Display method, device and equipment based on vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle-based display method and device, equipment and a storage medium. The method comprises the following steps: acquiring driving information of a vehicle, receiving map information sent to the vehicle by a cloud end, and receiving motion information sent to the vehicle by a road side unit; wherein the map information represents a high-precision map of a road where the vehicle is located, and the motion information represents the motion condition of a dynamic object in the road where the vehicle is located; determining an object to be rendered according to the driving information, the map information and the motion information; and displaying the to-be-rendered object on a preset focal plane according to the category of the to-be-rendered object. Through cooperation of three parties of vehicle and road cloud, the rendering accuracy is improved, and the driving experience and safety are improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a vehicle-based display method, apparatus, device, and storage medium. Background Technology

[0002] With the widespread application of augmented reality technology in in-vehicle display systems, AR-HUD (Augmented Reality-Head-Up Display) has been gradually implemented in various vehicle models as an important means to improve driving safety and interactive experience.

[0003] However, there is a lot of information that needs to be rendered and displayed on the road. Currently, it is impossible to accurately display objects, which can easily cause visual confusion and dizziness, affecting the driving experience and driving safety. Summary of the Invention

[0004] This application provides a vehicle-based display method, apparatus, device, and storage medium to improve the accuracy of information display on vehicles and enhance driving experience and safety.

[0005] In a first aspect, embodiments of this application provide a vehicle-based display method, which is applied to a vehicle and includes:

[0006] The system acquires the vehicle's driving information, receives map information sent from the cloud to the vehicle, and receives motion information sent from the roadside unit to the vehicle; wherein the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0007] The object to be rendered is determined based on the driving information, the map information, and the motion information.

[0008] Based on the category of the object to be rendered, the object to be rendered is displayed on a preset focal plane.

[0009] Secondly, embodiments of this application provide a vehicle-based display device, which is applied to a vehicle and includes:

[0010] The information acquisition unit is used to acquire the vehicle's driving information, receive map information sent from the cloud to the vehicle, and receive motion information sent from the roadside unit to the vehicle; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0011] An object determination unit is used to determine the object to be rendered based on the driving information, the map information, and the motion information.

[0012] An object display unit is used to display the object to be rendered on a preset focal plane according to the category of the object to be rendered.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0014] The memory stores computer-executed instructions;

[0015] The processor executes computer execution instructions stored in the memory, causing the processor to perform the implementation method described in the first aspect above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the embodiments of the first aspect above.

[0017] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the implementation methods described in the first aspect above.

[0018] This application provides a vehicle-based display method, device, equipment, and storage medium. When a vehicle is in motion, it can acquire its own driving information in real time, receive map information sent from the cloud, and receive motion information sent from roadside units. The map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the movement of dynamic objects on the road, such as pedestrians and other vehicles. Based on the driving information, map information, and motion information, the system achieves collaboration among vehicle, road, and cloud information to determine the objects to be displayed in the AR-HUD, which are then designated as objects to be rendered. The category of the objects to be rendered is determined, and based on the category, the objects are displayed on the corresponding preset focal plane, achieving targeted display of the objects. Through the collaboration of vehicle, road, and cloud information and targeted rendering based on different focal planes, the accuracy of object rendering is improved, visual confusion is avoided, and the driving experience and safety are enhanced. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 A schematic flowchart illustrating a vehicle-based display method provided in an embodiment of this application;

[0021] Figure 2 A schematic flowchart illustrating a vehicle-based display method provided in an embodiment of this application;

[0022] Figure 3 A schematic flowchart illustrating a vehicle-based display method provided in an embodiment of this application;

[0023] Figure 4 A schematic flowchart illustrating a vehicle-based display method provided in an embodiment of this application;

[0024] Figure 5 A schematic diagram of a vehicle-based display device provided in an embodiment of this application;

[0025] Figure 6 A schematic diagram of a vehicle-based display device provided in an embodiment of this application;

[0026] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] First, let me explain the terms used in this application:

[0030] AR-HUD: AR-HUD uses laser projection or optical waveguide technology to project virtual information (such as navigation arrows, lane lines, and warning boxes) onto the windshield or a special reflector, forming a virtual image superimposed on the real scene. The core technology is light field display technology, which uses multi-layer gratings or microlens arrays to achieve virtual image projection at different depths of field, solving the dizziness problem caused by the single focal plane of the virtual image in traditional HUDs.

[0031] V2X: Enables communication between vehicles and roadside units (RSU), other vehicles (V2V), or the cloud (V2C);

[0032] Light field display technology: By using multi-layer gratings or microlens arrays, AR information (static maps, dynamic targets) from different sources is projected onto different virtual image distances (such as 7m, 15m), which conforms to the depth-of-field fusion characteristics of the human eye and avoids dizziness caused by the stacking of information on a single focal plane.

[0033] As autonomous driving technology advances towards advanced commercialization, AR-HUD technology is being applied in various vehicle models. Currently, the industry primarily employs two solutions to improve AR-HUD performance, especially in extreme environments and beyond-line-of-sight requirements:

[0034] Pure vehicle-side enhancement solution: By improving the performance of vehicle-side sensors and the computing power of domain controllers, all environmental perception, modeling and rendering work can be completed inside the vehicle;

[0035] V2X solution: Connect vehicles to the cloud via 4G / 5G networks to obtain real-time traffic information and display it in the form of simple icons on the central control screen or HUD.

[0036] However, in severe weather conditions such as heavy rain and fog, the performance of LiDAR and cameras deteriorates sharply, and the sensing distance is significantly reduced, rendering AR-HUD ineffective. Furthermore, vehicles require high-performance computing chips, resulting in high hardware costs and hindering its widespread adoption in low- to mid-range models. Additionally, the current lack of low-latency, high-precision dynamic target data for real-time rendering of AR-HUDs can easily cause visual confusion and dizziness, affecting rendering efficiency and accuracy, and consequently impacting driving safety.

[0037] This application provides a vehicle-based display method, apparatus, device, and storage medium, which aims to solve the above-mentioned technical problems of the prior art.

[0038] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0039] Figure 1 This is a flowchart illustrating a vehicle-based display method provided in an embodiment of this application. The method can be executed by a vehicle-based display device. The method is applied to vehicles, such as... Figure 1 As shown, the method includes:

[0040] S101. Obtain vehicle driving information, receive map information sent from the cloud to the vehicle, and receive motion information sent from the roadside unit to the vehicle; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0041] For example, in extreme environments such as heavy rain, dense fog, or low visibility at night, traditional AR-HUD systems, which rely on onboard cameras and LiDAR for environmental perception, often face problems such as sensor failure and reduced detection range, resulting in an inability to effectively identify obstacles or lane lines ahead. Therefore, vehicles need to combine information from the vehicle, the cloud, and roadside units to achieve vehicle-road-cloud collaboration. Vehicle-road-cloud collaboration can also be called cloud-edge-device collaboration, where "cloud" refers to the cloud platform, "edge" refers to edge nodes (roadside units), and "device" refers to the vehicle.

[0042] The vehicle is equipped with one or more onboard sensors that can collect data reflecting the current operating status of the vehicle in real time, serving as driving information. This driving information may include, but is not limited to, vehicle speed, acceleration, steering angle, yaw rate, geographical location, and attitude information. Driving information is one of the fundamental inputs for subsequent three-way data fusion. Driving information can be acquired in real time via the vehicle's CAN bus or domain controller, with a sampling frequency set to at least 10Hz to ensure continuous dynamic response.

[0043] When a vehicle is traveling on the road, it can communicate with the cloud in real time. The cloud is a pre-configured cloud platform that stores high-precision maps of various roads. The cloud can send map information to the vehicle in real time or at regular intervals. This map information refers to a high-precision map of the road where the vehicle is located, with an accuracy down to the centimeter level. It includes lane-level geometry, semantic information, and coordinates of static facilities. For example, lane-level geometry can include curvature, slope, and superelevation; semantic information can include lane type, speed limit, and traffic sign location; and coordinates of static facilities can include guardrails, streetlights, and traffic light poles. The map information can be proactively pushed by the cloud after matching the location information reported by the vehicle, or it can be pre-downloaded to the vehicle's local storage before the trip to reduce communication dependence. In this embodiment, a digital twin map can also be used as a form of map information. It not only includes geometric and semantic information but can also integrate dynamic prior knowledge such as historical traffic flow and weather impact models.

[0044] Motion information consists of state parameters of dynamic objects on the road, collected and broadcast by Road Side Units (RSUs) deployed along the road. These dynamic objects include, but are not limited to, other vehicles, non-motorized vehicles, pedestrians, animals, or temporary obstacles. Motion information can include indicators such as the object's position, speed, acceleration, and heading angle, as well as the object's category and behavioral intentions. For example, the category can include cars, trucks, and pedestrians, and the behavioral intention can include lane changing, braking, and parking. Motion information is not raw point cloud or image data, but a lightweight structured parameter package processed by RSU edge computing, reducing computation on the vehicle side. RSUs can broadcast via C-V2X (Cellular Vehicle-to-Everything) or 5G-V2X PC5 direct communication interfaces, featuring low latency and high reliability. Due to the high installation position and wide coverage of roadside units, their equipped millimeter-wave radar, lidar, and other sensors can operate stably in adverse weather conditions such as fog, rain, and snow, compensating for the limited line-of-sight and perception degradation of onboard sensors.

[0045] Driving information, map information, and motion information are sourced from the device, cloud, and edge respectively, forming a collaborative information acquisition architecture of "cloud-edge-device". Among them, driving information reflects individual behavior, map information provides a global static background, and motion information supplements dynamic elements beyond line of sight. Together, these three constitute a complete environmental cognition foundation, improving the subsequent display accuracy.

[0046] In this embodiment, receiving map information sent from the cloud to the vehicle includes: sending the vehicle's location information to the cloud; wherein the location information represents the road where the vehicle is located, and the location information is used to instruct the cloud to send corresponding map information to the vehicle; and receiving map information fed back from the cloud.

[0047] Specifically, vehicles can determine their own location information in real time. Vehicles are equipped with onboard communication modules, such as 5G-V2X or 4G / 5G cellular networks. These modules allow vehicles to actively initiate data requests and upload their current location information to a remote cloud. Location information is typically obtained based on the Global Navigation Satellite System (GNSS) and can be integrated with inertial navigation system data to improve positioning accuracy, especially maintaining continuity in areas with weak signals, such as tunnels or urban canyons. The specific form of location information can be latitude and longitude coordinates. Location information can represent the road the vehicle is currently on; that is, it can indicate which road, section, or even specific lane the vehicle is currently traveling on.

[0048] The cloud receives the vehicle's location information and searches for the corresponding high-definition map. For example, it can determine the geographical area covered by the vehicle's current and expected driving routes based on the received location information, and then filter out matching high-definition maps. The filtered high-definition maps are then fed back to the vehicle as map information. This mechanism avoids the bandwidth waste and storage pressure caused by pre-downloading the entire map, and is especially suitable for long-distance cross-regional driving scenarios.

[0049] The location information can also include dynamic state parameters such as vehicle speed, heading angle, and acceleration, so that the cloud can predict the vehicle's trajectory in the next few seconds and prepare the map information needed for the next stage in advance, thus realizing the preloading and optimization of map information.

[0050] The vehicle receives map information from the cloud, which includes, but is not limited to, non-real-time attributes such as lane geometry, traffic sign semantic labels, road marking topology, static obstacle locations, and historical traffic flow statistics.

[0051] The beneficial effect of this setup is that it enables an efficient data interaction mechanism between the vehicle and the cloud. Because the vehicle actively reports accurate location information, the cloud can identify the road where the vehicle is located and send relevant map information accordingly, improving map acquisition efficiency, reducing network resource consumption, and enhancing the fit between AR display and real road conditions.

[0052] In this embodiment, receiving motion information sent by the roadside unit to the vehicle includes: receiving motion information broadcast by the roadside unit via a V2X link; wherein, the motion information is information representing the motion of dynamic targets extracted from the point cloud data after the roadside unit collects the point cloud data of the road.

[0053] Specifically, the vehicle utilizes its onboard communication module to receive data packets actively broadcast by the Roadside Unit (RSU) in real time via a dedicated vehicle-to-everything (V2X) communication link. This V2X link can be based on the PC5 interface in 5G-V2X or the C-V2X direct communication protocol, supporting low-latency, high-reliability near-field data transmission with typical communication latency below 100ms, making it suitable for AR-HUD rendering scenarios with high real-time requirements. This communication link does not rely on public network base station relays, enabling direct communication between the vehicle and roadside units in areas without network coverage, ensuring the timely delivery of critical safety information.

[0054] Motion information refers to semantically abstracted information that characterizes the state parameters of dynamic objects, rather than raw point cloud or image frames. Motion information includes at least the dynamic object's position coordinates, velocity vector, acceleration, heading angle, confidence level, and category identifier. Generated by roadside units from raw point cloud data collected by radar sensors, motion information is lightweight structured data; the size of a single message is significantly smaller than that of raw point cloud data, effectively reducing wireless channel load and transmission latency.

[0055] Roadside units (LSUs), acting as edge computing nodes, are deployed at key locations along roads, such as intersections, curves, and tunnel entrances, possessing environmental perception and edge computing capabilities. Each LSU integrates at least one radar sensor to continuously scan its covered road area and acquire point cloud data in three-dimensional space. The LSU runs pre-defined target detection and trajectory prediction algorithms to identify dynamic objects from the raw point cloud and fit their motion trajectories, ultimately compressing the results into structured motion information for broadcast. In this embodiment, the pre-defined algorithms built into the LSU are not specifically limited. For example, the LSU may be responsible for perceiving dynamic objects such as vehicles, pedestrians, and accidents beyond visual range, abstracting the trajectories of these dynamic objects into lightweight motion equation parameters as motion information, and broadcasting this motion information via a low-latency V2X link.

[0056] The extraction of motion information occurs locally on the roadside unit, avoiding the uploading of massive amounts of raw point cloud data to the cloud or sending it directly to vehicles, significantly reducing communication overhead and vehicle-side parsing burden. For example, in foggy weather, the effective detection range of onboard LiDAR may drop to less than 50 meters, while roadside millimeter-wave radar, with its stronger penetration capability, can stably detect the presence of an accident vehicle ahead from 200 meters away. It can then quickly generate motion parameters of the stationary vehicle through edge computing, such as zero speed, zero acceleration, and fixed position. These calculated parameters are then encapsulated into a lightweight message and broadcast to passing vehicles. After receiving the motion information, the vehicle no longer needs to perform complex target recognition tasks; it only needs to integrate the motion information into the local rendering engine to achieve beyond-line-of-sight warning prompts.

[0057] The beneficial effect of this setup is that it enables the roadside unit to provide vehicles with high-precision, low-latency motion information of dynamic objects even under extreme weather or visual obstruction conditions. This solves the problem of AR-HUD display failure caused by the limited performance of onboard sensors, significantly reduces communication resource consumption and vehicle-side computing pressure, and allows vehicles to quickly respond to and render key objects under limited computing power, thereby improving the practicality and universality of the AR-HUD system.

[0058] S102. Determine the object to be rendered based on driving information, map information, and motion information.

[0059] For example, "determining the object to be rendered" refers to selecting the target entity or abstract information item that needs to be visualized on the AR-HUD interface from the fusion results of multi-source information. For instance, if there is a vehicle stopped due to a malfunction 300 meters ahead, although the malfunctioning vehicle is not within the vehicle's onboard sensor field of view, it can be listed as an object to be rendered because it is on the vehicle's predicted trajectory and is stationary, posing a collision risk; while vehicles traveling normally in the distance with no possibility of interaction can be ignored. Another example is that near a ramp exit, even if there is no direct danger, a virtual guide arrow can still be used as an object to be rendered to assist navigation. In this embodiment, the logic of the object to be rendered can be implemented based on a preset rule engine or machine learning model. For example, driving information, map information, and motion information can be input into the machine learning model to output one or more objects to be rendered. In this embodiment, the model architecture of the preset rule engine and machine learning model is not specifically limited.

[0060] By combining multi-source information from vehicles, roads, and the cloud, cross-modal data processing can be achieved, enabling spatiotemporal synchronization and coordinate transformation. For example, GNSS (Global Navigation Satellite System) fusion positioning results can be used to match the vehicle's own position to a high-precision map coordinate system. Then, the positions of dynamic objects broadcast by the RSU can be transformed to the map coordinate system using known RSU geographic coordinates, ultimately achieving joint modeling of all objects under the same spatial reference.

[0061] S103. Based on the category of the object to be rendered, display the object on the preset focal plane.

[0062] For example, the "category of the object to be rendered" is a classification of the function or physical attributes of the object to be displayed. Common categories include: static road elements (such as lane lines and stop lines), dynamic traffic participants (such as vehicles in front and pedestrians), warning prompts (such as collision warnings and blind spot reminders), and navigation guidance (such as turn arrows and lane keeping indicators). Different categories have different visual importance, frequency of change, and spatial characteristics.

[0063] Preset focal planes refer to multiple virtual image distance planes pre-defined in an AR-HUD optical system, corresponding to different depths of focus for human vision. For example, the far focal plane can be set to a virtual image distance of 15 meters, suitable for projecting distant road structures or targets beyond visual range; the mid focal plane can be set to 10 meters for regular vehicle following prompts; and the near focal plane can be set to 7 meters, suitable for displaying information about the vehicle's surroundings or fixed HUD icons. Light field display technology supports simultaneous imaging of multiple focal planes, avoiding the eye accommodation conflict and dizziness caused by the inconsistency between virtual and real focus in traditional single-focal-plane AR-HUDs.

[0064] For each object to be rendered, its category is determined, and based on that category, the object is displayed on the preset focal plane corresponding to that category. That is, layered rendering is performed based on the mapping relationship between object categories and their corresponding focal planes. For example, virtual lane lines generated by cloud maps are static environmental information and should be projected onto the far focal plane to make them visually "attached" to the real road surface; while the location of an accident vehicle ahead, provided by the RSU, is also projected onto the far focal plane as a high-risk dynamic object, with a red pulse border overlaid to enhance the warning effect; navigation arrows, which need to guide the driver's attention to the upcoming intersection, can be placed on the mid-focal plane; basic information such as vehicle speed is retained on the near focal plane. Through this category-based layered strategy, the spatially ordered distribution of information is achieved, conforming to natural visual habits.

[0065] This embodiment achieves the integration of cloud, edge, and terminal data sources to determine the objects to be rendered under complex traffic environments and extreme weather conditions, and implements differentiated depth-of-field rendering based on object categories. It solves the problem of vehicle-mounted sensors failing to perceive in low visibility conditions, avoids visual confusion and eye fatigue caused by multiple layers of information stacked on the same plane, and improves the realism and comfort of AR displays.

[0066] This application provides a vehicle-based display method. While driving, the vehicle can acquire its own driving information in real time, receive map information sent from the cloud, and receive motion information sent from roadside units. The map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the movement of dynamic objects on the road, such as pedestrians and other vehicles. Based on the driving information, map information, and motion information, the vehicle, road, and cloud information are coordinated to determine the objects to be displayed in the AR-HUD, which are then designated as objects to be rendered. The category of the objects to be rendered is determined, and based on the category, the objects are displayed on the corresponding preset focal plane, achieving targeted display of the objects. Through the three-way collaboration of the vehicle, road, and cloud, and targeted rendering based on different focal planes, the accuracy of object rendering is improved, visual confusion is avoided, and the driving experience and safety are enhanced.

[0067] Figure 2 The following is a flowchart illustrating a vehicle-based display method provided in an embodiment of this application, as shown below. Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, a vehicle-based display method is described in detail, the method comprising:

[0068] S201. Obtain vehicle driving information, receive map information sent from the cloud to the vehicle, and receive motion information sent from the roadside unit to the vehicle; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0069] For example, this step can refer to step S101 above, and will not be repeated here.

[0070] S202. Based on motion information, predict the trajectory information of dynamic objects on the road where the vehicle is located.

[0071] For example, motion information refers to data that characterizes the motion state of dynamic objects in the road, collected and processed by the roadside unit using radar sensors and then broadcast. For instance, in a C-V2X communication environment, the RSU acquires point cloud data based on millimeter-wave radar or lidar, performs target detection and tracking, abstracts the raw perceived point cloud data into a lightweight motion equation parameter package, and broadcasts it to surrounding vehicles in a low-latency manner via the PC5 interface.

[0072] After receiving motion information, the vehicle-side rendering engine can substitute the motion information of dynamic objects into preset motion equations to predict the trajectory of dynamic objects in the next few seconds. For example, it can extrapolate the future path of dynamic objects based on preset dynamic models. Commonly used dynamic models can include constant velocity models, constant acceleration models, and interactive multi-models. For example, when a vehicle in front is currently traveling straight at a constant speed, linear extrapolation can be used to predict that it will move approximately 90 meters along the lane centerline in the next 3 seconds; if the vehicle is changing lanes, then by combining turn signal signals, lateral acceleration, and lane topology information, a polynomial curve is used to fit its lateral displacement trend, thereby generating a more accurate trajectory envelope.

[0073] In this embodiment, trajectory prediction can also incorporate machine learning models, such as long short-term memory networks or graph neural networks, to identify behavioral intentions and predict paths using historical trajectory fragments and surrounding traffic flow context, thereby improving accuracy in complex scenarios.

[0074] S203. Based on driving information, map information, and trajectory information of dynamic objects on the road where the vehicle is located, determine the objects to be rendered.

[0075] For example, driving information refers to the vehicle's real-time operating status parameters, including but not limited to vehicle speed, acceleration, steering angle, yaw rate, current position, heading, and driving mode. This information is collected by the vehicle's CAN bus or domain controller, reflecting the vehicle's current behavioral intentions and maneuverability. Map information is a high-precision map obtained by the vehicle from the cloud, including road geometry, semantic information, and the location of static facilities.

[0076] After obtaining the vehicle's driving information, map information, and trajectory information of other dynamic objects, a spatiotemporal joint analysis is performed to determine whether there are potential conflicts or objects requiring special attention. Objects with potential conflicts or requiring special attention are identified as objects to be rendered. For example, when the vehicle is about to enter a curve and an accident vehicle 200 meters ahead is predicted to be a long-term stationary target, even if the current visual field is limited, the accident vehicle can still be identified as an object to be rendered. Similarly, if a vehicle in an adjacent lane is predicted to cut into the vehicle's lane in 2 seconds, that vehicle can also be marked as an object to be rendered.

[0077] This embodiment combines map information, driving information, and trajectory information to enhance the rationality of the rendering display. For example, in the merging area of ​​a ramp, it can actively monitor the trajectory trend of vehicles coming from the main road; near pedestrian crossings, it increases sensitivity to pedestrian crossing behavior. By comprehensively considering factors such as spatial distance, relative speed, collision time, and path intersection probability, the accuracy of the rendering display is improved.

[0078] This embodiment implements a dynamic filtering mechanism for objects to be rendered based on multi-dimensional information fusion. By introducing the ability to predict the future trajectory of dynamic objects, it solves the problem that relying solely on instantaneous perception information is insufficient to provide early warnings of potential dangers. This enables AR-HUD to proactively present risk targets that have not yet entered the driver's direct field of vision, thereby improving the safety of assisted driving and the user experience, and ensuring the accuracy and effectiveness of AR prompts.

[0079] In this embodiment, the object to be rendered is determined based on driving information, map information, and trajectory information of dynamic objects on the road where the vehicle is located. This includes: determining candidate objects on the road where the vehicle is located and the rendering priority of each candidate object based on driving information, map information, and trajectory information of dynamic objects on the road where the vehicle is located; wherein, candidate objects include dynamic objects and / or static information; and determining the object to be rendered based on the rendering priority of each candidate object.

[0080] Specifically, it can determine the candidate objects on the road where the vehicle is located and the rendering priority of each candidate object. Candidate objects are objects that may need to be rendered; they can be dynamic objects or static information. Different candidate objects have different priorities, which represent the urgency of rendering. The higher the priority, the more necessary it is to be rendered; the lower the priority, the less necessary it may be to be rendered.

[0081] Based on the trajectory information of dynamic objects along the road where the vehicle is located, all dynamic objects in that road can be identified as candidate objects. Then, combined with driving and map information, the danger level of each candidate object to the vehicle is determined; the higher the danger level, the higher the priority. Alternatively, dynamic objects along the road can be filtered first based on their trajectory information, identifying those with abnormal motion states as candidate objects. Then, driving and map information are combined to determine the priority of each candidate object. Abnormal motion state judgment rules can be preset; for example, if the static time of a dynamic object exceeds a preset time threshold, the dynamic object is considered abnormal.

[0082] This embodiment combines map information and driving information to enhance the rationality of priority judgment. For example, in the merging area of ​​a ramp, the trajectory trend of vehicles coming from the main road can be actively monitored; near pedestrian crossings, the sensitivity to pedestrian crossing behavior is increased. By comprehensively considering factors such as spatial distance, relative speed, collision time, and path intersection probability, a set of candidate objects is constructed, and each candidate object is assigned a different rendering priority.

[0083] Determining rendering priority can also rely on a multi-dimensional evaluation model. This embodiment can comprehensively consider the following factors:

[0084] Object Category: Objects of different categories have different basic priorities. For example, "pedestrians crossing the road" has a higher basic priority than "vehicles far ahead", and "stationary obstacles" has a higher basic priority than "vehicles following normally".

[0085] Relative distance and approach speed: Threat level is calculated using either a TTC (Time to Collision) or minimum safe distance model. For example, if a moving object is expected to enter within 50 meters in front of the vehicle within 3 seconds and its relative speed is greater than a threshold, its priority is significantly increased.

[0086] Spatial relationship matching degree: Analyze whether the candidate object is located on the vehicle's predicted driving path. If the candidate object is in the adjacent lane and has no intention to change lanes, the priority is reduced; if it is in the lane of the vehicle or about to merge, the priority is increased.

[0087] Enhanced environmental context: Priorities are adjusted by combining auxiliary information such as weather, lighting, and road type. For example, in heavy rain or fog, the priority of all dynamic objects perceived beyond line of sight is increased to compensate for blind spots caused by the failure of onboard sensors.

[0088] Based on the rendering priority of each candidate object, one or more candidate objects are selected as the objects to be rendered. For example, candidate objects with a preset priority can be selected as the objects to be rendered, or the candidate object with the highest priority can be selected as the objects to be rendered.

[0089] The beneficial effects of this setup are that driving information determines the prediction of vehicle behavior, map information provides spatial reference and static context, and dynamic trajectory information supplements the external interactive situation. These three elements work together in the candidate object identification and priority determination process, ensuring that the objects to be rendered can be accurately extracted from complex traffic environments, thus improving display accuracy.

[0090] S204. Based on the category of the object to be rendered, display the object on the preset focal plane.

[0091] For example, this step can refer to step S103 above, and will not be repeated here.

[0092] This application provides a vehicle-based display method. While driving, the vehicle can acquire its own driving information in real time, receive map information sent from the cloud, and receive motion information sent from roadside units. The map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the movement of dynamic objects on the road, such as pedestrians and other vehicles. Based on the driving information, map information, and motion information, the vehicle, road, and cloud information are coordinated to determine the objects to be displayed in the AR-HUD, which are then designated as objects to be rendered. The category of the objects to be rendered is determined, and based on the category, the objects are displayed on the corresponding preset focal plane, achieving targeted display of the objects. Through the three-way collaboration of the vehicle, road, and cloud, and targeted rendering based on different focal planes, the accuracy of object rendering is improved, visual confusion is avoided, and the driving experience and safety are enhanced.

[0093] Figure 3 The following is a flowchart illustrating a vehicle-based display method provided in an embodiment of this application, as shown below. Figure 3 As shown, this embodiment, based on the above embodiments, provides a detailed description of a vehicle-based display method, which includes:

[0094] S301. Obtain vehicle driving information, receive map information sent to the vehicle from the cloud, and receive motion information sent to the vehicle from the roadside unit; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0095] For example, this step can refer to step S101 above, and will not be repeated here.

[0096] S302. Based on driving information, map information, and motion information, determine the object to be rendered.

[0097] For example, this step can refer to step S102 above, and will not be repeated here.

[0098] S303. Based on the preset first association relationship, determine the preset focal plane corresponding to the category of the object to be rendered, and based on the preset second association relationship, determine the display style corresponding to the category of the object to be rendered; wherein, the preset first association relationship represents the association relationship between the category of the object to be rendered and the preset focal plane, and the preset second association relationship represents the association relationship between the category of the object to be rendered and the display style.

[0099] For example, the category of the object to be rendered refers to the semantic category to which the object to be presented in the AR-HUD system belongs. This can include, but is not limited to, static road structures (such as lane lines, traffic signs, and guardrails), dynamic traffic participants (such as vehicles ahead, pedestrians, and non-motorized vehicles), abnormal event targets (such as accident vehicles, construction areas, and obstacles), and vehicle-mounted auxiliary information (such as navigation guide arrows, speed prompts, and speed limit signs). Different categories of objects to be rendered carry different safety levels and spatial attributes, therefore, differentiated rendering strategies are required to improve visual integration.

[0100] The preset first association is a pre-established mapping rule between object categories and specific virtual image distances to the focal plane in the light field HUD. The specific virtual image distance to the focal plane is the preset focal plane. This first association can be pre-configured and stored based on the object's spatial location characteristics, motion state, and the natural focusing habits of the human eye. For example, static road topology information perceived at long distances or beyond visual range (such as curve curvature and slope changes) can be assigned to the far focal plane (e.g., a 15-meter virtual image distance), while near-range dynamic interactive information (such as forward braking warnings) is projected onto the mid-focal plane (e.g., 10 meters), and basic driving information (such as current vehicle speed and turn signal status) is placed on the near focal plane (e.g., 7 meters). This layered layout strategy conforms to the depth perception mechanism of the human visual system, avoiding visual fatigue caused by frequent focusing.

[0101] The pre-defined second association is a mapping rule between object categories and their visual representations. The visual representation, or display style, can include display parameters such as display color, transparency, border style, flashing frequency, and animation effects. For example, a red pulse flashing frame with an overlaid sound alert icon can be used for vehicles ahead; a blue solid arrow can be used for general navigation guidance information; and white numbers with a gray background frame can be used for road speed limit signs. This design not only enhances information recognition but also quickly attracts the driver's attention in complex environments. In this embodiment, the first and second associations can be personalized based on different vehicle configurations, ambient lighting conditions, or driver habits.

[0102] After obtaining the category of the object to be rendered, for each object, the corresponding preset focal plane can be determined according to the first association relationship, and the corresponding display style can be determined according to the second association relationship.

[0103] The two types of relationships mentioned above can be configured independently or jointly optimized to form a unified rendering strategy table. Working together, they ensure that each type of object to be rendered is projected onto an optical focal plane that conforms to spatial logic and is presented in the most suitable visual style, thereby improving the efficiency and security of information expression.

[0104] S304. Based on the display style corresponding to the category of the object to be rendered, display the object to be rendered on the preset focal plane corresponding to the category of the object to be rendered.

[0105] For example, the vehicle is equipped with an AR rendering engine that can call a graphics processing unit or a dedicated display processor to generate graphic layers according to the display style defined in the second association relationship, and map them onto the corresponding preset focal plane. The light field HUD, as the core display device, has multi-plane focusing capabilities and can simultaneously present multiple virtual image distances through spatial light modulators, holographic optical elements, or multi-layer liquid crystal panels.

[0106] For example, if an object to be rendered is classified as a "distant static road element," the engine can find the corresponding preset focal plane of 15m from the first association relationship and the corresponding display style of semi-transparent blue lane lines from the second association relationship. Finally, the engine accurately projects the object to be rendered onto a virtual plane 15 meters away, aligning it with the real road geometry. Similarly, if a dynamic target with a potential collision risk is detected ahead, it is assigned to the same distant focal plane or a slightly closer focal plane and a high-contrast, high-frequency flashing red warning box is applied to highlight its dangerous attributes.

[0107] Each preset focal plane does not interfere with the others, allowing them to be spatially superimposed but visually separated. Drivers can clearly identify different levels of information without actively adjusting their eye focus. Furthermore, the focal plane distribution or display style can be dynamically fine-tuned based on contextual factors such as vehicle speed, ambient light intensity, and weather conditions. For example, the focal plane spacing can be appropriately compressed at high speeds to reduce visual jumps; and the brightness and flashing frequency of critical warning information can be increased at night or in low visibility conditions.

[0108] This embodiment achieves automatic matching of the optimal optical projection focal plane and visual expression mode according to the different types of objects to be rendered. Different types of objects are projected onto preset focal planes with different virtual image distances according to their spatial attributes and safety priorities. Combined with differentiated display styles, the recognition effect is enhanced. This solves the problems of virtual and real misalignment, information stacking and visual dizziness caused by traditional AR-HUDs that concentrate all information on a single focal plane. It improves the spatial fit of AR images, reduces cognitive load, improves human-computer interaction experience, and enhances driving safety.

[0109] This application provides a vehicle-based display method. While driving, the vehicle can acquire its own driving information in real time, receive map information sent from the cloud, and receive motion information sent from roadside units. The map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the movement of dynamic objects on the road, such as pedestrians and other vehicles. Based on the driving information, map information, and motion information, the vehicle, road, and cloud information are coordinated to determine the objects to be displayed in the AR-HUD, which are then designated as objects to be rendered. The category of the objects to be rendered is determined, and based on the category, the objects are displayed on the corresponding preset focal plane, achieving targeted display of the objects. Through the three-way collaboration of the vehicle, road, and cloud, and targeted rendering based on different focal planes, the accuracy of object rendering is improved, visual confusion is avoided, and the driving experience and safety are enhanced.

[0110] Figure 4 This is a flowchart illustrating a vehicle-based display method provided in an embodiment of this application. The method can be executed by a vehicle-based display device. This method is applied to roadside units, such as... Figure 4 As shown, the method includes:

[0111] S401: Based on a preset radar sensor, collect point cloud data of the road.

[0112] For example, a radar sensor refers to an environmental sensing device installed in a roadside unit, used to actively emit electromagnetic waves or laser beams and receive reflected signals to obtain information on the distance, speed, orientation, and three-dimensional structure of objects in the road space. Radar sensors may include, but are not limited to, millimeter-wave radar, lidar, and ultrasonic radar. Millimeter-wave radar or solid-state lidar with high resolution, strong penetration, and all-weather operation capabilities can be selected, suitable for stable detection of long-range dynamic targets under adverse weather conditions such as rain, fog, and snow.

[0113] Roadside units can collect point cloud data of their respective roads in real time using radar sensors. In this embodiment, the radar sensors can be combined with other sensing modules such as cameras and infrared sensors to form a multi-source fusion sensing array, thereby improving blind spot coverage and target tracking accuracy through collaborative sensing.

[0114] S402. Based on point cloud data, determine the motion information of dynamic targets in the road; whereby the motion information characterizes the motion of the dynamic object.

[0115] For example, point cloud data refers to the raw three-dimensional spatial sampling set collected by radar sensors. It is represented as a discrete sequence of spatial points and needs to be processed by algorithms such as filtering, clustering, and segmentation to extract the effective point groups belonging to dynamic objects. For example, statistical filtering is used to remove noise points, Euclidean clustering algorithm is used to divide the point cloud into several independent target clusters, and ground plane fitting technology is combined to remove static backgrounds such as guardrails, road signs, and trees.

[0116] Dynamic objects refer to traffic participants with displacement behavior in a road environment, including motor vehicles, non-motor vehicles, pedestrians, animals, or other temporary obstacles. By performing target matching and trajectory association on continuous frame point clouds, motion parameters such as the position, velocity, acceleration, heading angle, and their changing trends of each object can be calculated. These parameters together constitute motion information, enabling a quantitative description of the motion state of dynamic objects.

[0117] In this embodiment, detected objects can be fitted to minimum bounding boxes and assigned unique IDs to continuously track their motion trajectories. Motion information can be further abstracted into simplified mathematical expressions, such as uniform linear motion models, uniform acceleration models, or polynomial trajectory equations, facilitating compressed transmission. This embodiment does not impose specific limitations on the detection and tracking methods for dynamic objects.

[0118] It is worth noting that this step emphasizes completing the entire processing flow from raw point cloud to motion parameters at the edge, rather than uploading raw data to the cloud or vehicle, which significantly reduces communication bandwidth requirements and end-to-end latency, and alleviates the computational burden on the vehicle.

[0119] S403. Broadcast motion information to vehicles on the road; wherein, the motion information is used to instruct vehicles to determine the object to be rendered based on driving information, map information, and motion information, and to display the object to be rendered on a preset focal plane according to the category of the object to be rendered; driving information represents the driving information acquired by the vehicle, and map information represents the map information sent by the cloud to the vehicle.

[0120] For example, after determining motion information, the roadside unit can broadcast the motion information to vehicles on the road to which the roadside unit belongs, based on the PC5 interface in the C-V2X technology of the cellular network.

[0121] The broadcast motion information can be encapsulated in a lightweight data packet format, such as JSON. After receiving the motion information, the vehicle can perform spatiotemporal alignment and fusion analysis with its own acquired driving information and map information sent from the cloud, thereby identifying the object to be rendered and performing targeted rendering of the object.

[0122] For example, in foggy weather, onboard sensors can only detect objects within 50 meters ahead, while motion information provided by roadside units can reveal the presence of an accident vehicle 200 meters away. The vehicle can then prioritize the accident vehicle for rendering and project a red warning frame onto the far-field of the AR-HUD, achieving beyond-line-of-sight visual enhancement.

[0123] This embodiment achieves a closed-loop function where roadside units (RSUs) participate as edge nodes in the in-vehicle AR display system. Specifically, by deploying RSUs with radar sensing capabilities, all-weather monitoring of dynamic objects in complex traffic environments is achieved; localized data processing transforms raw point clouds into lightweight motion parameters, reducing communication load and latency; and a low-latency V2X broadcast mechanism enables vehicles to acquire beyond-line-of-sight dynamic information in a timely manner, supporting accurate rendering decisions for high-priority objects in the AR-HUD. This not only enhances the environmental adaptability of the in-vehicle AR system but also promotes the development of vehicle-road cooperative mechanisms, providing a safer and more intuitive interactive experience for intelligent connected vehicles.

[0124] This application provides a vehicle-based display method. While driving, the vehicle can acquire its own driving information in real time, receive map information sent from the cloud, and receive motion information sent from roadside units. The map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the movement of dynamic objects on the road, such as pedestrians and other vehicles. Based on the driving information, map information, and motion information, the vehicle, road, and cloud information are coordinated to determine the objects to be displayed in the AR-HUD, which are then designated as objects to be rendered. The category of the objects to be rendered is determined, and based on the category, the objects are displayed on the corresponding preset focal plane, achieving targeted display of the objects. Through the three-way collaboration of the vehicle, road, and cloud, and targeted rendering based on different focal planes, the accuracy of object rendering is improved, visual confusion is avoided, and the driving experience and safety are enhanced.

[0125] Figure 5 This is a schematic diagram of a vehicle-based display device provided as an embodiment of this application. The device is applied to a vehicle. Figure 5 As shown, the vehicle-based display device 50 provided in this embodiment includes:

[0126] The information acquisition unit 501 is used to acquire vehicle driving information, receive map information sent from the cloud to the vehicle, and receive motion information sent from the roadside unit to the vehicle; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located.

[0127] The object determination unit 502 is used to determine the object to be rendered based on driving information, map information, and motion information.

[0128] The object display unit 503 is used to display the object to be rendered on a preset focal plane according to the category of the object to be rendered.

[0129] In one possible implementation, the information acquisition unit 501 includes:

[0130] The map receiving module is used to send the vehicle's location information to the cloud; the location information represents the road where the vehicle is located, and the location information is used to instruct the cloud to send the corresponding map information to the vehicle; and it receives the map information fed back from the cloud.

[0131] In one possible implementation, the information acquisition unit 501 includes:

[0132] The motion receiving module is used to receive motion information broadcast by the roadside unit via the V2X link; wherein, the motion information is information representing the motion of dynamic targets extracted from the point cloud data after the roadside unit collects the point cloud data of the road.

[0133] In one possible implementation, the object determination unit 502 includes:

[0134] The trajectory determination module is used to predict the trajectory information of dynamic objects in the road where the vehicle is located based on motion information;

[0135] The object determination module is used to determine the objects to be rendered based on driving information, map information, and trajectory information of dynamic objects on the road where the vehicle is located.

[0136] In one possible implementation, the object determination module is specifically used for:

[0137] Based on driving information, map information, and trajectory information of dynamic objects on the road where the vehicle is located, candidate objects on the road where the vehicle is located and the rendering priority of each candidate object are determined; among them, candidate objects include dynamic objects and / or static information;

[0138] The object to be rendered is determined based on the rendering priority of each candidate object.

[0139] In one possible implementation, the object display unit 503 is specifically used for:

[0140] Based on a preset first association relationship, a preset focal plane corresponding to the category of the object to be rendered is determined, and based on a preset second association relationship, a display style corresponding to the category of the object to be rendered is determined; wherein, the preset first association relationship represents the association relationship between the category of the object to be rendered and the preset focal plane, and the preset second association relationship represents the association relationship between the category of the object to be rendered and the display style;

[0141] Based on the display style corresponding to the category of the object to be rendered, the object to be rendered is displayed on the preset focal plane corresponding to the category of the object to be rendered.

[0142] This embodiment provides a vehicle-based display device that can execute the methods provided in the above-described method embodiments. Its implementation principle and technical effects are similar, and will not be described in detail here.

[0143] Figure 6 This is a schematic diagram of a vehicle-based display device provided in an embodiment of this application, which is applied to a roadside unit. Figure 6 As shown, the vehicle-based display device 60 provided in this embodiment includes:

[0144] The data acquisition unit 601 is used to acquire point cloud data of the road based on a preset radar sensor;

[0145] The information determination unit 602 is used to determine the motion information of dynamic targets in the road based on point cloud data; wherein, the motion information represents the motion of the dynamic object;

[0146] The information broadcasting unit 603 is used to broadcast motion information to vehicles on the road; wherein, the motion information is used to instruct the vehicle to determine the object to be rendered based on the driving information, map information and motion information, and to display the object to be rendered on a preset focal plane according to the category of the object to be rendered; the driving information represents the driving information acquired by the vehicle, and the map information represents the map information sent by the cloud received by the vehicle.

[0147] This embodiment provides a vehicle-based display device that can execute the methods provided in the above-described method embodiments. Its implementation principle and technical effects are similar, and will not be described in detail here.

[0148] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0149] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0150] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0151] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0152] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0153] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0154] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0155] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0156] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0157] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0158] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention 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.

[0161] If a function 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 invention, or the part that contributes to the prior art, or a 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 of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0163] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A vehicle-based display method, characterized in that, The method is applied to a vehicle; the method includes: The system acquires the vehicle's driving information, receives map information sent from the cloud to the vehicle, and receives motion information sent from the roadside unit to the vehicle; wherein the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located. The object to be rendered is determined based on the driving information, the map information, and the motion information. Based on the category of the object to be rendered, the object to be rendered is displayed on a preset focal plane.

2. The method according to claim 1, characterized in that, Receive map information sent from the cloud to the vehicle, including: The vehicle's location information is sent to the cloud; wherein the location information represents the road where the vehicle is located, and the location information is used to instruct the cloud to send the corresponding map information to the vehicle; Receive the map information fed back from the cloud.

3. The method according to claim 1, characterized in that, Receiving motion information sent to the vehicle by the roadside unit, including: The motion information broadcast by the roadside unit is received via a V2X link; wherein, the motion information is information representing the motion of dynamic targets extracted from the point cloud data after the roadside unit collects the point cloud data of the road.

4. The method according to claim 1, characterized in that, Based on the driving information, the map information, and the motion information, the object to be rendered is determined, including: Based on the motion information, predict the trajectory information of dynamic objects on the road where the vehicle is located; The object to be rendered is determined based on the driving information, the map information, and the trajectory information of dynamic objects on the road where the vehicle is located.

5. The method according to claim 4, characterized in that, Based on the driving information, the map information, and the trajectory information of dynamic objects on the road where the vehicle is located, the object to be rendered is determined, including: Based on the driving information, the map information, and the trajectory information of dynamic objects on the road where the vehicle is located, candidate objects on the road where the vehicle is located and the rendering priority of each candidate object are determined; wherein, the candidate objects include dynamic objects and / or static information; The object to be rendered is determined based on the rendering priority of each candidate object.

6. The method according to claim 1, characterized in that, Based on the category of the object to be rendered, displaying the object on a preset focal plane includes: Based on a preset first association relationship, a preset focal plane corresponding to the category of the object to be rendered is determined, and based on a preset second association relationship, a display style corresponding to the category of the object to be rendered is determined; wherein, the preset first association relationship represents the association relationship between the category of the object to be rendered and the preset focal plane, and the preset second association relationship represents the association relationship between the category of the object to be rendered and the display style; Based on the display style corresponding to the category of the object to be rendered, the object to be rendered is displayed on a preset focal plane corresponding to the category of the object to be rendered.

7. A vehicle-based display method, characterized in that, The method is applied to roadside units; the method includes: Based on preset radar sensors, point cloud data of the road is collected; Based on the point cloud data, motion information of dynamic targets in the road is determined; wherein, the motion information characterizes the motion of the dynamic objects; The motion information is broadcast to vehicles on the road; wherein, the motion information is used to instruct vehicles to determine the object to be rendered based on driving information, map information, and motion information, and to display the object to be rendered on a preset focal plane according to the category of the object to be rendered; the driving information represents the driving information acquired by the vehicle, and the map information represents the map information sent by the cloud received by the vehicle.

8. A vehicle-based display device, characterized in that, The device is applied to a vehicle; the device includes: The information acquisition unit is used to acquire the vehicle's driving information, receive map information sent from the cloud to the vehicle, and receive motion information sent from the roadside unit to the vehicle; wherein, the map information represents a high-precision map of the road where the vehicle is located, and the motion information represents the motion of dynamic objects in the road where the vehicle is located. An object determination unit is used to determine the object to be rendered based on the driving information, the map information, and the motion information. An object display unit is used to display the object to be rendered on a preset focal plane according to the category of the object to be rendered.

9. A vehicle-based display device, characterized in that, The device is applied to a roadside unit; the device includes: The data acquisition unit is used to collect point cloud data of the road based on a preset radar sensor; An information determination unit is used to determine the motion information of a dynamic target in the road based on the point cloud data; wherein the motion information characterizes the motion of the dynamic object; An information broadcasting unit is used to broadcast the motion information to vehicles on the road; wherein, the motion information is used to instruct vehicles to determine the object to be rendered based on driving information, map information, and motion information, and to display the object to be rendered on a preset focal plane according to the category of the object to be rendered; the driving information represents the driving information acquired by the vehicle, and the map information represents the map information received by the vehicle from the cloud.

10. An electronic device / computer-readable storage medium / computer program product, characterized in that, The electronic device includes: a memory, a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7; and / or, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7; and / or, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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