Visualization processing method and apparatus for vehicle model, and device

Abstract

The embodiments of the present application relate to the technical field of autonomous driving. Provided are a visualization processing method and apparatus for a vehicle model, and a device. In the solution, when the display style of an on-board large screen of a vehicle is an interesting display mode, if it has been determined that there is a target vehicle within a preset range of the vehicle and traveling features of the target vehicle meet a preset interesting display condition, a target visualization model of the target vehicle is determined, and the target vehicle is displayed in the on-board large screen by means of the target visualization model, thereby providing personalized driving experience for a user and improving human-machine interaction.

Description

Visualization methods, devices and equipment for vehicle models

[0001] This application claims priority to Chinese Patent Application No. 202411673415.5, filed on November 21, 2024, entitled "Visualization Processing Method, Apparatus and Equipment for Vehicle Models", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of autonomous driving technology, and in particular to a method, apparatus and device for visualizing vehicle models. Background Technology

[0003] With the rapid development of autonomous driving technology, drivers typically rely on in-vehicle screens to obtain information about the surrounding driving environment.

[0004] Currently, in-vehicle displays use a preset model display style to present vehicle models and convey key information about the vehicle's surrounding environment. Specifically, the in-vehicle displays rely on a preset graphical interface to display core information about the vehicle and its surrounding environment in a concise and clear manner.

[0005] However, in the process of autonomous driving, the existing human-computer interaction functions are relatively simple and it is difficult to provide a personalized driving experience. Summary of the Invention

[0006] This application provides a method, apparatus, and device for visualizing vehicle models, which can provide a personalized driving experience and improve human-computer interaction.

[0007] Firstly, this application provides a visualization processing method for a vehicle model, the method comprising:

[0008] When the display style of the vehicle's in-vehicle screen is in the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display, then the target visualization model of the target vehicle is determined.

[0009] The target vehicle is displayed on the in-vehicle large screen using the target visualization model.

[0010] In one possible design of the first aspect, determining the target visualization model of the target vehicle includes:

[0011] A visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model;

[0012] The visualization model material library includes various driving features and the correspondence between at least one driving feature and a visualization model.

[0013] In one possible design of the first aspect, the target vehicle includes a vehicle having at least one of the following driving characteristics:

[0014] Vehicles traveling directly in front of the vehicle in the lane in which the vehicle is located;

[0015] Vehicles traveling on the left side of the road adjacent to and / or in front of the vehicle;

[0016] Vehicles that change lanes from in front of the vehicle and enter the lane where the vehicle is located;

[0017] Vehicles that violate the preset range;

[0018] Vehicles of a preset type within the preset range.

[0019] In one possible design of the first aspect, if the target vehicle is a vehicle that illegally changes lanes from in front of the vehicle into the lane where the vehicle is located, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including:

[0020] From the visualization model material library, obtain the visualization models corresponding to the two driving characteristics of illegal driving and lane change ahead, and use them as the target visualization models.

[0021] In one possible design of the first aspect, if the target vehicle is a vehicle traveling directly in front of the vehicle in the lane where the target vehicle is located, and the average speed and maximum speed of the target vehicle both conform to a preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including:

[0022] From the visualization model material library, obtain visualization models corresponding to the two driving characteristics of slow driving and driving directly in front of the vehicle in its lane, and use them as the target visualization models.

[0023] In one possible design of the first aspect, if the target vehicle is a vehicle traveling on the left side of the road adjacent to and / or ahead of the vehicle, and the average speed and maximum speed of the target vehicle both conform to a preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including:

[0024] From the visualization model material library, obtain visualization models corresponding to the two driving characteristics of slow driving and driving adjacent to and / or ahead of the vehicle on the left side of the road, and use them as the target visualization models.

[0025] In one possible design of the first aspect, the method further includes:

[0026] When the driving characteristics of the target vehicle do not meet the preset conditions for the fun display, the target vehicle will be displayed on the in-vehicle screen in a conventional visualization model.

[0027] And / or,

[0028] When the target visualization model is displayed on the in-vehicle screen for a preset duration, the target vehicle is then displayed on the in-vehicle screen using the conventional visualization model.

[0029] Secondly, this application provides a visualization processing device for a vehicle model, comprising:

[0030] The processing module is used to determine the target visualization model of the target vehicle when the display style of the vehicle's in-vehicle screen is in the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display.

[0031] The display module is used to display the target vehicle in the target visualization model on the in-vehicle large screen.

[0032] Thirdly, this application provides an in-vehicle large screen, including: a transceiver, a processor, and a memory communicatively connected to the processor; the memory stores computer-executed instructions;

[0033] The processor executes computer execution instructions stored in the memory to implement the vehicle model visualization processing method as described in any of the first aspects.

[0034] Fourthly, this application provides a vehicle, including: a vehicle body and the in-vehicle large screen described in the third aspect.

[0035] Fifthly, 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 first aspect and / or various possible implementations of the first aspect.

[0036] Sixthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0037] In a seventh aspect, embodiments of this application provide a chip, the chip including a memory and a processor, the memory storing code and data, the memory being coupled to the processor, and the processor running a program in the memory such that the chip is used to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0038] Eighthly, embodiments of this application provide a computer program that, when executed by a processor, performs the first aspect and / or various possible implementations of the first aspect.

[0039] The vehicle model visualization processing method, apparatus, and device provided in this application relate to the field of autonomous driving technology. Compared with the single-style display mode in the prior art, this method provides users with an engaging display mode through the vehicle's in-vehicle screen, significantly enhancing the user experience's fun and interactivity. When the in-vehicle screen is in engaging display mode, this method can intelligently identify target vehicles within a preset range and, based on whether their driving characteristics meet the preset conditions for engaging display, determine and display a personalized visualization model of the target vehicle, thereby enhancing driving entertainment, improving human-computer interaction, and providing users with a personalized driving experience. Attached Figure Description

[0040] 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.

[0041] Figure 1 is a schematic diagram of a scenario for the visualization processing method of the vehicle model provided in this application;

[0042] Figure 2 is a flowchart illustrating the visualization processing method for the vehicle model provided in this application.

[0043] Figure 3 is a flowchart illustrating the visualization processing method for the vehicle model provided in this application.

[0044] Figure 4 is a schematic diagram of a target vehicle that illegally changes lanes, as provided in this application.

[0045] Figure 5 is a schematic diagram of a target visualization model of a target vehicle provided in this application;

[0046] Figure 6 is a flowchart illustrating the visualization processing method for the vehicle model provided in this application.

[0047] Figure 7 is a schematic diagram of a target visualization model of a target vehicle provided in this application;

[0048] Figure 8 is a flowchart illustrating the visualization processing method for the vehicle model provided in this application.

[0049] Figure 9 is a schematic diagram of a target visualization model of a target vehicle provided in this application;

[0050] Figure 10 is a flowchart illustrating the visualization processing method for the vehicle model provided in this application.

[0051] Figure 11 is a schematic diagram of the framework of the vehicle model visualization processing method provided in this application;

[0052] Figure 12 is a schematic diagram of the structure of a first embodiment of the vehicle model visualization processing device provided in this application;

[0053] Figure 13 is a structural schematic diagram of the in-vehicle large screen provided in this application.

[0054] 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

[0055] 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.

[0056] With the rapid development of autonomous driving technology, users mainly rely on in-vehicle screens to obtain information about the driving environment around the vehicle.

[0057] Currently, in-vehicle screens typically use preset model display styles to present information about the vehicle model and its surrounding environment. Specifically, in-vehicle screens rely on a preset graphical interface to display core information about the vehicle and its surrounding environment in a concise and clear manner. This display method is based on two-dimensional or three-dimensional graphical models, combined with sensor data, to present information to the user in an intuitive way.

[0058] However, while existing in-vehicle large-screen display solutions can provide basic environmental information, their human-machine interaction functions are relatively limited during autonomous driving, making it difficult to offer a personalized driving experience. Specifically, a fixed display style cannot adapt to the preferences and needs of different users, resulting in a limited user experience. Furthermore, the fixed display style lacks interactive and engaging interface design, failing to effectively enhance user engagement and satisfaction. Therefore, how to achieve a more personalized and interactive display mode on in-vehicle large screens has become a pressing issue in the field of autonomous driving technology.

[0059] To address the aforementioned issues, during their research on vehicle model visualization methods, the inventors discovered that existing technical solutions typically use fixed vehicle models for visualization, lacking human-computer interaction and failing to provide users with a personalized driving experience. Therefore, the inventors considered whether it was possible to personalize the vehicle display on the in-vehicle screen based on its driving characteristics. Specifically, in addition to providing a standard display mode, the in-vehicle screen could offer a fun display mode. Furthermore, when the in-vehicle screen is in fun mode, if a target vehicle is identified within a preset range and its driving characteristics match the preset conditions for fun display, a target visual model of the target vehicle is determined and displayed on the in-vehicle screen, thus providing interactive enjoyment and a personalized driving experience.

[0060] Figure 1 is a schematic diagram of a scenario for the vehicle model visualization processing method provided in this application. As shown in Figure 1, the application scenario of the vehicle model visualization processing method provided in this application is a human-machine co-driving scenario. In this human-machine co-driving scenario, a vehicle 100 is included. Among them, an automatic driving control system 101 and an in-vehicle large screen 102 are deployed in the vehicle 100. It is worth noting that although only one vehicle 100 is shown in Figure 1, it should be understood that there can be two or more vehicles 100.

[0061] When the display style of the in-vehicle screen 102 is in the fun display mode, the autonomous driving control system 101 will detect whether there is a target vehicle within the preset range of the vehicle 100. If there is a target vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display, the autonomous driving control system 101 will determine the target visualization model of the target vehicle and display the target vehicle in the in-vehicle screen 102 with the target visualization model.

[0062] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario. In the specific application of the solution, it can be set according to actual needs.

[0063] It should be noted that the terms "user" and "driver" are used interchangeably in this article.

[0064] For ease of description, the following section uses the autonomous driving control system as an example to introduce the implementation method of the vehicle model visualization processing method. It should be understood that using the autonomous driving control system as the executing entity is merely an illustrative example and should not be construed as a limitation of the method.

[0065] 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 be described below with reference to the accompanying drawings.

[0066] Figure 2 is a flowchart illustrating the vehicle model visualization processing method provided in this application. As shown in Figure 2, the process of this vehicle model visualization processing method may include:

[0067] S201: When the display style of the vehicle's in-vehicle screen is set to the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display, then the target visualization model of the target vehicle is determined.

[0068] In this step, in order to achieve a personalized driving experience and provide emotional value to users during vehicle operation, this application provides users with a fun display mode for vehicle models, based on the existing conventional display mode display style.

[0069] It is worth noting that the display style of the vehicle's in-vehicle screen can be set according to user preferences. In one possible implementation, the display style of the vehicle's in-vehicle screen includes, but is not limited to, regular display, fun display, and customized display, providing users with diverse and engaging visual display styles. This display style can be set as an optional switch. While the vehicle is in motion or parked on the roadside, the vehicle responds to the user's display style selection operation, controlling the in-vehicle screen to display the mode corresponding to the user's chosen style. The in-vehicle screen is used to display the vehicle, obstacles, navigation, lane lines, surrounding environment, etc., providing users with visualized vehicle driving information. The in-vehicle screen is the central control screen in the vehicle. It is important to note that if the user wants to select the fun display mode, and the in-vehicle screen's display style was already in fun display mode during the previous drive, the user does not need to make a selection. If the user wants to use the regular visual model, they can make the corresponding selection, and the vehicle responds to the user's display style selection operation, controlling the in-vehicle screen to display the mode corresponding to the user's chosen style.

[0070] When the vehicle's in-vehicle screen is set to a "fun display mode," the perception module in the vehicle's autonomous driving control system detects and assesses the surrounding environment, particularly whether a target vehicle exists within a preset range. Specifically, the autonomous driving control system collects and analyzes data from the surrounding environment to identify the presence and driving characteristics of other vehicles, thereby determining whether to trigger the personalized display function of the fun display mode. This process not only enhances the interactivity of driving but also provides users with a richer visual experience. The preset range is determined based on the detectable range of various sensing devices in the autonomous driving control system, and this application does not impose specific limitations on it.

[0071] The perception module in the autonomous driving control system detects and calculates the road and road participants (people and vehicles) based on sensors such as on-board cameras, millimeter-wave radar, and lidar. The system can calculate information such as the position, lateral and longitudinal velocity, and lateral and longitudinal acceleration of each road participant around the vehicle, which is used to provide information to the autonomous driving system for automated control decisions.

[0072] In one possible implementation, the driving characteristics identified by the autonomous driving control system include, but are not limited to, the lateral speed, longitudinal speed, lateral acceleration, longitudinal acceleration, driving position, and lane information of other vehicles.

[0073] If a target vehicle is determined to exist within the preset range of the vehicle, then it is further determined whether the driving characteristics of the target vehicle meet the preset conditions for the fun display.

[0074] If the driving characteristics of the target vehicle meet the preset conditions for fun display, then the target visualization model of the target vehicle is further determined.

[0075] Correspondingly, if the driving characteristics of the target vehicle do not meet the preset conditions for the fun display, the target vehicle will be displayed on the vehicle's in-vehicle screen as a regular visualization model.

[0076] In one possible implementation, the preset conditions for the display include, but are not limited to, any one or any combination of road speed limits, speed thresholds, traffic flow speeds, lane changes, relative distances, and direction changes. Specifically, a road speed limit refers to whether the target vehicle's speed is within the speed limit specified for its lane. A speed threshold refers to whether the target vehicle's speed is within a specific range in high-speed or low-speed driving scenarios. Traffic flow speed refers to whether the target vehicle's speed is within the average speed range of the current traffic flow. Lane changes refer to whether the target vehicle is changing lanes or making other significant lateral movements. Relative distance refers to whether the distance between the target vehicle and the vehicle itself is within a specific range to ensure display relevance and safety. Direction changes refer to whether the target vehicle's direction of travel has changed significantly, such as turning or making a U-turn.

[0077] In one possible implementation, the method for determining the target visual model of the target vehicle based on the preset conditions of the fun display includes, but is not limited to, any one or any combination of dynamic analysis based on driving characteristics, context awareness, user preference settings, etc.

[0078] Specifically, dynamic analysis based on driving characteristics refers to dynamically generating a visual model based on the specific driving characteristics of the target vehicle, such as changes in speed, acceleration, and direction. For example, if the target vehicle is accelerating rapidly, a streamlined animation effect can be selected to represent this acceleration state.

[0079] Contextual awareness refers to selecting the appropriate visualization model based on the current driving situation. For example, in a traffic jam, the target vehicle can be displayed as a slowly moving icon, while on a highway it can be displayed as a fast-moving arrow.

[0080] User preference settings refer to the ability for users to pre-set their own preferences and choose different visualization styles. The vehicle then determines the display method based on these preferences, such as choosing a cartoon style, a futuristic style, or a minimalist style.

[0081] The specific types of target visualization models include, but are not limited to, different animal images and different object images; this application does not impose specific limitations on these. By using vivid visualization models to suggest the driving behavior of the target vehicle, it helps provide emotional value to the driver, using fun to alleviate the frustration caused by bad road conditions, while simultaneously promoting compliant driving values ​​and safe driving.

[0082] It is worth noting that the number of target vehicles meeting the preset conditions for the fun display can be 0, 1, or more; this application does not impose a specific limitation on this. Furthermore, regardless of whether the vehicle is in motion, if the driver is inside the vehicle and selects the fun display mode for the in-vehicle screen, the in-vehicle screen will display vehicle models within the preset range using the corresponding models.

[0083] S202: Display the target vehicle using a target visualization model on the in-vehicle large screen.

[0084] In this step, once the target visualization model is determined based on step S201, the target vehicle is displayed on the in-vehicle screen using the target visualization model. It is worth noting that if multiple target visualization models exist at the same time, all of these target visualization models will be displayed on the in-vehicle screen.

[0085] In one possible implementation, when the target vehicle is displayed as a target visualization model on the in-vehicle screen, relevant information, such as the target vehicle's speed, distance from the vehicle, and direction of travel, can be overlaid on the target visualization model in the form of text or icons to help users obtain key information.

[0086] It is worth noting that there is a certain time limit for displaying the target vehicle as a target visualization model on the in-vehicle large screen. The specific implementation method can be referred to in step S1001 below, which will not be repeated here.

[0087] It is worth noting that vehicles that are not the target vehicle within the preset range or that do not meet the preset conditions for the fun display are displayed on the vehicle's in-vehicle screen as regular visualization models.

[0088] The vehicle model visualization processing method provided in this application offers users a personalized driving experience through a fun display mode. Specifically, the autonomous driving control system detects target vehicles within a preset range to ensure real-time perception of the surrounding environment, thereby improving driving safety. Simultaneously, the autonomous driving system filters out vehicles that meet user needs by determining whether the target vehicle's driving characteristics match preset conditions for the fun display, effectively avoiding information overload. Furthermore, eligible target vehicles are presented as visual models on the in-vehicle screen, enhancing the enjoyment of human-computer interaction and providing users with a unique and personalized driving experience.

[0089] In one possible implementation, the process of determining the target visualization model of the target vehicle includes:

[0090] The visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model; wherein, the visualization model material library includes a variety of driving characteristics and the correspondence between at least one driving characteristic and the visualization model.

[0091] Specifically, when the autonomous driving system determines that the driving characteristics of the target vehicle meet the preset conditions for the fun display, the next step is to determine the target visualization model of the target vehicle.

[0092] The target visual model of the target vehicle can be determined through a pre-set visual model material library.

[0093] The visualization model library is a database containing various driving features and their corresponding visualization models. These driving features include, but are not limited to, the target vehicle's speed, acceleration, steering angle, and other dynamic behavioral characteristics. Notably, each driving feature is associated with one or more visualization models to visually reflect the vehicle's behavior.

[0094] When an autonomous driving control system detects a target vehicle, it analyzes the vehicle's current driving characteristics. For example, if the system determines the vehicle is in an aggressive driving state based on its characteristics, it will search its visualization library for a model corresponding to these characteristics, such as a model with strong color contrast and dynamic effects to represent high-speed driving. Conversely, if the system determines the vehicle is in a stable driving state, it will search its library for a model corresponding to these characteristics, such as a model with soft colors and rounded shapes to represent smooth and safe driving.

[0095] It is worth noting that the visualization models in the visualization model library are not limited to the representation of a single feature; some visualization models are combinations of multiple features. For example, a vehicle traveling at high speed on a highway while maintaining high stability will trigger a visualization model designed for a combination of multiple features.

[0096] Optionally, in one possible implementation, when the driving day falls on a traditional holiday, the autonomous driving system can select a target visualization model from a visualization model library that matches the holiday theme to enhance the festive atmosphere and improve human-machine interaction. This approach not only provides the driver with a more personalized and enjoyable driving experience but also adds to the festive atmosphere during the driving process.

[0097] For example, during the Dragon Boat Festival, the target visualization model can choose the image of a dragon boat, symbolizing the tradition and vitality of the festival, reminding the driver of its arrival and adding to the festive atmosphere. Similarly, during the Spring Festival, the autonomous driving system can choose a snowman or other images related to winter festivals to create a warm and joyful atmosphere. These festival-themed visualization models not only enhance the driver's enjoyment but also maintain the driver's attention and interest in the interface through visual changes.

[0098] Furthermore, through festival-related visualization models, autonomous driving systems can also promote cultural heritage and the dissemination of traditional customs, allowing drivers to experience the cultural connotations of festivals during their daily commutes. This approach not only enhances the fun and interactivity of human-computer interaction but also provides drivers with a unique driving experience, allowing them to feel the festive atmosphere and warmth while driving.

[0099] By using holiday-themed visualizations, autonomous driving systems can enhance driving safety while adding enjoyment and cultural value to the driving experience. This flexible visualization strategy provides users with a creative and personalized driving environment, further enhancing the system's user-friendliness and appeal.

[0100] Optionally, in one possible implementation, the target vehicle's visual model can be dynamically generated based on its driving characteristics, rather than being limited to a pre-set model library. By analyzing the target vehicle's behavior and environmental data in real time, the autonomous driving system can generate a visual model that highly matches the current driving situation. This dynamically generated method provides real-time adaptability, enabling the driver to more accurately understand and predict the target vehicle's behavior, thereby improving driving safety. Furthermore, personalized experiences and environmental perception capabilities enhance driving pleasure and the comprehensiveness of information. By utilizing advanced graphics technologies and algorithms, the autonomous driving system can generate unique models, enhancing interactivity and flexibility, and providing drivers with more precise and personalized support and guidance.

[0101] The vehicle model visualization method provided in this embodiment determines the target visualization model of the target vehicle by utilizing a preset visualization model material library, enabling users to quickly identify the dynamic characteristics of surrounding vehicles through intuitive visual elements. This method not only improves driving safety and efficiency but also provides users with a personalized driving experience, increasing driving pleasure and interactivity. Simultaneously, through this personalized visual presentation, users can better understand and adapt to complex traffic environments, thereby enjoying a more pleasant and safer driving journey.

[0102] In one possible implementation, the target vehicle includes a vehicle possessing at least one of the following driving characteristics:

[0103] Vehicles traveling directly in front of the vehicle in its lane;

[0104] Vehicles traveling on the left side of the road adjacent to and / or in front of the vehicle;

[0105] Vehicles that change lanes from in front of the vehicle and enter the lane where the vehicle is located;

[0106] Vehicles that violate regulations by driving within the preset area;

[0107] Vehicles of a preset type within a preset range.

[0108] Specifically, the perception module in the autonomous driving control system senses the driving characteristics of other vehicles within a preset range in real time to determine whether they are the target vehicle. This will be discussed in detail below.

[0109] Specifically, if a vehicle is traveling directly in front of the driver in its lane, that vehicle is identified as the target vehicle. Identifying vehicles with this driving characteristic is crucial for maintaining a safe following distance. The autonomous driving system needs to continuously monitor the speed, acceleration, and braking behavior of these vehicles to adjust its own speed as needed to avoid rear-end collisions. For example, if the vehicle in front suddenly decelerates, the autonomous driving control system can warn the driver in advance or automatically adjust its speed.

[0110] If a vehicle is identified as a target vehicle if it is traveling in the lane to the left of the vehicle, adjacent to and / or ahead of it. Specifically, these vehicles are located in the adjacent lane to the left of the vehicle, possibly traveling parallel to or slightly ahead of it. Identifying these vehicles is crucial for safe lane changing and overtaking maneuvers. The autonomous driving control system needs to determine the speed and position of these vehicles to ensure a collision is avoided during lane changes. For example, on a highway, when a driver may need to overtake, the autonomous driving control system provides real-time information about vehicles in the left lane to help the driver make a safe lane-changing decision.

[0111] If a vehicle's driving characteristics indicate that it is changing lanes from in front of the driver's lane into the driver's lane, that vehicle is identified as the target vehicle. Specifically, these vehicles change lanes from other lanes into the driver's lane, typically occurring in urban traffic or at highway entrances. The automated driving control system needs to quickly identify the intentions and lane-changing behavior of these vehicles to adjust its own speed and following distance, avoiding traffic accidents caused by sudden lane changes.

[0112] If a vehicle is found to be driving illegally within a preset range, that vehicle will be identified as the target vehicle. Specifically, these vehicles are identified as violating regulations within a preset safety range, such as speeding, driving against traffic, changing lanes improperly, or running red lights.

[0113] If a vehicle's driving characteristics fall within a preset range and are classified as a target vehicle, that vehicle is identified. Specifically, these vehicles include specific types such as emergency vehicles (ambulances, fire trucks), large vehicles (trucks, buses), or other vehicle types requiring special attention. Identifying these vehicles helps drivers make yielding decisions or adjust their driving strategies when necessary.

[0114] It is worth noting that the driving characteristics of the target vehicle can be any one of the above driving characteristics, or any combination of the above driving characteristics. For example, if a vehicle has a driving characteristic of a preset type within a preset range and is driving directly in front of the vehicle, then that vehicle is identified as the target vehicle. If a vehicle has a driving characteristic of a preset type within a preset range and is driving adjacent to and / or in front of the vehicle on the left side of the road, then that vehicle is identified as the target vehicle. Therefore, this application does not impose specific limitations in this regard.

[0115] By identifying and processing these target vehicles, the autonomous driving control system can significantly improve driving safety and efficiency, helping drivers make more informed decisions in complex traffic environments.

[0116] The vehicle model visualization processing method provided in this embodiment mainly illustrates the method for determining the target vehicle. By analyzing the vehicle's driving characteristics, the autonomous driving control system can accurately identify and judge target vehicles within a preset range. This method not only improves the accuracy of target vehicle identification but also enhances the adaptability of the autonomous driving control system in complex traffic environments.

[0117] Next, we will discuss in detail how to determine target visualization models of target vehicles with different driving characteristics from a visualization model material library. Figure 3 is a schematic flowchart of the vehicle model visualization processing method provided in this application. As shown in Figure 3, based on any of the above embodiments, the process of the vehicle model visualization processing method may include:

[0118] S301: If the target vehicle is a vehicle that illegally changed lanes from in front of the vehicle to enter the lane where the vehicle is located, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model.

[0119] In this step, the target vehicle is characterized by driving illegally within a preset range and changing lanes from in front of itself into its own lane.

[0120] Specifically, the target vehicle is not in the same lane as your vehicle, and the target vehicle illegally changes lanes from in front of your vehicle into your vehicle's lane.

[0121] In one possible implementation, whether a target vehicle violates lane rules when changing lanes is determined jointly based on the lane markings and the estimated collision time between the target and other vehicles. Specifically, if the target vehicle is traveling adjacent to or in front of the vehicle on its left before changing lanes, and the lane marking between the target and other vehicles is a solid line, and the estimated collision time between the target and other vehicles is less than or equal to a preset time length when the target vehicle performs the lane change, it is considered a lane change violation. Similarly, if the target vehicle is traveling adjacent to or in front of the vehicle on its right before changing lanes, and the lane marking between the target and other vehicles is a solid line, and the estimated collision time between the target and other vehicles is less than or equal to a preset time length when the target vehicle performs the lane change, it is also considered a lane change violation. Preferably, the preset time length is 1.2 seconds.

[0122] It is worth noting that the estimated collision time between the target vehicle and the vehicle itself is determined based on the ratio of the shortest distance between them to the vehicle's own speed. The shortest distance is calculated based on the characteristic positions of the target vehicle and the vehicle itself.

[0123] When the target vehicle is determined to have two driving characteristics, namely illegal driving and lane change-in, the visualization model corresponding to these two driving characteristics is obtained from the visualization model material library and used as the target visualization model.

[0124] Figure 4 is a schematic diagram of a target vehicle illegally changing lanes, as provided in this application. As shown in Figure 4, the lane line between the target vehicle and the vehicle itself is a solid line, and the target vehicle is illegally changing lanes from in front of the vehicle to enter the lane where the vehicle itself is located.

[0125] Figure 5 is a schematic diagram of a target visualization model of a target vehicle provided in this application. As shown in Figure 5, the target vehicle has the driving characteristics of illegal driving and lane changing ahead. Based on the above driving characteristics, the target visualization model determined from the visualization model material library is a crab model.

[0126] The vehicle model visualization method provided in this embodiment can generate corresponding target visualization models when the target vehicle exhibits two driving characteristics: illegal driving and lane-changing entry. This effectively helps drivers alleviate their emotions. Specifically, when a target vehicle illegally changes lanes, by generating a corresponding visualization model, the driver can not only intuitively understand the violation but also shift their attention from anger to dealing with the traffic situation. This visual feedback not only enhances safety awareness but also subtly reminds the driver to remain calm and avoid letting anger affect driving decisions. Furthermore, the vivid visualization effect helps drivers face complex traffic with a more positive attitude, reducing anxiety and improving the overall driving experience. Simultaneously, this visualization model optimizes the human-computer interaction experience, presenting information intuitively so that drivers can quickly understand the surrounding traffic conditions, improving the friendliness and responsiveness of the interaction.

[0127] Figure 6 is a schematic flowchart of the vehicle model visualization processing method provided in this application. As shown in Figure 6, based on any of the above embodiments, the process of the vehicle model visualization processing method may include:

[0128] S601: If the target vehicle is a vehicle traveling directly in front of the vehicle in its lane, and the average speed and maximum speed of the target vehicle both conform to the preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model.

[0129] In this step, the target vehicle is characterized by slow speed and traveling directly in front of its own lane.

[0130] Specifically, the target vehicle is traveling directly in front of the vehicle in its lane, and the target vehicle's speed is less than the preset lane speed.

[0131] In one possible implementation, whether the average speed and maximum speed of the target vehicle both conform to a preset speed range can be determined using the following calculation formula:

[0132] in, V represents the average speed of the target vehicle over a first preset time period. average This represents the average speed of all other vehicles within a preset range of the vehicle itself over a first preset time period. α represents a first preset calibration parameter, and β represents a second preset calibration parameter, where the first preset calibration parameter is less than the second preset calibration parameter. This represents the maximum speed of the target vehicle within a first preset time period. Preferably, α = 0.7, β = 0.75, and the first preset time period is 5 seconds.

[0133] It's important to note that α and β are primarily adjusted based on user experience to ensure the frequency of the target visualization model's appearance meets user satisfaction. If the frequency is too high, it can lead to information overload, causing confusion and anxiety for the driver, thus affecting attention and reaction time. Conversely, if the frequency is too low, the driver may miss important traffic information, increasing potential safety risks. Therefore, a proper balance between these two parameters is crucial for improving the driving experience and ensuring road safety. Precise adjustments enable effective information delivery, helping drivers remain calm and focused in complex traffic environments.

[0134] If the average speed and maximum speed of the target vehicle in the first preset time length satisfy the above calculation formula, then the target vehicle has the driving characteristic of slow driving.

[0135] Furthermore, if the target vehicle is in the same lane as the vehicle itself, and the target vehicle is traveling directly in front of the vehicle itself, then the target vehicle also has the characteristic of traveling directly in front of the vehicle itself.

[0136] Based on the two driving characteristics of slow driving and driving directly in front of the vehicle in its lane, the visualization models corresponding to these two characteristics are obtained from the visualization model material library and used as the target visualization models.

[0137] Figure 7 is a schematic diagram of a target visualization model of a target vehicle provided in this application. As shown in Figure 7, the target vehicle has the driving characteristics of slow speed and driving directly in front of the vehicle in its lane. Based on the above driving characteristics, the target visualization model determined from the visualization model material library is a golden turtle model.

[0138] The vehicle model visualization method provided in this embodiment can generate corresponding target visualization models when the target vehicle exhibits two driving characteristics: slow speed and driving directly in front of the vehicle in its lane. This significantly improves safety by providing real-time displays of traffic conditions ahead, helping drivers identify potential problems in advance and take timely measures. Furthermore, clear visual feedback effectively manages driver emotions, reduces anxiety and anger, improves driving decisions, and enhances the overall driving experience, thereby increasing driving satisfaction and comfort.

[0139] Figure 8 is a flowchart illustrating the vehicle model visualization processing method provided in this application. As shown in Figure 8, based on any of the above embodiments, the process of the vehicle model visualization processing method may include:

[0140] S801: If the target vehicle is a vehicle traveling on the left side of the road that is adjacent to and / or in front of the vehicle, and the average speed and maximum speed of the target vehicle both conform to the preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model.

[0141] In this step, the target vehicle is characterized by slow speed and is traveling adjacent to and / or in front of its own vehicle in the road to the left.

[0142] Specifically, the target vehicle is traveling in the road to the left of the vehicle, adjacent to and / or in front of it, and the average speed and maximum speed of the target vehicle both conform to the preset speed range.

[0143] It is worth noting that whether the average speed and maximum speed of the target vehicle both conform to the preset speed range can be referred to step S601, and will not be repeated here.

[0144] If the average speed and maximum speed of the target vehicle both fall within the preset speed range, then the target vehicle is characterized as slow driving.

[0145] Furthermore, if the target vehicle is located in the lane to the left of the lane where the vehicle is located, and the target vehicle is positioned adjacent to and / or in front of the vehicle in the left lane, then the target vehicle also has the characteristic of driving adjacent to and / or in front of the vehicle in the left lane.

[0146] Based on the two driving characteristics of slow driving and driving adjacent to and / or ahead of the vehicle in the left-hand road, the visualization model corresponding to the two driving characteristics of slow driving and driving adjacent to and / or ahead of the vehicle in the left-hand road is obtained from the visualization model material library and used as the target visualization model.

[0147] In one possible implementation, when determining the target visualization model from the visualization model library based on two driving characteristics—slow speed and proximity and / or forward movement in the left-hand lane—the vehicle type of the target vehicle can also be taken into account. For example, if the target vehicle is a sedan, the target visualization model can be a tortoise model. If the target vehicle is a large truck, the target visualization model can be a snail model. This application does not specifically limit the specific type of the target visualization model.

[0148] Figure 9 is a schematic diagram of a target visualization model of a target vehicle provided in this application. As shown in Figure 9, the target vehicle has the driving characteristics of slow speed and driving in front of the left side of the road. Based on the above driving characteristics, the target visualization model determined from the visualization model material library is a turtle model.

[0149] The vehicle model visualization method provided in this embodiment can generate a corresponding target visualization model when the target vehicle is moving slowly and is located adjacent to and / or in front of it on the left side of the road. By transforming the driving characteristics of the target vehicle into an intuitive visualization model, this method makes it easier for drivers to understand and predict the behavior of surrounding vehicles, thereby effectively reducing anxiety and unease caused by traffic conditions. Furthermore, the vivid and engaging visualization not only increases the fun and interactivity of the driving process but also further enhances the personalization and enjoyment of the driving experience.

[0150] Optionally, in one possible implementation, if a target vehicle accelerates past the vehicle in the adjacent lane, the target vehicle's driving characteristics are high speed and proximity and / or front in the left lane of the vehicle. In this case, a visualization model corresponding to the target vehicle's driving characteristics can be determined from a visualization model library as the target visualization model. For example, the target visualization model could be a rocket model.

[0151] Figure 10 is a flowchart illustrating the vehicle model visualization processing method provided in this application. As shown in Figure 10, based on any of the above embodiments, the vehicle model visualization processing method further includes:

[0152] S1001: When the driving characteristics of the target vehicle do not meet the preset conditions for fun display, the target vehicle is displayed on the in-vehicle screen as a regular visualization model; and / or, when the duration of the target visualization model displayed on the in-vehicle screen reaches the preset time length, the target vehicle is displayed on the in-vehicle screen as a regular visualization model.

[0153] In this step, to further enhance the user's personalized driving experience, there are certain limitations on the display time of the target visualization model.

[0154] Specifically, the display time of the target visualization model is subject to conditions including but not limited to the following three. These will be explained in detail below.

[0155] The first scenario occurs when the target vehicle's driving characteristics do not meet the preset conditions for engaging displays. In this case, the autonomous driving system will choose to use a conventional visualization model. Engaging displays are typically used in specific driving situations, such as when the target vehicle is traveling slowly or in a specific lane, to enhance the driver's attention and driving experience. However, under normal circumstances, using a conventional display avoids an overly complex interface, thereby reducing the driver's cognitive burden. This approach ensures clarity and consistency in information delivery, allowing the driver to focus on the driving task without distractions.

[0156] The second scenario involves the autonomous driving system automatically switching back to the regular visualization model when the target visualization model has been displayed on the in-vehicle screen for a preset duration. This strategy aims to prevent drivers from experiencing visual fatigue or habitually ignoring the engaging display. Prolonged display of the same engaging model can lead to a loss of novelty and appeal; therefore, by setting a reasonable display duration, the autonomous driving system can help maintain driver focus and ensure the interface's simplicity and effectiveness.

[0157] The third scenario occurs when either the target vehicle's driving characteristics do not meet the preset engaging display conditions, or the target visualization model is displayed on the in-vehicle screen for a preset duration. In either of these conditions, the autonomous driving system automatically switches back to the regular visualization model. This strategy provides dual protection, ensuring the driver's attention remains at an optimal level. First, when driving characteristics are no longer suitable for engaging display, switching back to regular display avoids unnecessary visual interference, ensuring the accuracy and simplicity of information delivery. Second, when engaging display time is too long, switching back to regular display prevents driver visual fatigue or habitual neglect, thus maintaining the interface's freshness and effectiveness. By combining these two conditions, the autonomous driving system can dynamically adjust its display strategy in different driving scenarios, providing a personalized and engaging experience while ensuring driving safety and information clarity.

[0158] Optionally, in one possible implementation, if the target vehicle moves out of the vehicle's preset range, the autonomous driving system will automatically switch back to the conventional visual model. By reducing unnecessary visual clutter and keeping the interface simple, the cognitive burden on the driver is reduced.

[0159] It is worth noting that the conditional restrictions corresponding to the display time of the target visualization model can be used individually or in any combination, and the specifics can be determined according to the actual situation. This application does not impose any specific restrictions on this.

[0160] The vehicle model visualization method provided in this embodiment uses a dynamically adjusted display strategy to enhance the driving experience while also ensuring driving safety and information clarity. By using engaging displays in appropriate contexts and reverting to regular displays when necessary, it provides users with a personalized driving experience while avoiding potential distractions and information overload.

[0161] Figure 11 is a schematic diagram of the visualization processing method for the vehicle model provided in this application. As shown in Figure 11, the autonomous driving control system of the vehicle detects environmental information within a preset range in real time, thereby obtaining the surrounding environment detection results. The surrounding environment detection results include the driving information and lane line information of all vehicles within the preset range. The driving information of the vehicles includes, but is not limited to, vehicle speed, lateral acceleration, longitudinal acceleration, and position. The lane line information includes, but is not limited to, solid lines, dashed lines, and lane line positions. Based on the surrounding environment detection results, the in-vehicle display system provides two modes: regular display and interactive display, to showcase the vehicle model. The vehicle displayed in interactive display mode meets the preset conditions for interactive display. It is worth noting that when the in-vehicle display system deployed in the vehicle visualizes the surrounding vehicles on the in-vehicle screen, the display effect is consistent with the relative positional relationship between the surrounding vehicles and the vehicle in the actual scene. Furthermore, during the vehicle's driving process, if it is determined that a target vehicle among the surrounding vehicles meets the preset conditions for interactive display, the corresponding vehicle model is displayed in an interactive manner. This visualization method focuses on non-compliant driving behaviors such as slow vehicles occupying the fast lane and cutting in line, providing emotional value and diverting road rage from users through engaging and interactive displays. Simultaneously, it promotes compliant driving values, safe driving, and guides users towards correct driving values. Optionally, in one possible implementation, environmental information surrounding the vehicle, such as obstacles and pedestrians, can also be displayed in an engaging manner.

[0162] Figure 12 is a schematic diagram of the structure of a vehicle model visualization processing device according to a first embodiment of the present application. As shown in Figure 12, the vehicle model visualization processing device 1200 includes:

[0163] The processing module 1201 is used to determine the target visualization model of the target vehicle when the display style of the vehicle's in-vehicle screen is in the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display.

[0164] The display module 1202 is used to display the target vehicle as a target visualization model on the in-vehicle large screen.

[0165] Optionally, the processing module 1201 is also used for:

[0166] The visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model; wherein, the visualization model material library includes a variety of driving characteristics and the correspondence between at least one driving characteristic and the visualization model.

[0167] Optionally, the target vehicle includes a vehicle that has at least one of the following driving characteristics:

[0168] Vehicles traveling directly in front of the vehicle in its lane;

[0169] Vehicles traveling on the left side of the road adjacent to and / or in front of the vehicle;

[0170] Vehicles that change lanes from in front of the vehicle and enter the lane where the vehicle is located;

[0171] Vehicles that violate regulations by driving within the preset area;

[0172] Vehicles of a preset type within a preset range.

[0173] Optionally, the processing module 1201 is also used for:

[0174] If the target vehicle is one that illegally changed lanes from in front of the target vehicle into its lane, then a visual model corresponding to the target vehicle's driving characteristics is determined from the visual model material library and used as the target visual model, including:

[0175] Obtain visual models corresponding to the two driving characteristics of illegal driving and lane change from the visualization model material library, and use them as target visual models.

[0176] Optionally, the processing module 1201 is also used for:

[0177] If the target vehicle is traveling directly in front of the vehicle in its lane, and both its average and maximum speeds fall within a preset speed range, then a visualization model corresponding to the target vehicle's driving characteristics is determined from the visualization model library and used as the target visualization model. This model includes:

[0178] From the visualization model material library, obtain visualization models corresponding to two driving characteristics: slow driving and driving directly in front of the vehicle in its lane, and use them as target visualization models.

[0179] Optionally, the processing module 1201 is also used for:

[0180] If the target vehicle is a vehicle traveling on the left side of the road adjacent to and / or ahead of the vehicle, and the average speed and maximum speed of the target vehicle both conform to a preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including:

[0181] From the visualization model material library, obtain visualization models corresponding to the two driving characteristics of slow driving and driving adjacent to and / or ahead of the vehicle in the left-hand road, and use them as target visualization models.

[0182] Optionally, the processing module 1201 is also used for:

[0183] When the driving characteristics of the target vehicle do not meet the preset conditions for the fun display, the target vehicle will be displayed on the in-vehicle screen as a regular visualization model; and / or, when the target visualization model is displayed on the in-vehicle screen for a preset duration, the target vehicle will be displayed on the in-vehicle screen as a regular visualization model.

[0184] The vehicle model visualization processing device provided in this application embodiment can be used to execute the vehicle model visualization processing method in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0185] Figure 13 is a schematic diagram of the structure of the in-vehicle large screen provided in this application. As shown in Figure 13, the in-vehicle large screen 102 provided in this embodiment includes: at least one transceiver 1301, a processor 1302, and a memory 1303. Optionally, the in-vehicle large screen 102 also includes a communication component. The processor 1302, the memory 1303, and the communication component are connected via a bus.

[0186] In the specific implementation process, at least one transceiver 1301 is used to transmit data with the autonomous driving control system, and at least one processor 1302 executes computer execution instructions stored in memory 1303, so that at least one processor 1302 executes the above-mentioned vehicle model visualization processing method.

[0187] The specific implementation process of processor 1302 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0188] 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.

[0189] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0190] 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.

[0191] This application also provides a vehicle, including a vehicle body and an in-vehicle large screen 102 as shown in FIG13.

[0192] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for visualizing a vehicle model.

[0193] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described vehicle model visualization processing method.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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 according to actual needs.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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 method for visualizing a vehicle model, characterized in that, The method includes: When the display style of the vehicle's in-vehicle screen is in the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display, then the target visualization model of the target vehicle is determined. The target vehicle is displayed on the in-vehicle large screen using the target visualization model.

2. The method according to claim 1, characterized in that, Determining the target visualization model of the target vehicle includes: A visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library and used as the target visualization model; The visualization model material library includes various driving features and the correspondence between at least one driving feature and a visualization model.

3. The method according to claim 1 or 2, characterized in that, The target vehicle includes vehicles possessing at least one of the following driving characteristics: Vehicles traveling directly in front of the vehicle in the lane in which the vehicle is located; Vehicles traveling on the left side of the road adjacent to and / or in front of the vehicle; Vehicles that change lanes from in front of the vehicle and enter the lane where the vehicle is located; Vehicles that violate the preset range; Vehicles of a preset type within the preset range.

4. The method according to claim 2 or 3, characterized in that, If the target vehicle is a vehicle that illegally changed lanes from in front of the target vehicle into the target vehicle's lane, then a visualization model corresponding to the target vehicle's driving characteristics is determined from the visualization model material library and used as the target visualization model, including: From the visualization model material library, obtain the visualization models corresponding to the two driving characteristics of illegal driving and lane change ahead, and use them as the target visualization models.

5. The method according to claim 2 or 3, characterized in that, If the target vehicle is a vehicle traveling directly in front of the vehicle in the lane where the target vehicle is located, and the average speed and maximum speed of the target vehicle both conform to a preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including: From the visualization model material library, obtain visualization models corresponding to the two driving characteristics of slow driving and driving directly in front of the vehicle in its lane, and use them as the target visualization models.

6. The method according to claim 2 or 3, characterized in that, If the target vehicle is a vehicle traveling on the left side of the road adjacent to and / or ahead of the vehicle, and the average speed and maximum speed of the target vehicle both conform to a preset speed range, then a visualization model corresponding to the driving characteristics of the target vehicle is determined from the visualization model material library as the target visualization model, including: From the visualization model material library, obtain visualization models corresponding to the two driving characteristics of slow driving and driving adjacent to and / or ahead of the vehicle on the left side of the road, and use them as the target visualization models.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: When the driving characteristics of the target vehicle do not meet the preset conditions for the fun display, the target vehicle will be displayed on the in-vehicle screen in a conventional visualization model. And / or, When the target visualization model is displayed on the in-vehicle screen for a preset duration, the target vehicle is then displayed on the in-vehicle screen using the conventional visualization model.

8. A visualization processing device for a vehicle model, characterized in that, include: The processing module is used to determine the target visualization model of the target vehicle when the display style of the vehicle's in-vehicle screen is in the fun display mode, if it is determined that there is a target vehicle within the preset range of the vehicle and the driving characteristics of the target vehicle meet the preset conditions of the fun display. The display module is used to display the target vehicle in the target visualization model on the in-vehicle large screen.

9. A vehicle-mounted large screen, characterized in that, It includes: a transceiver, a processor, and a memory communicatively connected to the processor; the memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the vehicle model visualization processing method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, include: The vehicle body and the in-vehicle large screen as described in claim 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the visualization processing method for the vehicle model as described in any one of claims 1 to 7.

12. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the visualization processing method for the vehicle model as described in any one of claims 1 to 7.

13. A chip, characterized in that, The chip includes a memory and a processor. The memory stores code and data and is coupled to the processor. The processor runs a program in the memory that enables the chip to perform the visualization processing method for a vehicle model as described in any one of claims 1 to 7.

14. A computer program, characterized in that, When the computer program is executed by a processor, it is used to perform the visualization processing method for the vehicle model as described in any one of claims 1 to 7.

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