by elasticity parameters (stiffness, damping) to ensure that it does not interfere with the driver's line of sight.

[0108] In the animation timing and callback chain, the control layer sends the target state and attention-aware animation parameters (duration, easing function type, whether elasticity is enabled, etc.) to the background container. The background container configures the animation execution strategy according to the parameters, and then passes the state and parameters to the rendering layer. The rendering layer derives the animation target values ​​such as size, transparency, position, and rounded corners and the interpolator configuration according to the state and parameters, and calls the animation kernel to perform interpolation. After the animation is completed, it sends a callback to the background container with the completed state and the actual execution duration. The background container then reports to the control layer. The control layer checks whether it is consistent with the initial target stable state. If it is consistent, the next round of state advancement can be started. When the driver's attention state or driving safety level changes significantly, the control layer can send an interrupt signal, and the rendering layer immediately terminates the current animation and switches to the new parameters for execution.

[0109] Understandably, the elastic or acceleration-deceleration easing function can be replaced with linear, piecewise linear, Bezier curve, etc., depending on the specific vehicle system performance, as long as the transition time and maximum speed meet the automotive-grade requirements for interface responsiveness and readability.

[0110] As a possible configuration table for attention-aware animation parameters, the control layer can maintain a set of preset configurations for attention-aware animation parameters, which could be: When the attention state is high, the size animation duration is 300ms, the transparency animation duration is 250ms, and the position animation duration is 280ms. The easing function is `spring` (stiffness=300, damping=0.7), with `elasticity` enabled (true) and `decorative animation` enabled (true, for breathing light effect and icon animation). When the attention state is medium, the size animation duration is 180ms, the transparency animation duration is 150ms, and the position animation duration is 180ms. The easing function is `ease-out`, with `elasticity` enabled (false) and `decorative animation` enabled (false). When the attention state is low, the size animation duration is 80ms, the transparency animation duration is 60ms, and the position animation duration is 80ms. The easing function is `linear`, with `elasticity` enabled (false) and `decorative animation` enabled (false). When the attention state is distracted, the size animation duration is 0ms (direct toggle), the transparency animation duration is 50ms (only to avoid flickering), the position animation duration is 0ms, the easing function is `linear`, with `elasticity` enabled (false) and `decorative animation` enabled (false).

[0111] Understandably, the above parameters can be calibrated and optimized based on the screen refresh rate, hardware performance, and user test feedback of different vehicle models. For example, the currently used discrete parameter configuration table (selecting different preset parameter groups according to attention status) can be replaced with continuous parameter interpolation (continuously adjusting the animation duration based on the floating-point value of attention concentration), and the animation duration adjustment ratio (standard: simplified: fast = 300ms: 180ms: 80ms) can be adjusted according to the screen refresh rate and hardware performance of different vehicle models.

[0112] When applying the task display method in a vehicle, a symmetrical arrangement of a first and second notification bar can be set on the in-vehicle display screen (such as the central control unit). This arrangement can be left-right symmetrical, top-bottom symmetrical, or diagonally symmetrical. A task display device is then configured, communicating with the in-vehicle display screen. This device executes the task display method, dynamically adjusting the display status of the first and second notification bars based on the driver's attention level and the risk level of the driving condition after scene filtering and priority sorting.

[0113] Figure 2 This is a schematic diagram of the structure of a task display device provided in an exemplary embodiment of this application, as shown below. Figure 2 As shown, the task display device 2 includes: an acquisition module 21 for acquiring vehicle status data, environmental data, and driver monitoring system data; an obtaining module 22 for obtaining driver attention status and driving status risk level based on vehicle status data, environmental data, and driver monitoring system data; a generation module 23 for generating a task display strategy based on driver attention status and driving status risk level; and an adjustment module 24 for adjusting the task display on the in-vehicle display screen based on the task display strategy.

[0114] As one possible implementation, the generation module 23 can be configured to: generate a form transition strategy and / or a display strategy based on the driver's attention state and driving state risk level; the execution of the form transition strategy includes the transition duration of the notification bar form change, and the transition duration is positively correlated with the driver's attention state concentration; the display strategy includes the visible number of notification bars corresponding to the task, and the driving state risk level is negatively correlated with the visible number of notification bars; wherein, the adjustment module 24 can be configured to: adjust the transition form of the notification bar and / or task display in the vehicle display screen based on the form transition strategy and / or the display strategy.

[0115] As one possible implementation, the generation module 23 can be configured to: determine a state combination based on the current driver attention state and the current driving state risk level; and obtain at least one of the form transition strategy and display strategy corresponding to the state combination based on a preset two-dimensional decision matrix, as a task display strategy; wherein the preset two-dimensional decision matrix defines the correspondence between different state combinations and task display strategies.

[0116] As one possible implementation, the generation module 23 can be configured to: display a first preset number of notification bars when the driving state risk level is less than or equal to a first preset level; and display a second preset number of notification bars when the driving state risk level is greater than the first preset level but less than or equal to a second preset level; wherein the second preset number is less than the first preset number.

[0117] As one possible implementation, the generation module 23 can be configured as follows: when the driver's attention state is in the first state, the form transition time of the notification bar is determined to be a first preset time, and the task interaction mode of the notification bar is set to immediate response mode; when the driver's attention state is in the second state, the form transition time of the notification bar is determined to be a second preset time, and the task interaction mode of the notification bar is set to retain only jump interaction mode; when the driver's attention state is in the third state, the form transition time of the notification bar is determined to be a third preset time, and the task interaction mode of the notification bar is set to restricted interaction mode; when the driver's attention state is in the fourth state, the form transition time of the notification bar is determined to be a fourth preset time, and the task interaction mode of the notification bar is set to prohibited interaction mode; wherein, the first preset time is longer than the second preset time, the second preset time is longer than the third preset time, and the third preset time is longer than the fourth preset time.

[0118] As one possible implementation, the task display device 2 can be configured to: sort the tasks in the candidate task set in descending order of priority based on a preset task priority rule to determine the tasks to be displayed; wherein, the task display strategy is generated based on the tasks to be displayed, and the preset task priorities include: the priority of safety and intervention tasks is higher than that of navigation and driving tasks, and the priority of navigation and driving tasks is higher than that of entertainment and ordinary tasks.

[0119] As one possible implementation, module 22 can be configured to: calculate the driving state risk coefficient based on vehicle state data and a first preset weight, environmental data and a second preset weight, driver monitoring system data and a third preset weight; and determine the driving state risk level based on the driving state risk coefficient.

[0120] As one possible implementation, module 22 can be configured to: obtain the driver's attention state based on vehicle status data, environmental data, and driver monitoring system data, including: obtaining the driver's gaze direction and attention concentration based on the driver monitoring system data; and obtaining the driver's attention state based on the driver's gaze direction and attention concentration.

[0121] As one possible implementation, the adjustment module 24 can be configured to: acquire the visibility signal of the foreground component, the visibility signal of the status bar, and the panoramic image forced preview signal; if the visibility signal of the foreground component indicates that the target component corresponding to the task is already visible in the foreground, then filter out tasks that have not set the ignore task conflict flag; if the visibility signal of the status bar indicates that the status bar is not visible, then control the tasks that have not set the ignore status bar constraint flag not to be displayed; if the panoramic image forced preview signal indicates that the vehicle display screen is in the panoramic image forced preview state, then mark the tasks that have not set the ignore panoramic image flag as prohibited from clicking to jump.

[0122] An electronic device includes: a processor; a memory for storing processor-executable instructions; and a processor for executing the task demonstration method described in the embodiments provided in this application.

[0123] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0124] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0125] like Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0126] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device 30 to perform desired functions.

[0127] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the task demonstration methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0128] In one example, the electronic device 30 may also include an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0129] When the electronic device is a standalone device, the input device 33 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0130] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0131] The output device 34 can output various information to the outside, including determined distance information, direction information, etc. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0132] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 30 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 30 may include any other suitable components depending on the specific application.

[0133] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0134] A computer-readable storage medium stores a computer program for performing the task demonstration method described in the embodiments provided in this application.

[0135] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0136] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A task display method, characterized in that, When applied to in-vehicle display screens, task display methods include: Acquire vehicle status data, environmental data, and driver monitoring system data; Based on vehicle status data, environmental data, and driver monitoring system data, the driver's attention status and driving status risk level are obtained; Based on the driver's attention state and the driving state risk level, a task display strategy is generated; Based on the aforementioned task display strategy, the task display on the in-vehicle display screen is adjusted.

2. The task display method according to claim 1, characterized in that, Based on the driver's attention state and the driving state risk level, a task display strategy is generated, including: Based on the driver's attention state and the driving state risk level, a form transition strategy and / or display strategy are generated; the execution of the form transition strategy includes the transition duration of the notification bar form change, and the transition duration is positively correlated with the concentration of the driver's attention state; the display strategy includes the visible number of notification bars corresponding to the task, and the driving state risk level is negatively correlated with the visible number of notification bars. The adjustment of the task display on the in-vehicle display screen based on the task display strategy includes: Based on the aforementioned form transition strategy and / or display strategy, adjust the transition form of the notification bar and / or task display in the vehicle display screen.

3. The task display method according to claim 2, characterized in that, Based on the driver's attention state and the driving state risk level, a form transition strategy and / or display strategy are generated, including: Based on the current driver attention state and the current driving state risk level, determine the state combination; Based on a preset two-dimensional decision matrix, at least one of a form transition strategy and a display strategy corresponding to the state combination is obtained as the task display strategy; wherein, the preset two-dimensional decision matrix defines the correspondence between different state combinations and task display strategies.

4. The task display method according to claim 2, characterized in that, Based on the driver's attention state and the driving state risk level, a display strategy is generated, including: When the driving state risk level is less than or equal to the first preset level, the first preset number of notification bars are displayed. When the driving status risk level is greater than the first preset level and less than or equal to the second preset level, a second preset number of notification bars are displayed; wherein, the second preset number is less than the first preset number.

5. The task display method according to claim 2, characterized in that, Based on the driver's attention state and the driving state risk level, a morphological transition strategy is generated, including: When the driver's attention state is in the first state, the transition time of the notification bar is determined to be the first preset time, and the task interaction mode of the notification bar is set to the immediate response mode. When the driver's attention state is in the second state, the transition time of the notification bar is determined to be the second preset time, and the task interaction mode of the notification bar is set to retain only the jump interaction mode. When the driver's attention state is in the third state, the transition time of the notification bar is determined to be the third preset time, and the task interaction mode of the notification bar is set to the restricted interaction mode. When the driver's attention state is in the fourth state, the transition time of the notification bar is determined to be the fourth preset time, and the task interaction mode of the notification bar is set to the disabled interaction mode. Wherein, the first preset duration is greater than the second preset duration, the second preset duration is greater than the third preset duration, and the third preset duration is greater than the fourth preset duration.

6. The task display method according to claim 1, characterized in that, The task display method also includes: Based on preset task priority rules, the tasks in the candidate task set are sorted in descending order of priority to determine the tasks to be displayed; wherein, the task display strategy is generated based on the tasks to be displayed, and the preset task priorities include: the priority of safety and intervention tasks is higher than that of navigation and driving tasks, and the priority of navigation and driving tasks is higher than that of entertainment and general tasks.

7. The task display method according to claim 1, characterized in that, Based on vehicle status data, environmental data, and driver monitoring system data, the driver's attention status is obtained, including: The driving status risk coefficient is calculated based on the vehicle status data and the first preset weight, the environmental data and the second preset weight, the driver monitoring system data and the third preset weight. The driving state risk level is determined based on the driving state risk coefficient.

8. The task display method according to claim 1, characterized in that, Based on vehicle status data, environmental data, and driver monitoring system data, the driver's attention status is obtained, including: Based on the data from the driver monitoring system, the driver's gaze direction and attention concentration are obtained; The driver's attention state is obtained based on the driver's gaze direction and the degree of attention concentration.

9. The task display method according to claim 1, characterized in that, Adjusting the task display on the vehicle-mounted display screen includes: Obtain the visibility signals of foreground components, status bar visibility signals, and forced preview signals for panoramic images; If the foreground component visibility signal indicates that the target component corresponding to the task is already visible in the foreground, then tasks that do not have the ignore task conflict flag set will be filtered out. If the status bar visibility signal indicates that the status bar is not visible, then tasks that have not been set to ignore status bar constraint flags will not be displayed. If the panoramic image forced preview signal indicates that the vehicle display screen is in the panoramic image forced preview state, then the task that has not set the ignore panoramic image mark will be marked as prohibited from clicking to jump.

10. A vehicle, characterized in that, include: The vehicle-mounted display screen is equipped with a notification bar. A task display device is communicatively connected to the vehicle-mounted display screen. The task display device is used to execute the task display method according to any one of claims 1-9 to adjust the task display of the notification bar.

Images