Information display method, system and vehicle for a vehicle

Abstract

This application provides a method, system, and vehicle for displaying vehicle information. The method includes: responding to an information display instruction for at least one target object around the vehicle; acquiring attribute information of the target object, wherein the attribute information represents the appearance characteristics and / or operating status of the target object; determining a display strategy for the target object based on the attribute information, wherein the display strategy represents the rules for displaying the target object; determining, based on the display strategy and attribute information, a graphic element to be displayed corresponding to the target object, and determining the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes represent the visual appearance of the graphic element to be displayed on a display client deployed on the vehicle; and invoking the display strategy to display the graphic element to be displayed with the rendering attributes on the display client. This application solves the technical problem of poor information display effects in vehicles.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and more specifically, to a method, system, and vehicle for displaying vehicle information. Background Technology

[0002] Currently, vehicle panoramic imaging systems typically use fixed graphics (e.g., rectangles of uniform color) to statically label each target object when displaying graphic elements on the display client, such as the head-up display (HUD). This method results in a single, fixed information dimension, leading to visual redundancy, the inundation of critical risk targets, and an inability to guide the driver's attention. It also easily causes cognitive load and visual interference, severely limiting the safety effectiveness of assisted driving. Therefore, the technical problem of poor vehicle information display remains.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a method, system, and vehicle for displaying vehicle information, in order to at least solve the technical problem of poor information display effect in vehicles.

[0005] According to one aspect of the embodiments of this application, a method for displaying vehicle information is provided, wherein the method may include: responding to an information display instruction for at least one target object around the vehicle, acquiring attribute information of the target object, wherein the attribute information is used to represent the appearance characteristics and / or operating status of the target object; determining a display strategy for the target object based on the attribute information, wherein the display strategy is used to represent the rules for displaying the target object; determining a graphic element to be displayed corresponding to the target object based on the display strategy and the attribute information, and determining the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes are used to represent the visual appearance of the graphic element to be displayed on a display client deployed on the vehicle; and invoking the display strategy to display the graphic element to be displayed with rendering attributes on the display client.

[0006] Furthermore, based on the attribute information, the display strategy for the target object is determined, including: determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy.

[0007] Further, the candidate display strategy includes a contour display strategy, which represents the rules for displaying the geometric boundaries of the target object. The attribute information includes the category information and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the running state of the target object. The candidate display strategy that matches the attribute information is determined as the display strategy from at least one candidate display strategy, including: determining the contour display strategy as the display strategy that matches the attribute information in response to the attribute information satisfying at least one of the following conditions: the semantic category is a static category, wherein the target object of the static category is in a stationary state; the dynamic parameters indicate that the speed of the target object is less than a speed threshold, and the distance between the target object and the vehicle is less than a distance threshold.

[0008] Furthermore, the candidate display strategy includes a guiding display strategy, which represents the rules for displaying the movement trend of the target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance characteristics of the target object, and the dynamic parameters represent the running state of the target object. The candidate display strategy that matches the attribute information among at least one candidate display strategy is determined as the display strategy, including: in response to the attribute information satisfying at least one of the following conditions, the guiding display strategy is determined as the display strategy that matches the attribute information: the semantic category is a dynamic category, wherein the target object of the dynamic category is in a moving state; the dynamic parameters represent the lane where the target object is located, and there is a correlation between the dynamic parameters and the lane where the vehicle is located.

[0009] Furthermore, the candidate display strategy includes a risk display strategy, which represents the rules for displaying the area where a collision risk occurs between the vehicle and the target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance characteristics of the target object, and the dynamic parameters represent the operating state of the target object. The candidate display strategy that matches the attribute information among at least one candidate display strategy is determined as the display strategy, including: in response to the attribute information satisfying at least one of the following conditions, the risk display strategy is determined as the display strategy that matches the attribute information: the dynamic parameters indicate that the speed of the target object is greater than a speed threshold; the distance between the target object and the vehicle is less than a distance threshold.

[0010] Furthermore, the rendering attributes that the graphic elements to be displayed need to satisfy are determined, including: generating initial rendering attributes of the graphic elements based on the display strategy; and adjusting the initial rendering attributes using attribute information to obtain the rendering attributes.

[0011] Furthermore, the rendering attributes include at least one of the following: color information of the graphic element, transparency of the graphic element, brightness of the graphic element, pulsation frequency of the graphic element, and size information of the graphic element. Using this attribute information, the initial rendering attributes are adjusted to obtain rendering attributes, including at least one of the following: based on the attribute information, determining the risk level of a collision between the vehicle and the target object, and adjusting the color information according to the risk level, wherein the risk level matches the color information; based on the attribute information, determining the interference level of the target object on the vehicle, and adjusting the transparency and size information according to the interference level, wherein the interference level is negatively correlated with transparency and negatively correlated with size information; based on the attribute information, determining the warning priority of the target object, and adjusting the pulsation frequency according to the warning priority, wherein the warning priority is positively correlated with the pulsation frequency; based on the attribute information, determining the brightness information of the vehicle's environment, and adjusting the element brightness according to the brightness information, wherein the brightness information is positively correlated with the element brightness.

[0012] Furthermore, the method also includes: responding to a target object having only one element, determining the position coordinates of the graphic element to be displayed in the display coordinate system of the display client based on the position information of the target object in the vehicle's environment; determining a layout strategy for the graphic element to be displayed on the display client based on the position coordinates, wherein the layout strategy represents the rules for laying out the graphic element to be displayed on the display client; responding to a target object having multiple elements, determining the display priority corresponding to each of the multiple target objects; determining the layout strategy based on the position coordinates and the display priority; and invoking the display strategy to display the graphic element to be displayed with rendering attributes on the display client, including: invoking the layout strategy to lay out the graphic element to be displayed with rendering attributes on the display client; and invoking the display strategy to display the laid-out graphic element to be displayed on the display client.

[0013] According to another aspect of the embodiments of this application, a vehicle information display system is also provided. This system may include: a data input layer, configured to respond to an information display instruction for at least one target object around the vehicle, and acquire attribute information of the target object, wherein the attribute information represents the appearance characteristics and / or operating status of the target object; a processing engine, configured to determine a display strategy for the target object based on the attribute information, wherein the display strategy represents the rules for displaying the target object; and based on the display strategy and attribute information, determine the graphic element to be displayed corresponding to the target object, and determine the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes represent the visual appearance of the graphic element to be displayed on a display client deployed on the vehicle; and a display layer, configured to invoke the display strategy and display the graphic element to be displayed with rendering attributes on the display client.

[0014] According to another aspect of the embodiments of this application, a vehicle information display device is also provided. The device may include: an acquisition module, configured to acquire attribute information of the target object in response to an information display instruction for at least one target object around the vehicle, wherein the attribute information is used to represent the appearance characteristics and / or operating status of the target object; a first determination module, configured to determine a display strategy for the target object based on the attribute information, wherein the display strategy is used to represent the rules for displaying the target object; a second determination module, configured to determine the graphic element to be displayed corresponding to the target object based on the display strategy and the attribute information, and to determine the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes are used to represent the visual appearance of the graphic element to be displayed on a display client deployed on the vehicle; and an invocation module, configured to invoke the display strategy and display the graphic element to be displayed with rendering attributes on the display client.

[0015] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0017] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the methods in various embodiments of this application.

[0020] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0021] In this embodiment, if an information display instruction for a target object around the vehicle is detected, the attribute information of the target object can be obtained. Based on the attribute information, a display strategy for the target object can be determined. Based on the display strategy and attribute information, the graphic element to be displayed corresponding to the target object can be determined, and the rendering attributes required for the graphic element to be displayed can be determined. Thus, the display strategy can be invoked, and the graphic element with the aforementioned rendering attributes can be displayed on the display client. In other words, in this embodiment, the display strategy and rendering attributes corresponding to the target object are dynamically determined through attribute information, thereby generating differentiated and adaptive graphic elements to be displayed, which are accurately presented on the display client. Compared to the method in related technologies that uniformly uses fixed rectangular boxes to statically label each target object, the method in this embodiment intelligently adjusts the graphic element to be displayed and the rendering attributes based on attribute information, achieving layered and optimal display of information, effectively avoiding visual redundancy and the submersion of key target objects, improving information recognition and driver attention guidance capabilities, thereby solving the technical problem of poor vehicle information display effect and achieving the technical effect of improving vehicle information display effect. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 This is a schematic diagram of an application scenario for displaying vehicle information according to an embodiment of this application;

[0024] Figure 2 This is a flowchart of a vehicle information display method according to an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of a voice-interactive panoramic image information display system on a HUD according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of a vehicle information display system according to an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of a vehicle information display device according to an embodiment of this application;

[0028] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 This is a schematic diagram illustrating an application scenario for displaying vehicle information according to an embodiment of this application, such as... Figure 1 As shown, the scenario described above may include terminal device 10, network 20, and vehicle 30. Terminal device 10 can be used to obtain information display commands from vehicle users (e.g., drivers, passengers) regarding whether information needs to be displayed on the vehicle. These commands can be voice commands or touch commands on the terminal device. The terminal device can be a mobile phone, computer, or other device used by the driver in the vehicle, or an interactive interface used for user interaction. The information display commands can be sent to vehicle 30 via network 20. At this point, vehicle 30 needs to execute steps S102 to S108 to implement the vehicle's information display process.

[0032] The following steps can be performed by vehicle 30: Step S102, respond to an information display instruction for at least one target object around the vehicle and obtain the attribute information of the target object; Step S104, determine the display strategy of the target object based on the attribute information; Step S106, determine the graphic element to be displayed corresponding to the target object based on the display strategy and attribute information, and determine the rendering attributes required for the graphic element to be displayed; Step S108, call the display strategy and display the graphic element to be displayed with rendering attributes on the display client.

[0033] In this embodiment, through steps S102 to S108, the display strategy and rendering attributes corresponding to the target object are dynamically determined based on attribute information, thereby generating differentiated and adaptive graphic elements to be displayed, which are accurately presented on the display client. Compared with the method of statically labeling each target object using fixed rectangles in related technologies, the method in this embodiment intelligently adjusts the graphic elements to be displayed and rendering attributes based on attribute information, realizing the hierarchical and optimal display of information, effectively avoiding visual redundancy and the submersion of key target objects, improving information recognition and driver attention guidance, thereby solving the technical problem of poor information display effect in vehicles and achieving the technical effect of improving the information display effect of vehicles.

[0034] According to an embodiment of this application, a method for displaying vehicle information is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a method for displaying vehicle information. Figure 2 This is a flowchart of a vehicle information display method according to an embodiment of this application, such as... Figure 2 As shown, the method may include the following steps.

[0036] Step S202: In response to an information display instruction for at least one target object around the vehicle, obtain the attribute information of the target object.

[0037] In the technical solution provided by step S202 of the embodiments of this application, attribute information can be used to represent the appearance features and / or operating state of the target object. The aforementioned attribute information can refer to a multi-dimensional data set used to comprehensively characterize the intrinsic properties and external state of the target object, and can include two types of information: appearance features and operating state. The appearance features can cover the semantic category of the target object (e.g., vehicle, pedestrian, road shoulder) and geometric contour information (e.g., boundary shape, size, edge continuity). The operating state can cover the dynamic parameters of the target object (e.g., relative distance, relative speed, relative acceleration) and spatial position (e.g., azimuth angle and lateral offset relative to the vehicle). It should be noted that, in order to ensure the accuracy of target object identification in multiple dimensions (e.g., appearance dimension, motion dimension), the attribute information in the embodiments of this application can be multi-dimensional attribute information.

[0038] Optionally, the aforementioned target object can refer to an external object in the environment surrounding the vehicle that is identifiable by the vehicle and has a physical entity or a clearly defined spatial boundary. The target object can include static and dynamic entities related to the vehicle's driving safety. The static entities may include, but are not limited to, road shoulders, lane lines, traffic signs, and obstacles around the vehicle. The dynamic entities may include, but are not limited to, other vehicles around the vehicle (e.g., vehicles in front), pedestrians on the side, bicycles, etc. The aforementioned target object can be simply referred to as a target.

[0039] Optionally, the aforementioned information display instructions can refer to control commands input by the occupant (e.g., the driver) via natural language, used to actively request the display of information related to a specific target object. The form of these information display instructions can be voice-based, without specific limitations. These instructions can be used to express the occupant's concern about target objects in the vehicle's surrounding environment. For example, the instructions could be "Check for obstacles on the right" or "What's ahead on the left?"

[0040] In this embodiment, if an information display instruction for at least one target object around the vehicle is detected, the attribute information of the target object can be obtained.

[0041] Optionally, during vehicle operation, information display commands issued by the driver and passengers can be detected in real time. These commands can be parsed to determine the target object from which the driver and passengers intend to display information. Multi-dimensional attribute information of the corresponding target object can then be extracted from the vehicle's panoramic imaging system.

[0042] Optionally, if the information display command is a voice command, the vehicle's built-in voice acquisition device can continuously monitor the voice signals (voice commands) in the vehicle's cabin environment. Upon detecting a preset wake-up word or voice activation mode from the voice signals, the voice recognition service can be activated. It can identify whether the content of the voice signals conforms to a preset semantic template, such as "Check the obstacle on the right" or "What car is in front of the left?". The voice waveform of the voice signals can be converted from analog to digital and then sent to the voice recognition engine to ensure that the source of the voice signals is clear, the intent is explicit, and background noise unrelated to the vehicle's operating status is effectively filtered out.

[0043] Optionally, the vehicle's semantic understanding module can map the voice text identified from the voice command to a preset intent library. Through keyword matching and syntactic structure analysis, it can determine the spatial location (e.g., "left side", "front") and semantic category (e.g., "vehicle", "shoulder", "pedestrian") of the target object referred to in the voice command, thereby locking the potential area of ​​the target object in the panoramic image.

[0044] Optionally, the vehicle's target perception and attribute analysis module can identify the attribute information of the target object. This module receives real-time video streams from the vehicle's surround-view camera array, combines them with attitude and motion data provided by the vehicle bus, and uses a deep learning model to identify and track the target object within the area indicated by the voice command, obtaining the identification result. This identification result can include: semantic category, used to characterize the physical nature of the target object, such as a vehicle, pedestrian, bicycle, shoulder, lane line, etc.; contour information, used to depict the geometric boundary shape and size of the target object; dynamic parameters, including the relative distance, relative speed, and relative acceleration between the target object and the vehicle, used to describe the motion trend of the target object; and spatial position, based on the vehicle's own coordinate system, characterizing the azimuth and lateral offset of the target object in three-dimensional space. These identification results can be determined as the attribute information of the target object.

[0045] In the embodiments of this application, the above method realizes the accurate mapping from the intention of displaying information required by the driver to the environmental perception data, breaking through the limitations of passive and full display in related technologies, ensuring that necessary attribute information is extracted when necessary for specific target objects, reducing computational load and improving response efficiency.

[0046] Step S204: Determine the display strategy for the target object based on the attribute information.

[0047] In the technical solution provided by step S204 of the embodiments of this application, the display strategy is used to represent the rules for displaying the target object. The display strategy can refer to decision rules used to regulate the visual presentation of the target object on the HUD. The essence of the display strategy is to transform the attribute information of the target object into executable graphical expression logic, which guides the morphological construction and visual organization of the augmented reality graphical elements of the target object. The display strategy can also be called a visualization modality, and its core function is to select an appropriate graphical expression paradigm based on the appearance characteristics and operating state of the target object, thereby achieving differentiated, contextualized, and semantic presentation of information.

[0048] For example, the aforementioned display strategies may include, but are not limited to, contour enhancement modality, directional indication modality, and risk labeling modality. The contour enhancement modality can be used to accurately delineate the boundary structure of static or low-speed target objects, suitable for target objects requiring high-precision spatial perception, such as road shoulders and bollards. The directional indication modality can be used to characterize the movement trend or path guidance relationship of target objects, suitable for providing auxiliary prompts for the driving direction of vehicles in adjacent lanes or for vehicles' lane-changing intentions. The risk labeling modality can be used to warn of potential collision threats, suitable for dynamic target objects with high relative speed and short expected collision time, enhancing the visual warning intensity through geometric expansion and dynamic effects.

[0049] It should be noted that the display strategy in this application does not directly define the specific pixels or colors of augmented reality graphic elements, but rather specifies the expression type, semantic mapping logic, and applicable conditions of augmented reality graphic elements to ensure that the displayed content is highly matched with the driving situation and safety requirements, and to avoid information homogenization and cognitive confusion.

[0050] In this embodiment, after obtaining the attribute information of the target object, the display strategy of the target object can be determined based on the attribute information.

[0051] Optionally, in determining the display strategy for the target object based on attribute information, the vehicle's display strategy decision engine can receive the target object's attribute information. This attribute information is used to distinguish the physical nature of the target object, such as vehicles, pedestrians, bicycles, road shoulders, lane lines, etc., with different categories corresponding to different visual expression priorities and paradigms. When the semantic category is pedestrian or bicycle, the target object can be determined as a highly vulnerable target, triggering the candidate set of contour enhancement modality or risk labeling modality. When the semantic category is road shoulder or lane line, the contour enhancement modality is preferentially selected to enhance boundary perception accuracy.

[0052] Optionally, based on the preliminary analysis of the target object's physical nature, the dynamic parameters of the target object can be further evaluated to determine whether they meet the preset strategy trigger thresholds: if the relative speed is greater than the speed threshold and the relative distance is less than the distance threshold, the collision risk of the target object is determined to meet the activation conditions of the risk identification mode; if the relative speed is close to zero and the distance is less than the short distance threshold, and the semantic category is non-motorized obstacle, the target object is determined to be suitable for the contour enhancement mode to assist in precise avoidance. The spatial position of the target object can be used to determine whether it is within the driver's main field of vision. If it is within the effective display range of the HUD, the display strategy is allowed to take effect; otherwise, it is suppressed. The combined judgment based on the above attribute information can be completed using a predefined decision tree or a lightweight rule base, ensuring that the determination of the display strategy is interpretable and engineering-feasible.

[0053] In this embodiment, the above method achieves a paradigm shift in display strategy from passive fixed to active adaptation. Visual expression is no longer a uniform rectangle, but rather intelligently reconstructed based on the semantic connotation and dynamic behavior of the target object, significantly improving the accuracy and efficiency of information transmission. Through layer-by-layer matching of attribute information and display strategy rules, without increasing display complexity, it achieves graded expression of risk for the target object and precise guidance of visual attention, effectively reducing the driver's cognitive load and enhancing situational awareness in complex traffic scenarios.

[0054] Step S206: Based on the display strategy and attribute information, determine the graphic element to be displayed corresponding to the target object, and determine the rendering attributes required for the graphic element to be displayed.

[0055] In the technical solution provided by step S206 of the embodiments of this application, the rendering attribute can be used to represent the visual appearance of the graphic element to be displayed on the display client, which is deployed on the vehicle.

[0056] Optionally, the graphic element to be displayed can refer to an augmented reality visual symbol generated based on the display strategy and attribute information of the target object, used for presentation on a head-up display. The essence of this graphic element is to transform the semantic and dynamic characteristics of the target object into a visually expressible geometric form, replacing the static labeling method of traditional fixed rectangular boxes. The graphic element to be displayed can also be called an augmented display graphic element. The type of this graphic element can be determined by the display strategy, and the types can include contour-enhanced graphics, directional graphics, and risk-marking graphics. Contour-enhanced graphics can be used to precisely fit the boundary geometric features of the target object, such as closed polygons or curved contours. Directional graphics can be used to represent movement trends or path associations, such as arrow lines or trajectory extensions. Risk-marking graphics can be used to warn of potential collision risks, such as fan-shaped areas, dynamically expanding areas, or warning halos.

[0057] Optionally, the aforementioned rendering attributes can refer to a set of adjustable parameters used to control the visual presentation of the graphic elements to be displayed on a head-up display. Their function is to optimize the perceptual salience and visual adaptability of the graphics based on the real-time state of the target object and environmental conditions. These rendering attributes may include the graphic color, transparency, pulsation frequency, and graphic size of the graphic elements to be displayed. Specifically, the graphic color can be used to characterize risk levels or semantic categories; for example, green indicates low risk, yellow indicates medium concern, and red indicates high risk. The color value is dynamically mapped based on preset color levels and attribute thresholds. Transparency is used to adjust the degree to which the graphic obscures the background, adjusting according to the relative distance to the target or the intensity of ambient light to ensure the visibility of the graphic elements to be displayed without obscuring key road information. The pulsation frequency can be used to enhance the visual appeal of high-priority target objects. When the target object meets the risk threshold conditions, the graphic elements to be displayed periodically change brightness at a specific frequency, with the frequency value positively correlated with relative speed and expected collision time. The graphic size is used to match the spatial scale and perceptual distance of the target; the size scales inversely with the relative distance to the target, ensuring that graphics for nearby targets do not exceed limits and graphics for distant targets do not become blurred. The aforementioned rendering attributes collectively constitute the visual expression dimension of graphics, achieving an upgrade from "whether it is displayed" to "how to display it better".

[0058] Optionally, the aforementioned display client can refer to an optical display device deployed inside the vehicle for projecting augmented reality graphic elements into the driver's forward field of vision. This display client can be a HUD or an Augmented Reality Head-Up Display (AR-HUD). As the terminal carrier for information presentation, the display client has an image generation unit and an optical combiner, capable of projecting digital graphics onto the windshield through an optical system to form a telephoto virtual image. This allows the driver to maintain focus on the road ahead while naturally overlaying virtual information into their line of sight. The display client does not alter the original road scene; by overlaying augmented graphics aligned with the real-world environment, the display area is limited to the driver's primary field of vision and must avoid visual conflicts with basic driving information such as vehicle speed and navigation.

[0059] In this embodiment, after determining the display strategy of the target object based on attribute information, the corresponding graphic elements to be displayed can be determined based on the display strategy and attribute information. Alternatively, the rendering attributes required for the graphic elements to be displayed can be determined.

[0060] Optionally, during the process of determining the graphic element to be displayed based on the display strategy and attribute information, the vehicle's graphics generation module can receive the confirmed display strategy and the attribute information of the target object. If the display strategy is contour enhancement mode, a closed polygon or continuous curve graphic that matches the real boundary geometry of the target object can be constructed based on the attribute information of the target object. The graphic can accurately fit the pixel-level contour of the target object in the panoramic image and perform three-dimensional projection based on the spatial position information to ensure that the graphic is aligned with the real object space in the HUD coordinate system. The graphic can be determined as the graphic element to be displayed corresponding to the target object at this time. If the display strategy is guidance indication mode, a linear trajectory graphic or directional extension strip graphic with arrows is generated based on the relative speed and direction of movement of the target object. The graphic does not cover the body of the target object but extends in front of the target object's movement path to indicate the future trend of the target object. The graphic can be determined as the graphic element to be displayed corresponding to the target object at this time. If the display strategy is a risk identification modality, the estimated collision time can be calculated based on the combination of relative distance and relative velocity. If the collision time is less than a preset collision time threshold, a fan-shaped area, a ring-shaped warning zone, or a dynamically expanding halo is generated. The geometric range of these graphics expands with increasing relative velocity and contracts with decreasing distance, ensuring that the spatial range of the graphics is consistent with the potential collision area. These graphics can be identified as the graphic elements to be displayed corresponding to the target object at this time.

[0061] It should be noted that the graphic elements to be displayed in the embodiments of this application can be parameterized virtual layers. The form of the graphic elements to be displayed can be determined by the type and attribute information of the display strategy, without relying on a fixed template, thus realizing semantically driven dynamic graphic construction.

[0062] Optionally, in determining the rendering attributes required for the graphic element to be displayed, multi-dimensional parameter mapping can be performed by combining the target object's attribute information, environmental context, and priority rules. These rendering attributes may include graphic color, transparency, pulsation frequency, and graphic size. The graphic colors are dynamically assigned based on the semantic category and risk level of the target object. As the risk level increases from low to high, the color gradually changes from green to red. The color value mapping is based on a preset color gradation model, which takes the combination of relative speed and relative distance as input and outputs the corresponding color value through a lookup table or linear interpolation algorithm. The transparency is adjusted according to the target distance and ambient light intensity. When the relative distance of the target is less than the near distance threshold, the transparency is reduced to enhance visual prominence. When the ambient light sensor detects nighttime or tunnel environments, the overall transparency is increased to avoid excessive darkness and interference. The pulse frequency is dynamically adjusted according to the risk priority of the target object. When the relative speed is higher than the speed threshold and the relative distance is lower than the distance threshold, it is determined to be a high-priority target, and its pulse frequency is set to be higher than the preset low-frequency threshold, such as above 2Hz, to attract attention. The graphic size is inversely scaled according to the relative distance between the target and the vehicle. When the distance is greater than the far distance threshold, the graphic size is reduced to the basic size. When the distance is less than the short distance threshold, the graphic size is enlarged proportionally to ensure that the near target has sufficient visual recognition in the field of view. The above rendering attributes can be set based on a preset rule base, and the parameters are coupled with each other to form a visual expression system that meets the needs of human factors engineering and driving safety.

[0063] In this embodiment, the method described above achieves a fundamental shift in augmented reality display from "uniform annotation" to "intelligent adaptation" through display strategy-driven graphics construction and parametric rendering attribute configuration. This ensures that the visual presentation of each target object is deeply bound to its semantic attributes, dynamic behavior, and environmental context. This process not only significantly improves the intuitiveness and accuracy of information transmission but also effectively guides the driver's attention to high-risk targets by dynamically adjusting the visual features of the graphics, reducing visual interference and cognitive load. Simultaneously, the parametric generation mechanism ensures the system's scalability and adaptability, enabling the system to meet the diverse needs of different vehicle models, lighting conditions, and driving scenarios.

[0064] Step S208: Invoke the display strategy and display the graphic elements with rendering attributes on the display client.

[0065] In the technical solution provided by step S208 of the embodiments of this application, based on the display strategy and attribute information, the graphic elements to be displayed of the target object and the rendering attribute information are determined, and the display strategy can be invoked to display the graphic elements to be displayed with rendering attributes on the display client.

[0066] In this embodiment, during the process of displaying a graphic element with rendering attributes on the display client, the vehicle's 3D registration and spatial management module can receive the generated graphic element and its corresponding rendering attributes. This 3D registration and spatial management module can calculate the precise position and orientation of the graphic element in the real-world coordinate system based on the 3D spatial position information of the target object obtained from the panoramic image, combined with the vehicle's own attitude data (including vehicle speed, steering angle, and pitch angle). This maps the graphic element to the virtual space of the HUD display coordinate system, achieving spatiotemporal synchronization between the graphic and the real target. The registration process does not rely on a single camera calibration but integrates multi-source sensor data to ensure stable anchoring of the graphic during dynamic driving, avoiding drift and jitter. The AR-HUD rendering control module receives the registered graphic data and calls the underlying graphics engine to perform layer fusion operations. This engine performs anti-aliasing, color temperature correction, and ambient light compensation on the graphic based on the graphic color, transparency, and brightness values ​​in the rendering attributes, ensuring that the graphic remains clearly distinguishable and not glaring under different lighting conditions (such as strong sunlight, dim tunnel lighting, and low nighttime illumination). The graphics size and pulsation frequency parameters are dynamically updated by the graphics engine at the frame rate to ensure smooth, stutter-free dynamic effects that meet the human eye's perception threshold, avoiding visual fatigue or dizziness. This stage of graphics rendering does not alter the original road image; it only overlays virtual information onto a transparent layer to maintain the integrity of the real scene.

[0067] Optionally, in scenarios with multiple targets, visual attention priority arbitration and layout optimization are performed to ensure the orderliness and effectiveness of displayed information. Based on the risk level, relative distance, and semantic category of each target object, a visual priority score is calculated for each graphic element to be displayed. This score is a comprehensive weighted value composed of a distance attenuation factor, a category risk coefficient, and a motion trend weight. The target object with the highest priority receives the highest display weight, and its graphic elements occupy a prominent position within the HUD display area. If multiple graphics have a risk of spatial overlap, the system automatically triggers a layout adjustment mechanism: for low-priority graphics, the system avoids them by reducing their transparency, partially shifting them, or reducing their size; for high-priority graphics, the system retains their original form and complete rendering attributes, ensuring that their pulse frequency, color contrast, and size are not weakened. Furthermore, the system presets safe display no-go zones, prohibiting any enhanced graphics from covering the HUD's basic information area, such as vehicle speed, navigation arrows, and lane keeping signs, ensuring that core driving information is always visible. The final presentation position of each graphic element to be displayed undergoes spatial conflict detection and visual occlusion assessment to ensure no redundant overlap or information occlusion within the driver's main field of view.

[0068] In this embodiment, the method described above achieves a natural, safe, and efficient integration of augmented reality information in real driving scenarios through precise 3D spatial registration, adaptive adjustment of multi-dimensional rendering attributes, and intelligent layout optimization. This process overcomes the limitations of HUDs in related technologies, which can only display fixed symbols or full images, enabling each graphic element to be displayed to dynamically adapt in terms of spatial location, visual performance, and priority ranking based on its semantic connotation, risk level, and environmental conditions. This not only significantly improves the driver's situational awareness efficiency in complex traffic environments but also effectively reduces cognitive load and the risk of misjudgment caused by information overload or visual confusion by minimizing visual interference and maximizing attention guidance.

[0069] In steps S202 to S208 of this embodiment, if an information display instruction for a target object around the vehicle is detected, the attribute information of the target object can be obtained. Based on the attribute information, a display strategy for the target object can be determined. Based on the display strategy and attribute information, the graphic element to be displayed corresponding to the target object can be determined, and the rendering attributes required for the graphic element to be displayed can be determined. Thus, the display strategy can be invoked, and the graphic element with the aforementioned rendering attributes can be displayed on the display client. In other words, in this embodiment, the display strategy and rendering attributes corresponding to the target object are dynamically determined through attribute information, thereby generating differentiated and adaptive graphic elements to be displayed, which are accurately presented on the display client. Compared to the method of statically labeling each target object using fixed rectangular boxes in related technologies, the method of this embodiment intelligently adjusts the graphic elements to be displayed and the rendering attributes based on attribute information, achieving layered and optimal display of information, effectively avoiding visual redundancy and obscuring key target objects, improving information recognition and driver attention guidance capabilities, thereby solving the technical problem of poor vehicle information display effect and achieving the technical effect of improving vehicle information display effect.

[0070] The embodiments of this application will be described in detail below with reference to the steps described above.

[0071] As an optional implementation, step S204, determining the display strategy of the target object based on the attribute information, includes: determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy.

[0072] In this embodiment, during the process of determining the display strategy for the target object based on attribute information, the candidate display strategy that matches the attribute information from at least one candidate display strategy can be determined as the display strategy. Here, a candidate display strategy can refer to a preset set of selectable visual presentation rules, where each rule corresponds to a specific expression paradigm for the target object. The role of the aforementioned candidate display strategies is to provide matching display logic options for target objects with different semantic categories and dynamic characteristics. The candidate display strategies can be called a visualization modality candidate set, including but not limited to contour enhancement modality, directional indication modality, and risk labeling modality. Each modality defines an independent set of graphic expression structures, applicable conditions, and visual performance specifications.

[0073] Optionally, this embodiment aims to select the single display strategy that best matches the target object in terms of semantic category and dynamic behavior from a preset set of candidate display strategies based on the attribute information of the target object, and use it as the basis for decision-making in subsequent augmented reality graphics generation and rendering.

[0074] Optionally, during the process of determining a candidate display strategy that matches the attribute information from among the candidate display strategies, a set of attribute information of the target object can be received. A preset set of candidate display strategies is sequentially traversed. This set includes three types of strategies: contour enhancement modality, guidance indication modality, and risk identification modality. Each type of strategy is equipped with a set of explicit and quantifiable triggering conditions. The candidate display strategy that matches the attribute information can be determined by identifying which candidate display strategy's triggering conditions the attribute information satisfies.

[0075] For example, in the contour enhancement modality among candidate display strategies, it is determined whether the semantic category of the target object belongs to the static or low-speed obstacle category, such as road shoulders, guardrails, parking lines, traffic cones, etc., and at the same time, it is verified whether its relative speed is less than a speed threshold of 2 meters per second. If the threshold is met, it means that the driving state information of the target object meets the applicable conditions of the contour enhancement modality; if the target object is a dynamically moving object and its relative speed is higher than the threshold, the strategy is excluded.

[0076] For example, regarding the directional indication modality in the candidate display strategies, the system determines whether the semantic category of the target object is a traffic participant with a clear movement trend, such as a vehicle, bicycle, or pedestrian. It further detects whether its relative speed is greater than zero and the absolute value of its relative acceleration is less than an acceleration threshold of 0.5 m / s². Simultaneously, its direction of movement deviates angularly from the vehicle's path, and this angle is greater than an angle threshold of 15 degrees. When all these conditions are met, it indicates that the target object has a path interaction intention, and the directional indication modality is selected as the appropriate strategy. For the risk labeling modality in the candidate display strategies, the system calculates the expected collision time of the target object based on the combination of relative distance and relative speed. When the expected collision time is less than a preset collision time threshold of 2.5 seconds, the system determines that there is a potential collision risk, and the risk level reaches medium-high risk. At this point, the system confirms the risk labeling modality as the appropriate strategy.

[0077] As an alternative example, if multiple candidate display strategies meet the conditions simultaneously, they are sorted according to the strategy priority rules, with the risk identification modality taking precedence over the guidance indication modality, and the guidance indication modality taking precedence over the contour enhancement modality. Finally, the strategy with the highest priority is selected as the final display strategy for the target object.

[0078] In this embodiment, the method described above achieves intelligent mapping from target attributes to visual expression through a structured, multi-condition, and priority-driven strategy matching mechanism, overcoming the rigidity of the "one-size-fits-all" display in related technologies. This process enables each target object to be matched with a display paradigm that conforms to cognitive rules based on its actual traffic behavior and environmental role, ensuring the maximum semantic accuracy and perceptual efficiency of visual information.

[0079] As an optional implementation, the candidate display strategy includes a contour display strategy, which represents the rules for displaying the geometric boundaries of the target object. The attribute information includes the category information and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the running state of the target object. The candidate display strategy that matches the attribute information is determined as the display strategy from at least one candidate display strategy, including: determining the contour display strategy as the display strategy that matches the attribute information in response to the attribute information satisfying at least one of the following conditions: the semantic category is a static category, wherein the target object of the static category is in a stationary state; the dynamic parameters indicate that the speed of the target object is less than a speed threshold, and the distance between the target object and the vehicle is less than a distance threshold.

[0080] In this embodiment, during the process of determining the display strategy that matches the attribute information from candidate display strategies, if the attribute information meets at least one of the following conditions, the contour display strategy can be determined as the display strategy that matches the attribute information: the semantic category of the target object in the attribute information is a static category; the dynamic parameters of the target object in the attribute information indicate that the speed of the target object is less than a speed threshold, and the distance between the target object and the vehicle is less than a distance threshold. The contour display strategy can refer to a visual presentation rule used on the display client to accurately represent the external boundary structure of the target object with high-fidelity geometric form. The contour display strategy does not rely on abstract symbols or generalized graphics, but rather generates a closed vector graphic that matches the real physical shape of the target object based on the pixel-level contour data extracted from the panoramic image, thereby clearly identifying the spatial range and structural contour of the target object. The contour display strategy can also be called a contour enhancement modality, whose core function is to improve the driver's spatial perception accuracy of nearby, low-speed, or stationary obstacles, especially suitable for scenarios requiring precise avoidance, such as road shoulders, bollards, parking lines, and construction barriers.

[0081] For example, the visual characteristics of the above-mentioned contour display strategy may include, but are not limited to: high-contrast edges, no fill or semi-transparent fill, no dynamic effects, emphasizing geometric accuracy rather than warning intensity, and the design principle conforms to the visual cognitive law of human factors engineering that prioritizes accurate perception over emotional arousal.

[0082] Optionally, the aforementioned category information can refer to structured data used to characterize the physical type or functional category to which the target object belongs at the semantic level. This data originates from the classification output of the deep learning model on the panoramic image content and is an abstract expression of the target object's appearance features. This category information does not involve the target object's motion state or spatial location; it can be used to describe the target object's essential attributes, such as whether the target object is a "vehicle," "pedestrian," "bicycle," "road shoulder," "traffic cone," "guardrail," or "obstacle."

[0083] Optionally, the aforementioned dynamic parameters can refer to a multi-dimensional set of numerical values ​​that quantify the motion state of the target object in space. These dynamic parameters can be derived from the joint calculation of the vehicle's sensors and vision algorithms. For example, the dynamic parameters may include, but are not limited to, relative distance, relative speed, relative acceleration, motion direction angle, and estimated collision time. The dynamic parameters do not need to reflect the appearance of the target object; they can be used to describe the changing trend of the spatiotemporal relationship between the target object and the vehicle, and to determine the target object's behavioral intentions and potential risks.

[0084] Optionally, the aforementioned static category can refer to a set of traffic element categories predefined in the category information as lacking significant autonomous movement capabilities or exhibiting extremely low movement speeds. For example, the target objects of the static category may include fixed or nearly stationary objects such as road shoulders, parking lines, traffic cones, bollards, construction barriers, and roadside guardrails. The determination criterion for the static category does not depend on instantaneous speed values, but rather on the functional attributes and physical characteristics of the target object in the traffic scenario. That is, the target object of the static category will not actively change its spatial position under normal driving conditions, and the existence of this target object has long-term stability.

[0085] Optionally, the aforementioned speed threshold can be a relative speed upper limit set to determine whether the target object is in a "low-speed" or "quasi-stationary" state. This speed threshold can be a preset engineering parameter of the system, determined based on the perceptual boundary between "avoidable static obstacles" and "dynamic hazards requiring active avoidance" for human drivers. In this embodiment, the speed threshold can be defined as 2 meters per second. When the relative speed of the target object is less than the speed threshold, it is determined that the target object's motion trend is insufficient to constitute an active collision threat, and its dynamic behavior characteristics are close to those of a static object, thus satisfying the dynamic triggering conditions of the contour display strategy. The speed threshold setting is calibrated through human factors experiments, taking into account both recognition sensitivity and false trigger suppression.

[0086] Optionally, the distance threshold can refer to the upper limit of the relative distance set to determine whether a target object is within the driver's key near-range perception range. Its function is to filter distant targets and avoid over-emphasizing irrelevant environmental elements. In this embodiment, the distance threshold can be defined as 5 meters, corresponding to the critical distance at which the driver can clearly identify and spatially locate the target object in the windshield's field of vision under normal seating posture. When the relative distance between the target object and the vehicle is less than the distance threshold, the target object is determined to be in a "near-range critical area." The geometric boundary of the target object directly affects obstacle avoidance decisions, thereby satisfying the spatial triggering conditions of the contour display strategy.

[0087] Optionally, in determining the contour display strategy to be compatible with the attribute information, after obtaining the attribute information, it is possible to verify whether the semantic category belongs to a static category. A static category is a predefined set of traffic elements that do not possess autonomous movement capabilities or whose movement trends are negligible in normal driving scenarios, such as road shoulders, parking lines, traffic cones, bollards, guardrails, and construction barriers. When the semantic category of the target object is determined to belong to the aforementioned static category, that is, the first semantic triggering condition of the contour display strategy is met, indicating that the target object has fixed or nearly fixed boundary features in space, making it necessary and reasonable to express it with high precision through geometric contours. If the semantic category is a dynamic target, such as a vehicle or pedestrian, then a secondary verification process for dynamic parameters is initiated. Dynamic parameters include the relative speed and relative distance between the target object and the vehicle, determining whether the relative speed is less than the speed threshold. When the relative speed of the target object is less than the speed threshold, it indicates that its movement trend is slow and insufficient to pose an immediate collision risk to the vehicle; the behavior characteristics of the target object are close to a static state, satisfying the dynamic triggering motion condition. Based on this, it is further determined whether the relative distance between the target object and the vehicle is less than the distance threshold. When the relative distance is less than the distance threshold, it indicates that the target object is in a critical spatial area that the driver needs to precisely avoid, and the geometric boundary information of the target object has significant operational value. When the semantic category is a static category, or when both the relative speed and the relative distance are less than the speed threshold, the contour display strategy is confirmed as the display strategy that is compatible with the attribute information of the target object.

[0088] In this embodiment, the method described above achieves precise mapping from perceived data to display strategies through a structured and quantifiable multi-condition matching mechanism, significantly improving the semantic accuracy and scene adaptability of augmented reality information presentation. This process overcomes the rigidity of "uniform selection" or "full annotation" in related technologies, ensuring that the contour display strategy is activated only when the target possesses the triple characteristics of "clear boundaries, slow movement, and close spatial proximity," thus concentrating visual resources on scenes that truly require high-precision expression and effectively reducing information redundancy and visual interference.

[0089] As an optional implementation, the candidate display strategy includes a guiding display strategy, which represents the rules for displaying the movement trend of the target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the running state of the target object. Determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy includes: in response to the attribute information satisfying at least one of the following conditions, determining the guiding display strategy as the display strategy that matches the attribute information: the semantic category is a dynamic category, wherein the target object of the dynamic category is in a moving state; the dynamic parameters represent the lane where the target object is located, and there is a correlation between the dynamic parameters and the lane where the vehicle is located.

[0090] In this embodiment, during the process of determining the display strategy that matches the attribute information from candidate display strategies, if the attribute information meets at least one of the following conditions, the guidance display strategy can be determined as the display strategy that matches the attribute information: the semantic type of the target object in the attribute information is a dynamic category; the dynamic parameters in the attribute information can be used to represent the lane where the target object is located, and there is a correlation between the target object and the lane where the vehicle is located. The guidance display strategy can refer to visual presentation rules used on the display client to accurately represent the future movement direction, path intention, or behavioral tendency of the target object using directional and trend-based graphic elements. The aforementioned guidance display strategy does not need to focus on the geometric boundaries or static contours of the target object, but rather uses non-filled graphic structures such as linear guide lines, arrows, trajectory prediction strips, dynamic streamlines, or path extension symbols to intuitively convey the movement evolution trend of the target in three-dimensional space. Its core function is to assist drivers in anticipating the potential behavioral intentions of traffic participants, establishing spatial collaborative cognition, and thus achieving proactive safety guidance. The wayfinding display strategy, also known as wayfinding indicator modality, has the following visual characteristics: lightweight, semi-transparent, low-saturation linear graphics with fading effect or dynamic extension characteristics. It does not interfere with basic road information. The design follows the cognitive intervention principle of "trend prediction is better than passive warning" in human factors engineering. It is suitable for high-risk interaction scenarios such as lane changing of adjacent lanes, pedestrian crossing prediction, and intersection merging intention recognition.

[0091] Optionally, the aforementioned dynamic category can refer to a set of traffic participant types predefined within the semantic category of the target object, which possess autonomous movement capabilities, predictable behavior, and may have spatial interaction relationships with the vehicle. This dynamic category does not include fixed or nearly stationary objects, but specifically refers to entities that move continuously or intermittently in dynamic traffic flow and have degrees of freedom in directional selection. The target objects of this dynamic category can include motor vehicles, non-motor vehicles (e.g., bicycles, electric scooters), pedestrians, motorcycles, and animals.

[0092] Optionally, the correlation can refer to the functional topological connection between the lane where the target object is located and the lane where the vehicle is currently traveling, in terms of spatial location and trajectory. Essentially, it serves as the logical basis for determining whether the target and the vehicle have potential path intersections, lateral approaches, or behavioral interference within a future time window. In this embodiment, the aforementioned correlation can refer to lane change correlation, that is, the lane where the target object is located and the lane where the vehicle is located are adjacent, and the target object's movement trend is towards the vehicle's lane, or the vehicle itself has planned a lane change, resulting in a spatial risk of intersection between the two. The determination of the aforementioned correlation relies on lane position information, lateral offset, relative velocity vector, and vehicle steering signal status in the dynamic parameters, including: the target object is located in the adjacent lane to the left or right of the vehicle, and the lateral distance is less than the lateral fusion threshold, while its relative lateral velocity component points towards the vehicle's lane; or the vehicle has activated its turn signal, and the system predicts that the lane change path will overlap with the target object's trajectory.

[0093] Optionally, this embodiment aims to determine whether a target object meets the triggering conditions of a guided display strategy based on its semantic category and dynamic parameters, thereby accurately selecting the strategy from the candidate display strategy set as the unique suitable display strategy for the target object. This process is a rule-based multi-condition logical judgment flow, the core of which lies in constructing a structured mapping relationship between "attribute information and strategy conditions". By verifying layer by layer whether the target meets the semantic constraints and spatial relationship constraints defined by the guided display strategy, intelligent decision-making from environmental perception to visual expression is achieved.

[0094] Optionally, in determining the guidance display strategy to be adapted to the attribute information, the system receives the attribute information of the target object and verifies whether the semantic category in the attribute information belongs to a dynamic category. A dynamic category is a predefined set of traffic participant types that possess autonomous movement capabilities in normal driving scenarios, exhibit predictable behavior, and may have spatial interaction relationships with the vehicle. Typical examples include motor vehicles, non-motor vehicles (such as bicycles and electric scooters), pedestrians, and motorcycles. Based on the semantic classification output of the panoramic image from a deep learning model, the system determines whether the target object belongs to the above categories. When the semantic category is confirmed as a dynamic category, it indicates that the target possesses a non-zero probability of movement and behavioral intent, satisfying the primary semantic prerequisite of the guidance display strategy.

[0095] At this point, the system enters a further verification process for dynamic parameters. These dynamic parameters include spatial relationship information between the lane where the target object is located and the lane where the vehicle is located. This information is jointly calculated and output by the lane recognition algorithm and the vehicle positioning module to determine whether there is a potential risk of path intersection or lateral interaction between the target and the vehicle. The system then determines whether this spatial relationship constitutes a correlation. A correlation refers to a foreseeable lateral approach or trajectory intersection possibility between the lane where the target object is located and the vehicle's current lane. Specifically, this manifests as follows: the target object is located in the adjacent lane to the left or right of the vehicle, and its lateral offset is less than the lateral fusion threshold (1.5 meters), which defines the "adjacent lane" range; or the target object is located ahead in the same lane, but its longitudinal distance is less than the following distance threshold (30 meters), and the vehicle has activated its turn signal, indicating a lane change intention. The system predicts that the target will enter the vehicle's future path.

[0096] If any of the above conditions are met, the association triggering condition for the guidance display strategy is satisfied. The system does not rely on a single parameter here, but rather uses a comprehensive judgment based on a combination of lane number, lateral offset, longitudinal distance, and the vehicle's steering state to ensure that the association determination is environmentally adaptable and scenario-realistic. When the semantic category is dynamic, or the dynamic parameters indicate a relationship between the lane where the target object is located and the lane where the vehicle is located, the system determines that the guidance display strategy is adapted to the attribute information of the target object, and the two are in an "OR" logical relationship. This ensures that the system can effectively activate the guidance indication modality in various typical interaction scenarios, avoiding strategy omissions due to the failure of a single condition.

[0097] In this embodiment, the method described above achieves precise mapping from perceptual data to visual expression through a structured and quantifiable multi-condition matching mechanism, significantly improving the semantic accuracy and scene adaptability of augmented reality information presentation. This process overcomes the rigidity of "full annotation" or "indiscriminate prompting" in traditional systems, ensuring that the guidance display strategy is activated only when the target possesses both "clear behavioral intent" and "potential spatial interaction," thus concentrating visual resources on high-risk interactive scenarios that truly require trend guidance and effectively reducing information redundancy and visual interference.

[0098] As an optional implementation, the candidate display strategy includes a risk display strategy, which represents the rules for displaying the area where a collision risk occurs between a vehicle and a target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance characteristics of the target object, and the dynamic parameters represent the operating state of the target object. The candidate display strategy that matches the attribute information from at least one candidate display strategy is determined as the display strategy, including: in response to the attribute information satisfying at least one of the following conditions, the risk display strategy is determined as the display strategy that matches the attribute information: the dynamic parameters indicate that the speed of the target object is greater than a speed threshold; the distance between the target object and the vehicle is less than a distance threshold.

[0099] In this embodiment, during the process of determining the display strategy that matches the attribute information from candidate display strategies, if the attribute information meets at least one of the following conditions, the risk display strategy can be determined as the display strategy that matches the attribute information: the dynamic parameters of the target object in the attribute information indicate that the speed of the target object is greater than a speed threshold; the distance between the target object and the vehicle in the attribute information is less than a distance threshold. The risk display strategy can refer to a visual presentation rule used on an augmented reality display client to accurately represent the potential collision hazard area between the vehicle and the target object using spatially regionalized, highly alert graphic elements. The aforementioned risk display strategy does not rely on the appearance or semantic category of the target object, but rather dynamically generates a three-dimensional risk area with clear geometric boundaries and visual intensity based on its dynamic motion state and spatial relative position. This area is used to intuitively indicate a high probability of collision within the region. Its core function is to guide the driver to identify and respond to the most threatening interactive situation in a very short time through visual priority enhancement, achieving proactive warning and attention orientation for sudden collision hazards.

[0100] Risk display strategies, also known as risk identification modalities, include the following visual characteristics: a fan-shaped, wedge-shaped, or elliptical filled area extending outward from the target object, the shape and size of which are calculated and determined by the target's relative velocity vector, relative distance, and the vehicle's trajectory; the color gradually changes from yellow to red according to the risk level, the transparency decreases as the risk increases, the brightness adaptively increases according to the ambient light intensity, and the graphic edges have a slight pulsating or flashing dynamic effect to conform to the visual cognition principle in human factors engineering that "high-priority warnings must have the ability to be instinctively captured."

[0101] Optionally, this embodiment aims to determine whether the target object meets the triggering conditions of the risk display strategy based on its dynamic parameters, thereby accurately selecting the strategy from the candidate display strategy set as the unique and suitable display strategy for the target object. This process is a rule-based, dual-condition logic judgment flow. The core lies in constructing a hierarchical mapping relationship between "dynamic parameters and strategy thresholds." By verifying layer by layer whether the motion state and spatial distance of the target object meet the physical risk criteria defined by the risk display strategy, intelligent decision-making from environmental perception to highly alert visual expression is achieved.

[0102] Optionally, during the process of determining the risk display strategy to be adapted to the attribute information, it can be verified whether the target object's speed is greater than a speed threshold. The speed threshold is a preset physical risk threshold value of the system, the value of which is determined based on the traffic accident dynamics model and the average human reaction time, and is used to distinguish between low-risk approach behavior and high-risk approach behavior. When the target object's speed is greater than the speed threshold, it indicates that the target has a kinetic energy level sufficient to cause a collision in a short time, meeting the primary dynamic triggering condition of the risk display strategy. At this time, a secondary verification process of relative distance is entered. The relative distance is the Euclidean distance between the vehicle and the target object at the nearest point in three-dimensional space, which is accurately calculated by the panoramic imaging system in combination with vehicle positioning information. It is then determined whether the relative distance is less than a distance threshold. The distance threshold is a warning boundary set by the system based on braking safety margin, effective field of view identification range, and collision time prediction model, with a value of 15 meters. When the relative distance is less than the distance threshold, it indicates that the target object has entered the emergency response space range that the vehicle can intervene in, meeting the spatial triggering condition of the risk display strategy.

[0103] The two conditions above constitute an "OR" logical relationship, meaning that if either the target object's speed exceeds a speed threshold or the relative distance between the target object and the vehicle is less than a distance threshold, the risk display strategy is determined to be suitable for the target object's attribute information. This logical design does not rely on the semantic category of the target object; regardless of whether the target is a vehicle, pedestrian, bicycle, or static obstacle, physical risk parameters are used as the sole criterion to ensure unbiased and complete responses in high-risk situations. The system does not perform semantic filtering during this process; it only performs threshold comparisons at the purely physical level, ensuring the objectivity and universality of risk identification.

[0104] In this embodiment, the method described above achieves a precise mapping from motion state and spatial location to visual warning modes through a structured and quantitative dual-threshold determination mechanism, significantly improving the accuracy and response efficiency of the augmented reality system in identifying collision risks. This process overcomes the one-sidedness of related technologies that only rely on semantic categories or static contours for warnings, ensuring that the risk display strategy is only activated when the target possesses either a "high-speed approach" or "close-range intrusion" physical risk characteristic. This ensures that visual resources are concentrated on truly dangerous scenarios that require high-priority warnings, effectively reducing visual noise and information overload. Simultaneously, the coordinated design of speed and distance thresholds enhances the system's environmental adaptability. Even under complex lighting conditions such as nighttime, rain, fog, or strong light, if the perception accuracy slightly decreases, the system can still trigger a warning through any reliable parameter, ensuring the robustness of the safety function.

[0105] As an optional implementation, step S206, determining the rendering attributes that the graphic element to be displayed needs to satisfy, includes: generating initial rendering attributes of the graphic element based on the display strategy; and adjusting the initial rendering attributes using attribute information to obtain the rendering attributes.

[0106] In this embodiment, during the process of determining the rendering attributes that the graphic element to be displayed needs to satisfy, initial rendering attributes of the graphic element can be generated based on the display strategy. The initial rendering attributes can then be adjusted using the attribute information to obtain the final rendering attributes.

[0107] Optionally, initial rendering attributes can refer to a set of basic visual expression parameters generated for the corresponding target object in an augmented reality display system, based on the selected display strategy, and not yet finely adjusted in conjunction with real-time dynamic parameters. This set of attributes defines the initial visual form of the graphic element presented on the head-up display. Its function is to provide a structured and scalable rendering benchmark for subsequent adaptive optimization based on semantic categories, dynamic parameters, and environmental context, serving as an intermediate bridge connecting strategy decisions and the final visual output. Initial rendering attributes can also be called rendering templates. The content of the aforementioned initial rendering attributes does not depend on the real-time state of the target object, but is determined by the inherent visual semantic specifications of the display strategy itself, ensuring that the graphic expression of the same type of display strategy has consistency, recognizability, and unity of design intent in different scenarios.

[0108] For example, the initial rendering attributes mentioned above may include, but are not limited to, visual parameters such as graphic shape, base color, initial transparency, base brightness, static or dynamic effect mode, and graphic size range. For instance, when the display strategy is contour enhancement mode, the initial rendering attributes are defined as follows: the graphic shape is a closed continuous wireframe, the base color is blue, the initial transparency is 0.5, there is no dynamic effect, and the graphic size is scaled according to the original outline of the target; when the display strategy is directional indicator mode, the initial rendering attributes are defined as follows: the graphic shape is a one-way arrow or trajectory extension line, the base color is cyan, the initial transparency is 0.6, it has a slight linear flow effect, and the graphic length is fixed according to the preset path extension range; when the display strategy is risk indicator mode, the initial rendering attributes are defined as follows: the graphic shape is a fan-shaped risk area, the base color is yellow, the initial transparency is 0.4, it has a static pulsation frequency of 0.5Hz, and the graphic angle and radius are initialized according to the preset standard values ​​of typical collision scenarios.

[0109] Optionally, generating initial rendering attributes for graphical elements based on display strategies aims to establish a unified, standardized, and reusable visual expression baseline for each candidate display strategy. Display strategies are predefined abstract models used to characterize the rules for presenting specific types of information, including contour enhancement modalities, directional indication modalities, and risk signage modalities. After matching strategies based on the semantic category and dynamic parameters of the target object, the corresponding display strategy is determined. At this point, the initial rendering attribute template bound to that strategy is called from the strategy library. This template is jointly developed by human factors engineering specifications and driving safety standards, does not rely on any real-time input data, and only reflects the inherent visual semantic intent of the strategy.

[0110] For example, when the display strategy is outline enhancement mode, the initial rendering attributes are initialized as follows: the graphic shape is a closed wireframe, the base color is blue, the initial transparency is 0.5, there are no dynamic effects, and the graphic size is scaled 1:1 according to the original outline ratio of the target. When the display strategy is directional indicator mode, the initial rendering attributes are initialized as follows: the graphic shape is a one-way arrow or a trajectory extension line, the base color is cyan, the initial transparency is 0.6, it has a low-frequency linear flow effect, and the graphic length is fixed at 1.2 meters according to the preset path extension range. When the display strategy is risk sign mode, the initial rendering attributes are initialized as follows: the graphic shape is a fan-shaped area, the base color is yellow, the initial transparency is 0.4, the pulsation frequency is 0.5Hz, the fan angle is 45 degrees, and the radius is 10 meters. The above parameters are all preset values ​​of the strategy and do not change with the target state. Their purpose is to ensure that no matter what kind of entity the target is or what environment it is in, as long as the same display strategy is matched, its visual form has a recognizable consistency, avoiding semantic confusion or increased cognitive load caused by random generation.

[0111] Optionally, adjusting the initial rendering attributes using attribute information to obtain the final rendering attributes is the second stage of this process. The core of this stage lies in dynamically optimizing visual parameters through real-time perception data, adapting the initial template to the complexity and dynamism of real-world driving scenarios. Attribute information includes the semantic category of the target object, dynamic parameters (such as relative speed, relative distance, and lane position), ambient light intensity, vehicle status (such as steering angle and gear position), and spatial relationships with other targets. Based on these parameters, the system sequentially performs independent but coordinated calibrations on the color information, transparency, pulsation frequency, and element brightness in the initial rendering attributes. First, the system calculates the collision risk level of the target object based on relative speed and relative distance, and performs a non-linear mapping of the color information according to the risk level: when the risk level is low, the color is adjusted from yellow to green; when the risk level is high, the color is adjusted from yellow to red. This adjustment process is calibrated according to international traffic warning color standards (such as ISO 3864) to ensure that the color change corresponds positively to the hazard level.

[0112] Optionally, the system calculates the interference degree of the target object on the driver's main line of sight. The interference degree is jointly determined by the target's projected area in the HUD display coordinate system, its spatial overlap with core information (such as vehicle speed and navigation arrows), and the lateral offset rate of its movement trajectory. When the interference degree exceeds a preset threshold, the system reduces transparency to decrease visual penetration and simultaneously shrinks the graphic size to compress space occupation, achieving a negative linkage between interference degree and transparency and size information. Furthermore, the system calculates the warning priority based on risk level, target category risk weight (e.g., pedestrian > bicycle > motor vehicle), and whether the location is in a blind spot. When the warning priority is higher than 0.7, the pulse frequency is increased from 0.5Hz to 2Hz to trigger the instinctive alert response of human vision, ensuring that high-priority targets are perceived first in multi-target competition scenarios.

[0113] Optionally, ambient light information collected by an ambient light sensor is read. When the ambient light is below 1000 lux, the element brightness is increased to 80% of its maximum value; when the ambient light is above 8000 lux, the element brightness is increased to 100%, and color saturation is simultaneously enhanced to ensure that the graphics remain recognizable in nighttime or bright light environments. All of the above adjustments are based on a predefined mapping function, and all parameter adjustments are made on the basis of the initial strategy template, without changing its semantic essence; only context-adaptive effects are achieved in terms of visual intensity and presentation.

[0114] In the embodiments of this application, the above method ensures the uniformity and cognitive comprehensibility of visual language through "strategy definition of initial form", avoiding graphic chaos caused by the lack of standards in traditional systems; and achieves deep coupling between visual expression and real driving situation through "attribute information driven dynamic adjustment", so that the graphics can maintain optimal readability and minimum interference under different lighting, different distances, different risk levels and different interference environments.

[0115] As an optional implementation, the rendering attributes include at least one of the following: color information of the graphic element, transparency of the graphic element, brightness of the graphic element, pulsation frequency of the graphic element, and size information of the graphic element. Using this attribute information, the initial rendering attributes are adjusted to obtain rendering attributes, including at least one of the following: based on the attribute information, determining the risk level of a collision between the vehicle and the target object, and adjusting the color information according to the risk level, wherein the risk level matches the color information; based on the attribute information, determining the interference level of the target object on the vehicle, and adjusting the transparency and size information according to the interference level, wherein the interference level is negatively correlated with the transparency and the size information; based on the attribute information, determining the warning priority of the target object, and adjusting the pulsation frequency according to the warning priority, wherein the warning priority is positively correlated with the pulsation frequency; based on the attribute information, determining the brightness information of the vehicle's environment, and adjusting the element brightness according to the brightness information, wherein the brightness information is positively correlated with the element brightness.

[0116] In this embodiment, during the adjustment of the initial rendering attributes, the risk level of a collision between the vehicle and the target object can be determined based on the attribute information. The color information in the initial rendering attributes can then be adjusted according to this risk level. Alternatively, the interference level of the target object on the vehicle can be determined based on the attribute information, and the transparency and size information in the initial rendering attributes can be adjusted accordingly. Furthermore, the warning priority of the target object can be determined based on the attribute information, and the pulse frequency in the initial rendering attributes can be adjusted accordingly. Finally, the brightness information of the vehicle's environment can be determined based on the attribute information, and the element brightness in the initial rendering attributes can be adjusted accordingly.

[0117] Optionally, based on attribute information, the risk level of a collision between the vehicle and a target object is determined, and the color information is adjusted according to the risk level. Matching the risk level with the color information is the first layer of visual encoding of the potential threat from the target object. The system first calculates the comprehensive risk level of a collision between the target object and the vehicle based on the target object's dynamic parameters, including relative speed and relative distance, combined with a preset risk assessment model. This level is divided into three levels: low, medium, and high, each corresponding to a specific visual warning intensity. When the relative speed is less than a speed threshold and the relative distance is greater than a distance threshold, the risk level is determined to be low, and the corresponding color is green, indicating general concern within a safe distance. When the relative speed is greater than the speed threshold but the relative distance is still greater than the safety threshold, or the relative speed is within the threshold range and the distance is close to the warning boundary, the system determines the risk level to be medium, and the corresponding color is yellow, indicating a potential threat is forming. When the relative speed is significantly higher than the speed threshold and the relative distance is less than the distance threshold, the system determines the risk level to be high, and the corresponding color is red, indicating an emergency collision risk has been established. The adjustment of color information is not a simple mapping, but a non-linear calibration based on internationally accepted traffic warning color standards and the perceptual characteristics of human eye sensitivity to color. This ensures that red still has high contrast in strong light environments, green does not appear dim in nighttime environments, and yellow has moderate wake-up ability, thereby achieving intuitive and unambiguous visual communication of risk levels.

[0118] Optionally, based on attribute information, the interference level of the target object on the vehicle is determined, and the transparency and size information are adjusted according to the interference level. The interference level is negatively correlated with transparency and size information, which is a key mechanism for the system to balance information display clarity and visual immersion. Interference level is defined as the comprehensive degree to which the target object visually obstructs or distracts the driver's main field of vision or basic HUD information (such as vehicle speed and navigation arrows) in terms of spatial location, movement trajectory, or visual coverage. Its calculation is based on the target's spatial coordinates in the HUD display coordinate system, the percentage of the graphic's coverage area, its proximity to the core information area, and the lateral offset rate of the target's movement. When the target object is located in the center of the driver's main field of vision and the graphic's projected area exceeds a preset area threshold, or its movement trajectory overlaps with the navigation guide line more than an overlap threshold, the system determines the interference level to be high. When the target object is located in the edge area of ​​the HUD, the graphic size is small, and there is no spatial overlap with key information, the system determines the interference level to be low. Under high interference conditions, the system reduces transparency to weaken the visual penetration of the graphic and prevent it from excessively interfering with the road background. The transparency adjustment range is reduced from 0.8 to 0.3. At the same time, the graphic size is reduced to compress its space occupation in the field of view. The size adjustment is based on a preset scaling factor and decreases linearly according to the interference level to ensure that the target is still identifiable but not dominant. Under low interference conditions, the system increases transparency to above 0.6 and increases the graphic size to enhance its presence, achieving an adaptive balance mechanism of "the stronger the interference, the weaker the display; the weaker the interference, the stronger the display."

[0119] Optionally, based on attribute information, the warning priority of the target object is determined, and the pulse frequency is adjusted according to the warning priority. The warning priority and pulse frequency are positively correlated, making it the core dynamic control method for the system to guide attention. The warning priority is calculated by the system based on multiple factors, including risk level, target category risk coefficient (e.g., pedestrians are higher than bicycles, bicycles are higher than vehicles), relative approach speed, and whether the spatial location is in a turning blind spot. Its value ranges from 0 to 1, measuring the urgency of the target for the driver's decision. When the warning priority is below 0.3, the system does not enable the pulse effect and maintains a static display. When the warning priority is between 0.3 and 0.7, the system sets the pulse frequency to 0.5Hz, using slight periodic changes in brightness to attract secondary attention. When the warning priority is above 0.7, the system increases the pulse frequency to 2Hz, creating a high-frequency flashing effect that triggers the instinctive alertness of the human visual system, ensuring that in scenarios with multiple targets and complex information, the highest priority target can be identified in the fastest and most prominent way. The adjustment of the pulse frequency does not depend on subjective settings, but is based on a nonlinear function relationship established by the driver's cognitive load model and attention capture experimental data, ensuring that the flashing frequency is below the visual fatigue threshold while meeting the physiological needs of alertness arousal.

[0120] Optionally, based on attribute information, the brightness information of the vehicle's environment is determined, and the element brightness is adjusted accordingly. The brightness information and element brightness are positively correlated, serving as an environmental adaptive mechanism to ensure all-weather display readability. Brightness information is collected in real-time by an ambient light sensor, ranging from 0 to 10000 lux, covering a complete scene from low illumination in tunnels to strong midday light. Based on a preset brightness response curve, the system divides ambient light intensity into three intervals: when the ambient light intensity is below 1000 lux, it is considered a low-light environment, and element brightness is increased to 80% of its maximum value; when the ambient light intensity is between 1000 and 8000 lux, it is considered a normal lighting environment, and element brightness is increased linearly to 60%; when the ambient light intensity is above 8000 lux, it is considered a strong-light environment, and element brightness is increased to 100% of its maximum value, along with color saturation enhancement, ensuring that red warnings remain clearly visible even under direct sunlight. This adjustment process does not rely on fixed brightness values, but rather on the non-linear characteristics of human eye's contrast perception under different lighting conditions. This avoids glare caused by excessive brightness at night and information overload caused by insufficient brightness during the day, achieving true "environmentally adaptive brightness matching".

[0121] In this embodiment, the method employs four independent yet coordinated parametric adjustment mechanisms to achieve refined and contextualized control over the rendering attributes of augmented reality graphics elements, significantly improving information transmission efficiency and visual safety in complex dynamic environments. This process overcomes the shortcomings of traditional systems' "static rendering, unified across all scenes," such as information ambiguity, interference redundancy, and slow response. It ensures that each visual parameter responds to the physical and cognitive needs of real-world driving scenarios, guaranteeing that graphics are clearly discernible under any lighting conditions, do not obscure key information in any spatial location, possess appropriate warning intensity at any risk level, and provide precise guidance in any attention-chasing environment.

[0122] As an optional implementation, the method further includes: responding to the fact that the number of target objects is one, determining the position coordinates of the graphic element to be displayed in the display coordinate system of the display client based on the position information of the target object in the environment where the vehicle is located; determining the layout strategy of the graphic element to be displayed on the display client based on the position coordinates, wherein the layout strategy is used to represent the rules for laying out the graphic element to be displayed on the display client; responding to the fact that the number of target objects is multiple, determining the display priority corresponding to each of the multiple target objects; determining the layout strategy based on the position coordinates and the display priority; step S208, invoking the display strategy to display the graphic element to be displayed with rendering attributes on the display client, including: invoking the layout strategy to lay out the graphic element to be displayed with rendering attributes on the display client; invoking the display strategy to display the laid-out graphic element to be displayed on the display client.

[0123] In this embodiment, if there is only one target object, the position coordinates of the graphic element to be displayed in the display coordinate system of the display client can be determined based on the position information of the target object in the vehicle's environment. Based on these position coordinates, a layout strategy for which element to be displayed on the display client should be determined. If there are multiple target objects, the display priorities corresponding to each target object can be determined. A layout strategy can be determined based on the position coordinates and display priorities. During the process of calling the display strategy to display the graphic element with rendering attributes on the display client, the layout strategy can be called to lay out the graphic element with rendering attributes on the display client. The display strategy can be called to display the laid-out graphic element on the display client. The position information refers to the geometric position description of the target object in the real three-dimensional spatial environment of the vehicle, used to characterize the spatial distribution state of the target object relative to the vehicle's own coordinate system. The position information can be calculated by the vehicle's perception system through the fusion of surround-view camera group, ultrasonic sensors, and vehicle dynamics data. The position information may include the lateral offset, longitudinal distance, and vertical height of the target object in the vehicle coordinate system.

[0124] Optionally, position coordinates can refer to the specific values ​​mapped from the target object's position information after 3D spatial registration processing to the 2D projection coordinate system used by the head-up display (HUD) client. These coordinates represent the visual projection position of the target object on the virtual image plane in front of the driver. The HUD client uses a display coordinate system independent of the vehicle coordinate system. Its origin is typically located at the intersection of the driver's line of sight and the virtual image of the windshield. The horizontal axis represents the left-right direction (X-axis), and the vertical axis represents the up-down direction (Y-axis). The coordinate unit is pixels or normalized units. Position coordinates are the final display parameters obtained after perspective projection transformation, optical combiner distortion correction, and parallax compensation. Their function is to precisely "anchor" the target position in the real world onto the virtual display canvas of the HUD, ensuring that graphic elements maintain spatial consistency with real objects in the driver's field of vision. For example, a vehicle located 15 meters directly in front of the vehicle might have position coordinates mapped to (0, 200) pixels, while a bicycle located 3 meters to the right with a lateral offset angle of 15° might have position coordinates of (180, 250) pixels. Position coordinates are the sole basis for visually positioning graphic elements on the HUD interface; they can also be called display spatial position or virtual projection coordinates.

[0125] Optionally, the aforementioned layout strategy can refer to a set of rules governing the reasonable distribution of multiple augmented reality graphic elements on the HUD display client. This set determines the spatial arrangement of graphics within a limited display area, the overlap handling mechanism, and the visual priority ranking logic, thereby achieving comprehensive optimization of information clarity, non-interference, and attention guidance. The layout strategy is based on the position coordinates and display priority of the target objects, and its core function is to solve the problems of occlusion, overlap, and visual crowding that may occur with multiple target graphics on a two-dimensional display plane.

[0126] Optionally, in response to the presence of only one target object, the system determines the position coordinates of the graphic element to be displayed in the display coordinate system of the display client based on the target object's position information in the vehicle's environment. Based on these position coordinates, the system determines the layout strategy for the graphic element on the display client. This is the simplest yet most accurate layout processing mechanism executed by the system in a single target scenario. When the system confirms that there is only one target object to be displayed, it first acquires the three-dimensional position information of the target object in the vehicle coordinate system. This information includes its lateral offset, longitudinal distance, and vertical height relative to the vehicle, calculated by fusing data from the surround-view camera and the vehicle's dynamic sensors.

[0127] Optionally, a 3D spatial registration process is executed, converting the location information into 2D position coordinates in the HUD display coordinate system through a perspective projection model, optical combiner distortion correction, and driver eye-point compensation algorithm. These position coordinates represent a unique location point within the display plane, with the horizontal axis corresponding to the left-right direction and the vertical axis corresponding to the up-down direction, in standardized pixel values. For example, a target located 18 meters directly in front of the vehicle with a lateral offset of 0 meters might have position coordinates of (0, 180) pixels. After obtaining these position coordinates, the system employs a "direct alignment layout strategy": this strategy is a pre-defined single rule that avoids any positional offset, scaling, transparency adjustments, or competition for space with other elements. It directly aligns the center point of the graphic element to be displayed with these position coordinates, ensuring precise spatial alignment between the graphic and the real target in the driver's field of vision. This strategy does not introduce any arbitration or trade-off logic because there is no competition for display resources in a single-target scenario; its sole objective is to achieve the highest accuracy of visual anchoring.

[0128] Optionally, in response to the presence of multiple target objects, a display priority is determined for each target object. Based on location coordinates and display priorities, a layout strategy is determined, which is the core mechanism for resource optimization and attention management in multi-target competition scenarios. When two or more target objects to be displayed are identified, the display priority is calculated independently for each target object. The display priority is a normalized value ranging from 0 to 1, calculated by weighting factors such as risk level, target category risk coefficient, relative distance, relative speed, spatial proximity to key driving information (such as vehicle speed and navigation arrows), and whether it is in a turning blind spot. For example, a pedestrian located 2 meters to the right with a relative speed of 3 km / h might have a priority of 0.85; while a stationary shoulder located 4 meters to the left might have a priority of 0.3.

[0129] Based on the position coordinates and priority ranking of all targets, a layout arbitration process is initiated. High-priority targets (priority above 0.7) are forcibly anchored in the center of the driver's main field of vision, i.e., the area within ±100 pixels on the Y-axis of the HUD display coordinate system, to ensure maximum visual attention. For medium-priority targets (0.4 to 0.7), the system performs lateral offset compensation based on their original position coordinates, translating them along the X-axis to the edge area to avoid spatial overlap with high-priority targets. For low-priority targets (below 0.4), the system not only performs lateral offset but also appropriately reduces the graphic size and transparency to minimize visual interference with the main field of vision while still ensuring their recognizability. If multiple targets highly overlap in space, the system will activate a local displacement compensation algorithm, making slight offsets to the graphics without violating the principle of real spatial mapping, such as offsetting two adjacent targets by ±15 pixels along the X-axis to achieve visual separation. This layout strategy can also be called a priority-driven spatial allocation rule, which essentially achieves optimal allocation of limited display resources by quantifying priority and spatial constraints.

[0130] Optionally, the calculated layout strategy is passed as a control command to the HUD graphics rendering engine. Based on the position coordinates, size, transparency, and offset of each graphic element determined in the layout strategy, the engine performs spatial repositioning and layer overlay processing on the generated graphic elements with rendering attributes (such as color, pulsation frequency, and brightness), ensuring that all graphics meet the distribution logic set by the layout rules on the display canvas. After layout, the system calls the corresponding display strategy, which serves as the standard for graphic semantic expression, activating the graphic's shape template and dynamic effects. For example, the risk sign modality triggers a red fan-shaped area and a 2Hz pulsation, while the contour enhancement modality activates a blue highlighted outline. The rendering engine merges the laid-out graphic elements with the underlying HUD basic information (vehicle speed, navigation arrows, lane lines) for rendering, performing anti-aliasing, color correction, and brightness equalization processing. Finally, it projects the image onto the windshield through an optical combiner, forming a far-focus virtual image seamlessly integrated with the real road environment. The entire process is end-to-end automated, from spatial positioning to final imaging, without any manual intervention.

[0131] In this embodiment, the method significantly improves the usability and safety of augmented reality information in the in-vehicle environment by differentiating between single-target and multi-target scenarios and implementing two differentiated layout mechanisms: precise alignment and priority arbitration. In single-target scenarios, it ensures absolute accuracy of visual anchoring, avoiding positioning deviations caused by redundant processing. In multi-target scenarios, it achieves orderly organization of information and efficient guidance of attention through quantified priorities and spatial constraints, effectively addressing the core defects of related HUD technologies, such as "information stacking, blurred focus, and severe interference." This method not only ensures strict consistency between the spatial position of graphic elements and real-world targets but also achieves dynamic optimization of visual resources through intelligent layout strategies, enabling drivers to quickly identify the highest-risk targets in complex traffic environments while reducing visual interference from irrelevant information.

[0132] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0133] Currently, with the development of automotive intelligence, panoramic imaging systems and head-up displays (HUDs) have become common features in high-end models. Panoramic imaging systems generate a bird's-eye view of the vehicle's surroundings using surround-view cameras, helping drivers perceive blind spots. HUDs project key driving information onto the windshield, allowing drivers to view information without looking down, thus improving driving safety. However, effectively presenting environmental perception information remains a critical issue. Existing technologies typically use a single, fixed visualization method (such as uniformly colored boxes) to label all identified targets (e.g., vehicles, pedestrians, road shoulders), which has significant drawbacks.

[0134] The closest related technology to this application is as follows: a vehicle system capable of controlling the switching on and off of a panoramic imaging system via voice and displaying sensor-triggered abstract warning symbols fixedly on the HUD. However, the aforementioned related technology has failed to achieve a technological breakthrough in extracting rich visual elements from panoramic images in real time and intelligently via voice commands, and accurately mapping them to the corresponding display position on the HUD in a specific manner.

[0135] The aforementioned technologies have the following drawbacks and shortcomings: Manually switching or adjusting the HUD display content (such as switching to a specific viewpoint) under complex road conditions is cumbersome for drivers, increasing cognitive load and operational risk. Regardless of target type, distance, speed, or degree of danger, all targets are displayed using the same graphic (such as a rectangle), failing to intuitively convey the target's specific category, dynamic trend, or potential risk level, requiring additional cognitive interpretation from the driver. Display styles (such as color, transparency, and size) do not dynamically adjust based on the current driving scenario (such as high-speed cruising or low-speed parking), vehicle status (such as steering or acceleration), or ambient lighting (day or night), potentially causing unclear or interfering displays. Multiple target AR markers on the HUD may overlap, obscuring crucial driving information (such as navigation arrows), or failing to effectively guide the driver's attention to the highest-risk target. Therefore, an intelligent information retrieval method and an augmented reality display solution that can understand target meaning, adapt to driving situations, and intelligently manage visual presentation are needed.

[0136] In this embodiment, based on the aforementioned related technologies, the following problems are mainly addressed: A closed-loop intelligent interaction mechanism of "intent-extraction-presentation" is created. Natural language speech is used as the intent input, driving computer vision algorithms to extract and recognize information on demand and accurately from the panoramic image data stream. Finally, the information is presented in a contextualized and visually intuitive manner through AR-HUD. This achieves a paradigm shift from "driver operating machine functions" to "system understanding and serving driver intent." A method for adaptively generating differentiated, multimodal augmented reality displays that conform to attention guidance principles, based on the semantics, dynamic characteristics, and environmental context of the target object, is provided to overcome the shortcomings of related technologies, such as the single, rigid, and insufficiently effective display methods of HUD targets.

[0137] The methods of the embodiments of this application will be further illustrated below.

[0138] In this application embodiment, a method and system for intelligent presentation of vehicle panoramic image information based on voice interaction is proposed. Its core is to construct a closed loop of "voice command driven, visual intelligent extraction, and AR scene fusion" to solve the safety hazards that drivers need to take their eyes off the road when obtaining surrounding information in complex environments, as well as the problems of abstract and passive existing HUD warning information.

[0139] Optionally, the driver can proactively express their concern for specific environmental elements around the vehicle through natural language voice commands (such as "Check the distance to the right" or "What cars are ahead on the left?"). After parsing the voice commands, the system uses computer vision algorithms from the real-time video stream of the panoramic imaging system to perform target recognition, feature extraction, and measurement (such as recognizing road shoulder lines and vehicle outlines, and calculating their distances), accurately capturing the "information fragment" indicated by the command, rather than outputting the entire video frame. The panoramic image target information is adaptively enhanced for display, specifically involving: identifying and tracking at least one target object from the real-time image of the vehicle's panoramic imaging system, and obtaining its multi-dimensional attribute information; the attribute information includes at least: semantic category (such as vehicle, pedestrian, bicycle, road shoulder, parking line), dynamic parameters (relative distance, relative speed, relative acceleration), spatial position, and outline information.

[0140] Optionally, a suitable "visualization modality" is assigned to the target object based on its semantic category and / or dynamic parameters. The visualization modalities include: contour enhancement modality, used to highlight the precise boundaries of an object, suitable for near static or low-speed targets that require precise avoidance (such as road shoulders or guardrails); guidance indication modality, used to indicate movement trends or guide paths, suitable for the planned path of the vehicle itself or the flow of vehicles in adjacent lanes; and risk identification modality, used to warn of potential collision risks, suitable for dynamic targets with high relative speeds and short expected collision times.

[0141] Optionally, based on the assigned visualization modality and the real-time dynamic parameters of the target, corresponding augmented reality graphic elements are dynamically generated, and their rendering attributes are determined. These rendering attributes include: graphic color (gradient from green to red according to risk level), transparency (adjusted according to distance or interference level), brightness, pulse frequency (for high-priority warnings), and graphic size. All generated augmented reality graphic elements are registered in 3D space according to the spatial location of their corresponding targets in the real world, determining their position in the HUD display coordinate system. Visual attention priority arbitration and layout optimization are performed, calculating the display priority score for each target (based on distance, speed, and category risk coefficient). Based on priority and spatial location, the final layout of the graphic elements on the HUD is automatically adjusted to avoid mutual occlusion and ensure that the highest priority target receives the most prominent visual presentation, while not conflicting with basic HUD information (vehicle speed, navigation).

[0142] Figure 3 This is a schematic diagram of a voice-interactive panoramic image information display system on a HUD according to an embodiment of this application, as shown below. Figure 3As shown, the system may include a data input layer 31, a core processing engine 32, and an output and presentation layer 33. The data input layer 31 may include a surround-view camera group 311, a vehicle bus 312, an ambient light sensor 313, and a driver status / voice module 314. The core processing engine 32 may include a target perception and attribute parsing module 321, a context perception unit 322, a visualization modal decision engine 323, an adaptive AR graphics generator 324, and a 3D registration and spatial management module 325. The output and presentation layer 33 may include an AR-HUD rendering control module 331, AR-HUD optical hardware 332, and driver visual perception 333. The AR-HUD optical hardware 332 may include an image generation unit (PGU) and an optical system.

[0143] Optionally, such as Figure 3 As shown, the surround-view camera group 311 is used to collect real-time environmental video streams from 360 degrees around the vehicle, providing raw visual input for the panoramic imaging system. It serves as the data foundation for target recognition and spatial perception, ensuring the system can comprehensively perceive static and dynamic objects around the vehicle. The vehicle bus 312 is used to acquire the vehicle's own dynamic operating parameters, including speed, steering angle, gear, acceleration, and braking status, providing key contextual information for the system to determine the current driving scenario (such as parking, cruising, and lane changing), supporting situational adaptive decision-making. The ambient light sensor 313 is used to monitor the light intensity of the external environment in real time, transmitting ambient brightness information to the system to dynamically adjust the brightness and contrast of augmented reality graphics, ensuring that graphics are clearly distinguishable and do not cause visual glare in nighttime, tunnels, or bright light environments. The driver status / voice module 314 is used to receive the driver's natural language voice commands or monitor their gaze direction, head posture, and other status information, serving as auxiliary input for intent recognition and priority arbitration, enabling driver-centric personalized interaction and attention guidance.

[0144] Optionally, such as Figure 3As shown, the target perception and attribute parsing module 321 is used to detect, classify, and track target objects in real time based on the video stream input from the surround-view camera group using deep learning algorithms. It outputs multi-dimensional attribute information such as semantic category, boundary contour, relative distance, relative speed, and spatial position of each target, and is the core perception module for the system to understand the environment. The aforementioned context perception unit 322 is used to fuse data from the vehicle bus and ambient light sensors to perform semantic recognition and labeling output for the current driving scenario, such as "following in urban congestion," "driving on a narrow road at night," or "changing lanes on a highway," providing contextual support for the dynamic adaptation of subsequent visualization strategies. By comprehensively analyzing vehicle bus signals and ambient light data, it outputs the current "driving scenario label," such as [following in urban congestion], [cruising on a highway], and [driving on a narrow road at night]. The aforementioned visualization modal decision engine 323 is used to select an appropriate graphic representation for each target object from a preset visualization modal library (such as contour enhancement, directional indication, and risk identification) based on the target attribute information and context label, and to initially determine its color, dynamic effects, and other rendering strategies. It is the core of intelligent decision-making for realizing semantic-to-visual mapping. The aforementioned adaptive AR graphics generator 324 is used to generate specific, parameterized augmented reality graphics elements based on the instructions output by the visualization modal decision engine and the target's real-time dynamic parameters. These elements include renderable attributes such as the shape, size, color gradient, transparency, and pulsation frequency of the augmented reality graphics elements. It is an execution unit that transforms abstract semantics into displayable visual elements.

[0145] Optionally, such as Figure 3As shown, the 3D registration and spatial management module 325 is used to precisely align the generated virtual graphic elements to their corresponding physical positions in the real world, completing the spatial registration between virtual and reality; at the same time, it calculates the display priority of each target and executes the layout optimization algorithm to solve the problem of occlusion and overlap between multiple graphics, ensuring that visual information is orderly, clear, and does not interfere with key driving information. The AR-HUD rendering control module 331 is used to receive the optimized graphic elements and their spatial coordinates, coordinate the underlying graphics engine to complete layer compositing, anti-aliasing processing, color correction, and dynamic effect rendering, and output the final image data stream that conforms to the visual characteristics of the human eye to the optical system. It is the last software control link before graphic presentation. In the AR-HUD optical hardware 332, the PGU is used to convert the digital image signal into a high-brightness, high-refresh-rate optical image. The optical system (including collimating lenses, combiners, etc.) then projects the image onto the windshield after reflection and beam expansion to form a telephoto virtual image, realizing the seamless integration of augmented reality information and real road. Driver visual perception 333 refers to the driver's natural reception of augmented reality graphics projected by AR-HUD while looking at the road ahead. This enables synchronous perception of information and the real environment, completing the "perception-understanding-decision" closed loop, and is the ultimate object and value realization point of the entire system. When looking at the road ahead, the driver naturally perceives augmented reality information that is integrated with the real environment, with distinct layers and key points, thus completing the entire information closed loop.

[0146] In this embodiment, compared to the closest related technology, the above method projects key environmental information actively queried by the driver directly onto their forward field of vision (HUD) via voice wake-up, avoiding the driver's eyes leaving the road due to looking down at the panoramic image on the central control screen, thus fundamentally reducing the risk of collision. Driven by "semantic category + dynamic parameters," appropriate visualization forms are assigned to targets (e.g., using "surfaces" to mark risk areas, "lines" to mark guiding paths, and "outlines" to mark precise objects), enabling the displayed content to carry richer semantic information and greatly improving intuitiveness. Graphic colors, transparency, and dynamic effects (e.g., pulsation) are automatically adjusted according to the real-time risk, distance, and ambient lighting of the target, achieving deep adaptive fusion of display and context, ensuring clear and readable information while minimizing visual interference. Through a priority arbitration mechanism, the driver's visual attention is automatically guided to the highest-risk or most relevant target, and intelligent layout avoids information overlap, achieving optimized configuration of HUD display resources and proactively improving driving safety.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0148] According to another aspect of the embodiments of this application, corresponding to the embodiments of the above-described vehicle information display method, this specification also provides a vehicle information display system.

[0149] Figure 4 This is a schematic diagram of a vehicle information display system according to an embodiment of this application, such as... Figure 4 As shown, the vehicle information display system 40 may include: a data input layer 401, used to respond to an information display command for at least one target object around the vehicle, and to obtain attribute information of the target object, wherein the attribute information is used to represent the appearance characteristics and / or operating status of the target object; a processing engine 402, used to determine a display strategy for the target object based on the attribute information, wherein the display strategy is used to represent the rules for displaying the target object; and to determine the graphic element to be displayed corresponding to the target object based on the display strategy and attribute information, and to determine the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes are used to represent the visual appearance of the graphic element to be displayed on the display client, which is deployed on the vehicle; and a display layer 403, used to invoke the display strategy and display the graphic element to be displayed with rendering attributes on the display client.

[0150] According to another aspect of the embodiments of this application, corresponding to the embodiments of the above-described vehicle information display method, this specification also provides a vehicle information display device.

[0151] Figure 5 This is a schematic diagram of a vehicle information display device according to an embodiment of this application, such as... Figure 5As shown, the vehicle information display device 50 may include: an acquisition module 502, a first determination module 504, a second determination module 506, and a calling module 508. The acquisition module 502 is used to acquire attribute information of the target object in response to an information display command for at least one target object around the vehicle, wherein the attribute information represents the appearance characteristics and / or operating status of the target object; the first determination module 504 is used to determine a display strategy for the target object based on the attribute information, wherein the display strategy represents the rules for displaying the target object; the second determination module 506 is used to determine the graphic element to be displayed corresponding to the target object and the rendering attributes required for the graphic element to be displayed based on the display strategy and attribute information, wherein the rendering attributes represent the visual appearance of the graphic element to be displayed on the display client, which is deployed on the vehicle; the calling module 508 is used to call the display strategy and display the graphic element to be displayed with rendering attributes on the display client.

[0152] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0153] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application, such as... Figure 6 As shown, an electronic device 60 is also provided, including: a memory 601 storing an executable program; and a processor 602 for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0154] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0155] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0156] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0157] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0158] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0160] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0161] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0162] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0163] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An information display method for a vehicle, characterized by, include: In response to an information display command for at least one target object around the vehicle, the vehicle acquires attribute information of the target object, wherein the attribute information is used to represent the appearance features and / or operating status of the target object; Based on the attribute information, a display strategy for the target object is determined, wherein the display strategy is used to represent the rules for displaying the target object; Based on the display strategy and the attribute information, the graphic element to be displayed corresponding to the target object is determined, and the rendering attributes required for the graphic element to be displayed are determined, wherein the rendering attributes are used to represent the visual appearance of the graphic element to be displayed on the display client, and the display client is deployed on the vehicle; The display strategy is invoked to display the graphic element to be displayed, which has the rendering attributes, on the display client.

2. The method of claim 1, wherein, Determining the display strategy for the target object based on the attribute information includes: The candidate display strategy that matches the attribute information from at least one candidate display strategy is determined as the display strategy.

3. The method of claim 2, wherein, The candidate display strategy includes a contour display strategy, which represents the rules for displaying the geometric boundaries of the target object. The attribute information includes the category information and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the running state of the target object. The step of determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy includes: In response to the attribute information satisfying at least one of the following conditions, the outline display strategy is determined to be the display strategy adapted to the attribute information: The semantic category is a static category, wherein the target object of the static category is in a static state; The dynamic parameters indicate that the speed of the target object is less than a speed threshold, and the distance between the target object and the vehicle is less than a distance threshold.

4. The method of claim 2, wherein, The candidate display strategy includes a guided display strategy, which represents a rule for displaying the movement trend of the target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the running state of the target object. Determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy includes: In response to the attribute information satisfying at least one of the following conditions, the guidance display strategy is determined to be the display strategy adapted to the attribute information: The semantic category is a dynamic category, wherein the target object of the dynamic category is in a moving state; The dynamic parameters represent the lane where the target object is located, and there is a correlation between the target object and the lane where the vehicle is located.

5. The method of claim 2, wherein, The candidate display strategy includes a risk display strategy, which represents a rule for displaying the area where a collision risk occurs between the vehicle and the target object. The attribute information includes the semantic category and / or dynamic parameters of the target object. The category information represents the appearance features of the target object, and the dynamic parameters represent the operating state of the target object. Determining the candidate display strategy that matches the attribute information from at least one candidate display strategy as the display strategy includes: In response to the attribute information satisfying at least one of the following conditions, the risk display strategy is determined to be the display strategy adapted to the attribute information: The dynamic parameter indicates that the speed of the target object is greater than the speed threshold. The distance between the target object and the vehicle is less than a distance threshold.

6. The method of claim 1, wherein, Determining the rendering attributes that the graphic element to be displayed needs to satisfy includes: Based on the display strategy, the initial rendering attributes of the graphic elements to be displayed are generated; Using the attribute information, the initial rendering attributes are adjusted to obtain the rendering attributes.

7. The method of claim 5, wherein, The rendering attributes include at least one of the following: color information of the graphic element to be displayed, transparency of the graphic element to be displayed, element brightness of the graphic element to be displayed, pulsation frequency of the graphic element to be displayed, and size information of the graphic element to be displayed. The step of adjusting the initial rendering attributes using the attribute information to obtain the final rendering attributes includes at least one of the following: Based on the attribute information, the risk level of the collision risk between the vehicle and the target object is determined, and the color information is adjusted according to the risk level, wherein the risk level matches the color information; Based on the attribute information, the interference degree of the target object on the vehicle is determined, and the transparency and size information are adjusted according to the interference degree, wherein the interference degree is negatively correlated with the transparency and the size information. Based on the attribute information, the warning priority of the target object is determined, and the pulse frequency is adjusted according to the warning priority, wherein the warning priority is positively correlated with the pulse frequency; Based on the attribute information, the brightness information of the environment in which the vehicle is located is determined, and the brightness of the element is adjusted according to the brightness information, wherein the brightness information and the brightness of the element are positively correlated.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: In response to the fact that the number of target objects is one, based on the position information of the target objects in the environment where the vehicle is located, the position coordinates of the graphic element to be displayed in the display coordinate system of the display client are determined, and based on the position coordinates, the layout strategy of the graphic element to be displayed on the display client is determined, wherein the layout strategy is used to represent the rules for laying out the graphic element to be displayed on the display client; In response to the fact that there are multiple target objects, the display priority corresponding to each of the multiple target objects is determined; the layout strategy is determined based on the position coordinates and the display priority. The step of invoking the display strategy to display the graphic element to be displayed, which has the rendering attributes, on the display client includes: The layout strategy is invoked to lay out the graphic elements to be displayed that have the rendering attributes on the display client; The display strategy is invoked to display the laid-out graphic element on the display client.

9. An information display system for a vehicle, characterized by comprising: include: A data input layer is used to respond to an information display instruction for at least one target object around the vehicle and obtain attribute information of the target object, wherein the attribute information is used to represent the appearance characteristics and / or operating status of the target object; A processing engine is configured to determine a display strategy for the target object based on the attribute information, wherein the display strategy represents the rules for displaying the target object; and to determine the graphic element to be displayed corresponding to the target object based on the display strategy and the attribute information, and to determine the rendering attributes required for the graphic element to be displayed, wherein the rendering attributes represent the visual appearance of the graphic element to be displayed on the display client, which is deployed on the vehicle. The display layer is used to invoke the display strategy and display the graphic element to be displayed, which has the rendering attributes, on the display client.

10. A vehicle characterized by comprising: include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

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