Mobile platform accident processing method, computer device, storage medium, program product and mobile platform

Abstract

Disclosed in the present application are a mobile platform accident processing method, a computer device, a computer-readable storage medium, a computer program product and a mobile platform. The mobile platform accident processing method comprises: acquiring multi-angle sensing data collected by a mobile platform when an accident occurs; and, according to the multi-angle sensing data, generating multi-angle accident scene images. When an accident occurs, multi-angle sensing data collected by a mobile platform is acquired, and multi-angle accident scene images are generated on the basis of the multi-angle sensing data, so that the present application can present accident scenes from different angles, helping to analyze circumstances of accidents from different angles of view, thus achieving more accurate and objective distribution of liability for accidents.

Description

Mobile platform incident handling methods, computer equipment, storage media, software products, and mobile platforms Technical Field

[0001] This application relates to the field of vehicle driving technology, and in particular to a mobile platform accident handling method, computer equipment, computer-readable storage medium, computer program product, and mobile platform. Background Technology

[0002] In the current vehicle sales and maintenance process, car manufacturers or third-party parts suppliers often provide dashcams to customers. Dashcams record external information while the user is driving, helping the user to review the scene in the event of an accident and assisting traffic police in determining liability. However, the current common practice for dashcams is to install a monocular camera in the car to record video from a single angle, which cannot provide a more comprehensive view of the vehicle's surroundings. Summary of the Invention

[0003] This application provides a mobile platform accident handling method, a computer device, a computer-readable storage medium, a computer program product, and a mobile platform to at least solve one of the above-mentioned technical problems.

[0004] In a first aspect, embodiments of this application provide a mobile platform accident handling method, comprising: acquiring multi-angle sensing data collected by the mobile platform at the time of the accident; and generating multi-angle accident scene images based on the multi-angle sensing data.

[0005] In some embodiments, the multi-angle perception data includes: real-scene videos from multiple angles surrounding the mobile platform acquired by multiple video acquisition devices mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: synthesizing multi-angle accident scene videos from the real-scene videos from multiple angles; and / or, the multi-angle perception data includes: environmental perception data around the mobile platform acquired by multiple environmental perception sensors mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: generating simulated images based on the environmental perception data.

[0006] In some embodiments, the mobile platform is configured with a display interface; the method further includes: presenting the multi-angle real-scene video of the accident scene and the simulated image on the display interface.

[0007] In some embodiments, the method further includes: generating a local real-scene video of the accident scene corresponding to the direction in which the accident occurred and / or generating a local simulated image corresponding to the direction in which the accident occurred.

[0008] In some embodiments, the simulated imagery includes at least one of a first-view simulated imagery, a second-view simulated imagery, and a third-view simulated imagery; wherein, the first-view simulated imagery includes simulated imagery viewed from the mobile platform itself in the surrounding direction; the second-view simulated imagery includes simulated imagery viewed from other mobile platforms themselves in the surrounding direction; and the third-view simulated imagery includes simulated imagery viewed from the surrounding environment towards the mobile platform and the other mobile platforms.

[0009] In some embodiments, the mobile platform accident handling method further includes: acquiring the real-time driving data of the mobile platform itself; acquiring the real-time driving data of other mobile platforms around the mobile platform; and displaying the real-time driving data of the mobile platform and the real-time driving data of the external platforms in real time.

[0010] In some embodiments, the real-time driving data of this platform includes at least one of the following: real-time driving speed of this platform, real-time acceleration of this platform, and indicator light control data of this platform; the real-time driving data of the external platform includes at least one of the following: real-time driving speed of the external platform, real-time acceleration of the external platform, and indicator light control data of the external platform.

[0011] In some embodiments, acquiring multi-angle perception data collected by the mobile platform at the time of the accident includes: detecting an accident signal on the mobile platform; and when the accident signal is detected, acquiring multi-angle perception data collected by the mobile platform within a predetermined time period at the time of the accident.

[0012] In some embodiments, detecting a mobile platform accident signal includes detecting one or more of the following: autonomous driving fault detection data, active safety data, and acceleration data of the mobile platform.

[0013] In some embodiments, the mobile platform accident handling method further includes: obtaining the mobile platform's driving data, internal fault data, and the accident location before the accident to determine accident liability.

[0014] In some embodiments, obtaining driving data, internal fault data, and traffic regulations of the accident location from the mobile platform before the accident to determine accident liability includes: inputting the driving data, internal fault data, and accident location of the mobile platform into a general accident analysis model to obtain the accident liability determination result; or, determining an accident analysis model for the accident location based on the traffic regulations of the accident location; and inputting the driving data and internal fault data of the mobile platform into the accident analysis model for the accident location to obtain the accident liability determination result.

[0015] In some embodiments, the mobile platform incident handling method further includes automatically guiding the user to handle the incident after it occurs.

[0016] In some embodiments, after an accident occurs, the user is automatically guided to handle the accident, including: after the accident occurs, determining the accident level; determining the corresponding accident handling plan based on the determined accident level; and guiding the user to handle the accident according to the corresponding accident handling plan.

[0017] Secondly, embodiments of this application provide a computer device including a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of the method described in any of the foregoing embodiments. Thirdly, this application provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing embodiments. Fourthly, this application provides a computer program product including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in any of the foregoing embodiments. Fifthly, this application provides a mobile platform equipped with the computer device described in any of the foregoing embodiments.

[0018] The beneficial effects of this application embodiment are as follows: when an accident occurs, multi-angle perception data collected by the mobile platform is acquired, and multi-angle accident scene images are generated based on the multi-angle perception data, so that the accident scene can be presented from multiple angles, which helps to analyze the scene when the accident occurs from multiple perspectives and can more accurately and objectively divide the responsibility for the accident. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 is a flowchart of an embodiment of the mobile platform accident handling method of this application;

[0021] Figure 2 is a flowchart of another embodiment of the mobile platform accident handling method of this application;

[0022] Figure 3 is a flowchart of another embodiment of the mobile platform accident handling method of this application;

[0023] Figure 4 is a schematic diagram of the structure of an embodiment of the computer device of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0025] It should also be noted that, in this document, the terms "comprising" or "including" include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] This application provides a method for handling accidents on mobile platforms. This method is applied to mobile platforms, including but not limited to vehicles and robots (including but not limited to bipedal robots, quadrupedal robots, tracked robots, and wheeled robots). As traffic participants, vehicles and robots, using this mobile platform accident handling method, can reconstruct the accident scene from multiple angles when a traffic accident occurs, which helps in determining liability. It should be noted that the following embodiments all use vehicles as examples, but the method is also applicable to robots, and this application does not limit its application to them.

[0027] As shown in Figure 1, an embodiment of this application provides a mobile platform accident handling method, the method comprising:

[0028] S10. Acquire multi-angle perception data collected by the mobile platform at the time of the accident.

[0029] For example, the mobile platform is a vehicle. Accordingly, the method of this embodiment is a vehicle accident handling method, applicable to accident types including but not limited to single-vehicle accidents and multi-vehicle accidents. Single-vehicle accidents include, for example, accidents caused by vehicle malfunction, accidents involving collisions between the vehicle and a curb, accidents involving collisions between the vehicle and a median strip, and accidents involving collisions between the vehicle and a lamppost; multi-vehicle accidents include, for example, accidents involving collisions between the vehicle and other vehicles, and accidents involving collisions between the vehicle and pedestrians.

[0030] For example, the vehicle is equipped with multiple sensors at various angles. While the vehicle is in motion, these sensors collect multi-angle perception data in real time. When an accident occurs, the corresponding multi-angle perception data collected by the mobile platform can be obtained.

[0031] S20. Generate multi-angle accident scene images based on the multi-angle perception data.

[0032] In this embodiment, multi-angle perception data collected by the mobile platform is acquired when an accident occurs, and multi-angle accident scene images are generated based on the multi-angle perception data. This allows the accident scene to be presented from multiple angles, which helps to analyze the scene when the accident occurred from multiple perspectives and enables a more accurate and objective division of accident responsibility.

[0033] As shown in Figure 2, an embodiment of this application provides a mobile platform accident handling method. In this embodiment, multi-angle sensing data collected by the mobile platform at the time of the accident is acquired, including:

[0034] S11, Detect the signal of an accident occurring on the mobile platform.

[0035] For example, one or more of the following data are detected: autonomous driving fault detection data, active safety data, and acceleration data. These data can be obtained directly from the vehicle's autonomous driving system or from devices such as collision sensors and speed sensors; this application does not limit the specific data types used. At least one of these data types can be used as an accident occurrence signal; the more data types used, the more accurately the accident can be determined.

[0036] For example, autonomous driving fault detection data includes, but is not limited to, vehicle hardware fault data. For instance, vehicle hardware fault data includes, but is not limited to, vehicle radar fault data or vehicle surround-view camera fault data. The occurrence of vehicle hardware fault data may be due to a vehicle collision, thus causing the vehicle hardware fault data. Therefore, this vehicle hardware fault data can be used as an accident signal to determine that an accident has occurred (e.g., a rear-end collision, causing radar malfunction at the rear of the vehicle).

[0037] For example, active safety data includes, but is not limited to, emergency braking data. Before or after an accident (e.g., a vehicle rear-ending another vehicle or being rear-ended), emergency braking typically occurs, and the vehicle's systems can collect this emergency braking data. This active safety data can then be used as an accident signal to determine that an accident has occurred.

[0038] For example, acceleration data includes, but is not limited to, sudden increases or decreases in acceleration. Furthermore, if such a sudden increase or decrease exceeds the abrupt changes that occur during normal vehicle acceleration or deceleration, it can be determined as an abnormal situation. Therefore, this acceleration data can be used as an accident signal, indicating that an accident has occurred.

[0039] For example, when both autonomous driving fault detection data and active safety data are used simultaneously for accident detection signals, the occurrence of an accident can be determined more reliably. For instance, the autonomous driving fault detection data might be radar malfunction data from the front of the vehicle, and the active safety data might be emergency braking data. When both occur simultaneously, the following accident can be determined more reliably:

[0040] When the vehicle spotted the vehicle in front, it braked suddenly, but due to insufficient braking distance, it rear-ended the vehicle in front, damaging the vehicle's front radar and generating radar malfunction data.

[0041] For example, when both active safety data and acceleration data are used simultaneously for accident detection signals, the occurrence of an accident can be determined more reliably. For instance, active safety data might indicate emergency braking, while acceleration data might indicate increased acceleration. If both occur simultaneously, it can be more reliably determined that the vehicle was rear-ended, resulting in increased acceleration and subsequent emergency braking.

[0042] S12. When an accident signal is detected, acquire the multi-angle sensing data collected by the mobile platform within a predetermined time period when the accident occurs.

[0043] For example, when a vehicle accident occurs, the vehicle's fault detection system can detect the accident and obtain multi-angle sensing data within a preset time range from the already collected sensing data. The preset time range could be 1 minute before and 1 minute after the accident; this is just an example, and the preset time range can be set according to actual needs (e.g., 3 minutes, 5 minutes, etc.). In this embodiment, the multi-angle sensing data is the sensing data within the preset time range of the accident occurrence. Based on this multi-angle sensing data, multi-angle accident scene images are generated to reconstruct the specific situation before and after the accident, thereby facilitating the determination of accident responsibility.

[0044] In some embodiments, a plurality of video acquisition devices (e.g., dashcams or smart driving cameras) are installed on a mobile platform (e.g., a vehicle), and these devices are mounted around the mobile platform. The multi-angle perception data includes: real-world videos from multiple angles around the mobile platform acquired by the multiple video acquisition devices; generating multi-angle accident scene images based on the multi-angle perception data includes: synthesizing multi-angle accident scene real-world videos from the real-world videos at multiple angles. The vehicle may be equipped with a display interface that can display the multi-angle accident scene real-world videos.

[0045] For example, the multiple video capture devices include five cameras, one of which is mounted at the front of the vehicle, and the other four are positioned at the four corners of the vehicle. Optionally, adjacent cameras among the five cameras can cooperate to form a binocular camera system for calculating the distance to the subject.

[0046] For example, synthesizing a multi-angle real-scene video of an accident scene based on real-scene videos from multiple angles includes: aligning the timestamps of the real-scene videos from multiple angles for simultaneous synchronous display of the videos, wherein the videos from multiple angles can be displayed independently (e.g., five real-scene videos from five angles are displayed independently in five areas on the display screen, and the angle corresponding to each area is marked); or, aligning the timestamps of the real-scene videos from multiple angles for simultaneous synchronous display of the videos, and then stitching the aligned videos from multiple angles together to form a 3D surround real-scene video centered on the vehicle. Optionally, only the 3D surround real-scene video can be displayed on the vehicle, or both the real-scene videos from multiple angles and the 3D surround real-scene video can be displayed simultaneously; or, depending on the settings, some angles of real-scene video and the 3D surround real-scene video can be displayed simultaneously.

[0047] In some embodiments, a plurality of environmental perception sensors are mounted on a mobile platform (e.g., a vehicle), the plurality of environmental perception sensors being mounted around the mobile platform. Multi-angle perception data includes: environmental perception data of the area surrounding the mobile platform collected by the plurality of environmental perception sensors; generating multi-angle accident scene images based on the multi-angle perception data includes: generating simulated images based on the environmental perception data.

[0048] For example, multiple environmental perception sensors include multiple radar detection devices. These radar detection devices are installed around the vehicle to detect objects around it. The detected objects include their types (external vehicles, pedestrians, road edges, etc.), speeds, and relative distances. The system uses multiple radar detection devices to detect objects around the vehicle, creating depth map data and constructing a corresponding point cloud map. Object recognition is then performed on the point cloud map to identify external vehicles, pedestrians, road edges, etc. Object recognition on the point cloud map can employ a pre-trained neural network model or other methods; this application does not limit this approach. Furthermore, the detection of the speed and relative distance of external objects using radar detection devices can refer to related technologies such as radar speed measurement and radar ranging; this application does not limit this approach either.

[0049] For example, the environmental perception data includes the type, speed, and relative distance of objects around the vehicle. The vehicle may be equipped with a display interface that can present the simulated image. Generating the simulated image based on the environmental perception data includes: generating the simulated image based on the type, speed, and relative distance of objects around the vehicle. This simulated image is displayed on the vehicle's interactive screen as a SR (Situational Awareness) autonomous driving environment simulation display.

[0050] For example, generating simulated images based on the type, speed, and relative distance of objects surrounding the vehicle includes: generating a rendering image of the corresponding type of object based on the type of the objects surrounding the vehicle (e.g., generating a rendering image of a person when the object type is a person; generating a rendering image of a vehicle when the object type is a vehicle; generating a rendering image of a curb when the object type is a curb), and also generating a rendering image of the vehicle itself; then, based on the relative distance, rendering and distributing the vehicle itself and the objects surrounding it onto a global rendering image; and further, based on the determined speed of the objects surrounding the vehicle, labeling the corresponding objects in the rendering image with their speed information. In other embodiments, it also supports users switching or zooming the viewpoints of displayed real-world videos, 3D surround real-world videos, or simulated images from multiple angles.

[0051] In some embodiments, the multi-angle perception data includes: real-scene videos from multiple angles around the mobile platform acquired by multiple video acquisition devices and environmental perception data around the mobile platform acquired by multiple environmental perception sensors, wherein the multiple video acquisition devices and multiple environmental perception sensors are mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: generating multi-angle accident scene real-scene videos and simulated images based on the real-scene videos from multiple angles and the environmental perception data.

[0052] For example, multiple environmental perception sensors include multiple radar detection devices. Multiple video acquisition devices and multiple radar detection devices are installed around the vehicle to capture and detect objects around the vehicle. Specifically, multiple video acquisition devices can capture real-world videos of the vehicle's surroundings from multiple angles; multiple radar detection devices can detect the types of objects around the vehicle (external vehicles, pedestrians, road edges, etc.), their speeds, relative distances, and other detection results. Alternatively, image recognition (e.g., using a pre-trained neural network model) can be performed on images from the real-world videos captured by multiple video acquisition devices to determine the types of objects around the vehicle. Furthermore, two of the video acquisition devices can be configured as a binocular camera to estimate the relative distances to objects around the vehicle. Additionally, object tracking algorithms can be used to predict the speeds of objects around the vehicle.

[0053] In some embodiments, to improve the accuracy of the detection results of objects around the vehicle, the types, speeds, and relative distances of objects around the vehicle obtained from real-scene videos acquired by multiple video acquisition devices can be fused with the types, speeds, and relative distances of objects around the vehicle detected by radar detection devices to ultimately complete the detection results of objects around the vehicle. For example, the environmental perception data includes the types, speeds, and relative distances of objects around the vehicle.

[0054] In some embodiments, generating multi-angle real-scene videos and simulated images of the accident scene based on real-scene videos from multiple angles and environmental perception data includes:

[0055] Step 1: Recognize the images in the real-world videos from multiple angles to obtain information such as road edges, the location and category of static obstacles, the location and category of dynamic obstacles, and the license plate information and location of vehicles surrounding the vehicle. Road edges, static obstacles, and dynamic obstacles all refer to objects surrounding the vehicle.

[0056] Step 2: Based on environmental perception data (e.g., detection results data from radar detection equipment), obtain the relative distances between static and dynamic obstacles and the vehicle, the relative speeds of static obstacles and the vehicle, the absolute speeds of dynamic obstacles, and the positions of risk obstacles (obstacles that are less than a set distance threshold from the vehicle).

[0057] Step 3: Post-process and optimize the vehicle's surrounding environment data obtained in Steps 1 and 2. For example, filter obstacle position information based on time changes, and define the display range of surrounding information based on the vehicle's position to ensure smooth display and visual experience.

[0058] Step 4: The post-processed environmental information data surrounding the vehicle is restored to a 3D world to form a simulated image, which is then displayed on the vehicle's screen. Furthermore, the simulated image can also display information such as the speed of adjacent vehicles, obstacle markers, license plate information of obstacles (e.g., if the obstacle is a vehicle), and the distance of obstacles from the vehicle.

[0059] Step 5: Synthesize a multi-angle real-scene video of the accident scene based on real-scene videos from multiple angles. The specific synthesis method has been described in detail in the previous embodiments and will not be repeated here.

[0060] In some embodiments, the vehicle may be equipped with a display interface that can display multi-angle real-time accident scene videos and simulated images. For example, when a user needs to view the real-time accident scene videos and simulated images, they can operate on the display interface configured in the vehicle, and both the real-time accident scene videos and simulated images will be displayed simultaneously. Furthermore, users can download the real-time accident scene videos and simulated images to other terminal devices for viewing. For example, download links or QR codes corresponding to the real-time accident scene videos and simulated images can be automatically generated to facilitate user download and viewing.

[0061] In the process of developing this application, the inventors discovered that while achieving multi-angle and comprehensive reconstruction of the accident scene is important, the most crucial element in determining accident liability is often the scene at the accident site. Therefore, the inventors further optimized the mobile platform accident handling method of this application. The optimized mobile platform accident handling method also includes: generating a local real-scene video of the accident scene corresponding to the direction of the accident and / or generating a local simulated image corresponding to the direction of the accident.

[0062] The direction of the accident can be determined based on images captured by the video capture equipment installed in the vehicle; or based on changes in the vehicle's state (for example, if the vehicle suddenly accelerates forward, it may have been rear-ended, indicating the accident occurred behind the vehicle; if the vehicle suddenly decelerates, it may have rear-ended the vehicle in front, indicating the accident occurred in front of the vehicle; if the vehicle suddenly moves laterally to the left, it may have collided on the right side; if the vehicle suddenly moves laterally to the right, it may have collided on the left side, etc.).

[0063] After determining the direction of the accident, the system can acquire real-scene video from the video acquisition device in that direction and generate a corresponding local real-scene video; and / or acquire detection result data from the radar detection device in that direction and generate a corresponding local simulated image; or acquire real-scene video from the video acquisition device in that direction and generate a corresponding local simulated image; or acquire real-scene video from the video acquisition device in that direction and detection result data from the radar detection device in that direction and generate a corresponding local simulated image.

[0064] In some embodiments, after generating a local real-scene video of the accident scene corresponding to the direction of the accident and / or generating a local simulated image corresponding to the direction of the accident, the image is highlighted on a display device (e.g., a local magnified display is performed, or a local magnified real-scene video or simulated image in that direction is displayed in a picture-in-picture manner).

[0065] In some embodiments, in order to facilitate viewing and reconstructing the accident scene from different angles, the simulated images generated in this application embodiment include at least one of a first-view simulated image, a second-view simulated image, and a third-view simulated image; wherein, the first-view simulated image includes a simulated image viewed from the mobile platform itself in the surrounding direction; the second-view simulated image includes a simulated image viewed from other mobile platforms themselves in the surrounding direction; and the third-view simulated image includes a simulated image viewed from the surrounding environment towards the mobile platform and the other mobile platforms.

[0066] The first-person perspective simulated image can be generated based on environmental perception data collected by multiple radar sensors, as described in the previous embodiments, and will not be repeated here.

[0067] Second-view simulated images can be generated based on environmental perception data collected by multiple radar sensors. For example, environmental perception data includes the type, speed, and relative distance of objects around the vehicle. The object type and speed in this data can be directly used to generate second-view simulated images; the only difference is the "relative distance" of the objects around the vehicle, which needs to be replaced with the relative distance between other vehicles (hereinafter referred to as the target external vehicle) and all other external objects. After determining the relative distance between the target external vehicle and all other external objects, the second-view simulated image can be generated directly by referring to the method used to generate the first-view simulated image. The determination of the relative distance between the target external vehicle and all other external objects can be calculated based on the relative distances of objects around the vehicle in the existing environmental perception data. The specific calculation method can employ existing techniques such as coordinate transformation, and this application does not limit it.

[0068] Third-person perspective simulated imagery, such as overhead view simulated imagery, is relatively simple to generate. Referring to the description for generating second-person perspective simulated imagery, the relative distances between all objects are calculated based on the relative distances of objects around the vehicle in the existing environmental perception data. After determining the relative distances of all objects, simply generate the corresponding type of rendered image at the appropriate location in the rendered image. Furthermore, if speed calibration is required, the speed information can be calibrated on the corresponding rendered image.

[0069] This embodiment provides three simulated images from different perspectives. When reviewing the footage, users can switch between the three simulated images as needed, which helps them to understand the overall situation at the time of the accident more efficiently and accurately.

[0070] As shown in Figure 3, an embodiment of this application provides a mobile platform accident handling method, which further includes:

[0071] S30. Obtain the real-time driving data of the mobile platform. The real-time driving data includes at least one of the following: real-time driving speed, real-time acceleration, direction / angle of movement, and indicator light control data. For example, during vehicle operation, the vehicle's driving speed, real-time acceleration, and indicator light control data are acquired in real-time for use in synthesizing multi-angle accident scene videos and simulated images.

[0072] S40. Obtain real-time driving data of other mobile platforms surrounding the mobile platform. This real-time driving data includes at least one of the following: real-time speed of the external platform, real-time acceleration of the external platform, direction / angle of movement of the platform, and indicator light control data of the external platform. For example, the vehicle's radar detection device can be used to obtain the speed and real-time acceleration of other vehicles around the vehicle, and image recognition data from the vehicle's camera can be used to synthesize real-time video and simulated images of the multi-angle accident scene.

[0073] S50. Display the real-time driving data of the local platform and the external platform in real time. For example, display the real-time driving data of the local platform and the external platform on a display interface configured on the vehicle. For instance, the vehicle's speed is displayed at a preset location in the multi-angle accident scene video (e.g., the lower left corner of the display interface; the specific location is not limited in this application), and the speeds of the external vehicles are displayed near (e.g., on the roof) of the external vehicles in the multi-angle accident scene video. In the simulated image, the speeds of each vehicle are displayed near (e.g., on the roof) of both the local and external vehicles.

[0074] This embodiment displays the real-time speeds of both the vehicle and external vehicles, facilitating a more intuitive determination of accident liability through video playback (e.g., speed data can indicate whether speeding occurred). For example, the vehicle's acceleration is displayed at a preset location in the multi-angle accident scene video (e.g., the lower left corner of the display interface; the specific location is not limited in this application), and the accelerations of the external vehicles are displayed near their respective locations (e.g., on their roofs) in the multi-angle accident scene video. In the simulated image, the accelerations of each vehicle are displayed near both the vehicle and the external vehicles (e.g., on their roofs).

[0075] In this embodiment, by displaying the real-time acceleration of the vehicle and external vehicles, it is easier to intuitively determine the responsibility for the accident through video playback (for example, by checking whether the acceleration of the vehicle decreases during the collision with the external vehicle, if so, it is considered that the driver of the vehicle has taken necessary braking measures, i.e., taken necessary evasive measures; for example, by checking whether the acceleration of the external vehicle decreases during the collision with the vehicle, if so, it is considered that the driver of the vehicle has taken necessary braking measures, i.e., taken necessary evasive measures).

[0076] For example, the indicator light control data of the vehicle is displayed at a preset position in the multi-angle real-time accident scene video (e.g., the lower left corner of the display interface, the specific position is not limited in this application). In the simulated image, the indicator light status of each vehicle is displayed at the headlight positions of the vehicle and external vehicles respectively (e.g., the left lane change indicator light of this vehicle is lit).

[0077] In this embodiment, by displaying the indicator light control data of the vehicle and external vehicles, it is easier to intuitively determine the accident liability through video playback (for example, if the vehicle is rear-ended while changing lanes to the left, the vehicle's liability can be determined by checking whether the left lane change indicator light was on before changing lanes).

[0078] In some embodiments, the mobile platform accident handling method further includes recording vehicle damage and notifying the user. For example, if the vehicle's diagnostic system detects damage to the vehicle body after an accident, the user is notified, or a vehicle damage warning is generated when the user starts the vehicle. The vehicle's diagnostic system records the damaged vehicle structure through its internal diagnostic mechanism, allowing the user to view the damage and make timely judgments about the accident. The diagnostic mechanism of the vehicle's diagnostic system, in addition to traditional diagnostic methods, includes information such as whether the LiDAR used by the dashcam is faulty, and whether the camera is dirty, obstructed, or faulty, to assist in diagnosis. For example, the types of vehicle body damage that the internal vehicle diagnostic mechanism can diagnose include, but are not limited to, rearview mirror malfunctions, power battery system malfunctions, and drive system malfunctions.

[0079] In some embodiments, the mobile platform accident handling method further includes: identifying images in real-world videos captured by multiple video acquisition devices to obtain behaviors such as speeding, crossing lanes, close proximity, and collisions of the vehicle and surrounding objects; and identifying license plate information using OCR and sensors. The key behaviors (e.g., speeding, crossing lanes, close proximity, collisions) and key information (e.g., license plate, vehicle speed) of the vehicle and surrounding objects in the driving scenario are marked in multi-angle real-world video and / or simulated images of the accident scene. The surrounding objects include, but are not limited to, adjacent vehicles and VRUs (Vulnerable Road Users).

[0080] This application marks the key behaviors (e.g., speeding, crossing lanes, being too close, collisions, etc.) and key information (e.g., license plate, speed) of the identified vehicle and surrounding objects in a driving scenario onto multi-angle real-world videos and / or simulated images of the accident scene. This allows users to intuitively understand the key behaviors and key information of the vehicle and surrounding objects when viewing multi-angle real-world videos and / or simulated images of the accident scene, thereby improving the efficiency of determining accident liability.

[0081] In some embodiments, for determining liability in an accident, the mobile platform accident handling method of this application further includes: acquiring the mobile platform's driving data, internal fault data, and the accident location before the accident occurred to determine liability. For example, the mobile platform's driving data, internal fault data, and the accident location are input into a general accident analysis model to obtain the accident liability determination result.

[0082] For example, the general accident analysis model is trained using a VGG network or a ResNet network, and this application does not limit it to this. The training samples used are historical accident data, which includes the driving data of the mobile platform, internal fault data, traffic regulations corresponding to the accident location, and the accident liability determination results in historical accidents.

[0083] Accordingly, in practical use, it is necessary to obtain the driving data, internal fault data, and location or traffic laws and regulations of the mobile platform before the accident, input them into a trained general accident analysis model, and output the corresponding accident liability determination results for reference. The accident liability determination results include, for example, the participants in the traffic accident, the accident type, the accident level, the division of accident liability, and recommendations for liability determination. During vehicle operation, driving data is recorded in real time, so that after an accident, the driving data of the mobile platform before or during the accident can be obtained as needed. Internal fault data is detected and recorded by the vehicle's fault detection system for retrieval after the accident. The location or traffic laws and regulations of the accident site can be determined by the vehicle's own positioning information, thus obtaining the location of the accident site. As for traffic laws and regulations, the corresponding traffic laws and regulations can be obtained based on the determined accident site (for example, the correspondence between different regions and corresponding traffic laws and regulations can be pre-stored, so that the corresponding traffic laws and regulations can be obtained based on the location of the accident site; or the corresponding traffic laws and regulations can be obtained from other devices through location networking).

[0084] In some embodiments, for determining liability in an accident, the mobile platform accident handling method of this application further includes: acquiring the mobile platform's driving data, internal fault data, and the accident location before the accident occurred to determine liability. For example, determining an accident location accident analysis model based on the accident location; inputting the mobile platform's driving data and internal fault data into the accident location accident analysis model to obtain the accident liability determination result.

[0085] In this embodiment, corresponding accident analysis models are trained for different regions or cities. Therefore, when an accident occurs, the corresponding accident analysis model for the accident location is first determined based on the accident location. The acquired driving data and internal fault data of the mobile platform are then input into the accident analysis model to obtain the accident liability determination result for reference. The accident analysis model for the accident location is trained using a VGG network or a ResNet network; this application does not limit the choice. The training samples used are historical accident data, which includes the driving data, internal fault data, and accident liability determination results of the mobile platform in historical accidents. Accordingly, in practical use, it is necessary to first determine the corresponding accident analysis model for the accident location based on the accident location, and then input the acquired driving data and internal fault data of the mobile platform prior to the accident into the accident analysis model for the accident location to obtain the accident liability determination result for reference.

[0086] In some embodiments, after determining the accident liability determination result, an index for downloading the result can be generated. For example, this could be a link or a QR code for download. Users can simply click the link or scan the QR code to download the corresponding accident liability determination result. This result can be presented in report form for easy viewing by users and traffic police, thus more efficiently assisting in the determination of accident liability.

[0087] In developing this application, the applicant discovered that accidents are low-probability events, and most people are unfamiliar with how to handle them. They often become anxious after an accident and fail to take timely and appropriate action. To address this issue, the mobile platform accident handling method proposed in this application further includes automatically guiding the user through the accident handling process after it occurs. For example, this automatic guidance could involve displaying the standard traffic regulation procedures on the vehicle's infotainment screen. Furthermore, it can support user interaction with the vehicle's infotainment system, such as making voice calls to report emergencies or insurance claims, thereby improving accident handling efficiency.

[0088] Furthermore, the applicant found that the above-mentioned normal traffic regulation handling procedure is not applicable to all accident scenarios. Sometimes, depending on the severity of the accident, targeted guidance is needed to ensure more efficient and effective handling. After all, time is life in traffic accidents, and every second wasted means that the injured person is one second closer to danger. Therefore, the applicant has optimized the embodiments of this application as follows: after an accident occurs, automatically guide the user to handle the accident, including: after the accident occurs, determining the accident level; determining the corresponding accident handling plan based on the determined accident level; and guiding the user to handle the accident according to the corresponding accident handling plan.

[0089] For example, accidents can be classified according to their severity, such as Level 1, Level 2, and Level 3 accidents (with increasing severity).

[0090] For a Level 1 accident, such as a minor scratch, the corresponding accident handling procedure is to guide the user to quickly take photos, move the vehicle, and call the insurance company to report the incident and file a claim.

[0091] For a Level 2 accident, such as a rear-end collision, the corresponding accident handling procedure is to guide the user to check if anyone is injured, and if so, to call 120 immediately and rescue the injured.

[0092] For a level 3 accident, such as a vehicle rollover, the corresponding accident handling procedure is to immediately and automatically dial 120 and the police, and guide the user to help themselves.

[0093] In some embodiments, the accident level is determined after an accident occurs as follows: the accident level is determined based on the multi-angle real-scene video and / or simulated images of the accident scene generated in the foregoing embodiments. For example, the vehicle speed before the accident and the changes in acceleration before and after the accident are determined based on the multi-angle real-scene video and / or simulated images of the accident scene; if the vehicle speed is less than a first speed threshold (e.g., 20 km / h) and the acceleration remains unchanged, the accident is identified as a Level 1 accident; if the vehicle speed is less than the first speed threshold (e.g., 20 km / h) and the acceleration changes abruptly (e.g., a rear-end collision causing a sudden increase in acceleration), the accident is identified as a Level 2 accident; if the vehicle speed is greater than a second speed threshold (e.g., 60 km / h) and the acceleration changes abruptly (e.g., a collision causing a sudden increase in acceleration), the accident is identified as a Level 3 accident. Another example is that accident type analysis is performed based on the multi-angle real-scene video and / or simulated images of the accident scene, such as identifying the accident type through image recognition methods, and then the corresponding accident level is determined by the accident type and the correspondence between the accident type and the accident level. In some embodiments, the accident level is determined after an accident occurs as follows: the accident level is determined by analyzing the perception data collected by the mobile platform. For example, if a vehicle suddenly decelerates at a low speed and there is no other fault information, it is determined to be a Level 1 accident; if a vehicle suddenly accelerates or decelerates and a collision occurs but there is no other fault information or only a few component failures, it can be determined to be a Level 2 accident; if a vehicle collision is detected and multiple vehicle component failures are detected, it can be determined to be a Level 3 accident.

[0094] In some embodiments, this application also provides a mobile platform accident handling device, including: a sensing data acquisition program module, used to acquire multi-angle sensing data collected by the mobile platform when an accident occurs; and an image generation program module, used to generate multi-angle accident scene images based on the multi-angle sensing data.

[0095] This application acquires multi-angle perception data collected by a mobile platform when an accident occurs, and generates multi-angle accident scene images based on the multi-angle perception data. This allows the accident scene to be presented from multiple angles, which helps to analyze the situation when the accident occurred from multiple perspectives and enables a more accurate and objective division of accident responsibility.

[0096] In some embodiments, the multi-angle perception data includes: real-scene videos from multiple angles surrounding the mobile platform acquired by multiple video acquisition devices mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: synthesizing multi-angle accident scene videos from the real-scene videos from multiple angles; and / or, the multi-angle perception data includes: environmental perception data around the mobile platform acquired by multiple environmental perception sensors mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: generating simulated images based on the environmental perception data.

[0097] In some embodiments, the image generation module is further configured to generate a local real-scene video of the accident scene corresponding to the direction in which the accident occurred and / or generate a local simulated image corresponding to the direction in which the accident occurred.

[0098] In some embodiments, the simulated imagery includes at least one of a first-view simulated imagery, a second-view simulated imagery, and a third-view simulated imagery; wherein, the first-view simulated imagery includes simulated imagery viewed from the mobile platform itself in the surrounding direction; the second-view simulated imagery includes simulated imagery viewed from other mobile platforms themselves in the surrounding direction; and the third-view simulated imagery includes simulated imagery viewed from the surrounding environment towards the mobile platform and the other mobile platforms.

[0099] In some embodiments, the mobile platform accident handling device further includes: a driving data acquisition program module, used to acquire the real-time driving data of the mobile platform itself; and to acquire the real-time driving data of other mobile platforms around the mobile platform; and a data display program module, used to display the real-time driving data of the mobile platform itself and the real-time driving data of the external platforms in real time.

[0100] In some embodiments, the mobile platform accident handling device further includes: a liability determination module, used to obtain the mobile platform's driving data, internal fault data and the accident location before the accident occurred to determine the liability for the accident.

[0101] In some embodiments, obtaining the driving data, internal fault data, and accident location of the mobile platform before the accident to determine accident liability includes: inputting the driving data, internal fault data, and accident location of the mobile platform into a general accident analysis model to obtain an accident liability determination result; or, determining an accident location accident analysis model based on the accident location; and inputting the driving data and internal fault data of the mobile platform into the accident location accident analysis model to obtain an accident liability determination result.

[0102] In some embodiments, the mobile platform incident handling device further includes a teaching program module for automatically guiding the user to handle the incident after it occurs.

[0103] In some embodiments, after an incident occurs, the user is automatically guided to handle the incident, including:

[0104] After an accident occurs, the accident level is determined; based on the determined accident level, a corresponding accident handling plan is determined; and the user is guided to handle the accident according to the corresponding accident handling plan.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of combined actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Secondly, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application. In the above embodiments, the descriptions of each embodiment have their own emphasis; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0106] This application utilizes a design scheme incorporating features such as global environmental fusion information recording, accident key information marking, vehicle damage self-diagnosis, liability division auxiliary analysis, QR code accident video and accident report index, and liability determination process tutorials. In practice, this provides users with a more comprehensive display of the accident environment, other vehicle information, and damage information, as well as more efficient accident liability determination processing and video information export.

[0107] In some embodiments, this application provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in any of the foregoing embodiments.

[0108] In some embodiments, this application provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing embodiments.

[0109] In some embodiments, this application provides a computer program product including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the steps of the method described in any of the foregoing embodiments.

[0110] In some embodiments, this application provides a mobile platform characterized by having a computer device as described in any of the foregoing embodiments installed.

[0111] Figure 4 is a schematic diagram of the hardware structure of a computer device for executing a mobile platform accident handling method according to another embodiment of this application. As shown in Figure 4, the device includes:

[0112] One or more processors 410 and memory 420, with one processor 410 as an example in Figure 4.

[0113] The device for performing the mobile platform accident handling method may also include an input device 430 and an output device 440.

[0114] The processor 410, memory 420, input device 430 and output device 440 can be connected by a bus or other means. Figure 4 shows an example of connection by a bus.

[0115] The memory 420, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the mobile platform accident handling method in the embodiments of this application. The processor 410 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 420, thereby implementing the mobile platform accident handling method in the above-described method embodiments.

[0116] The memory 420 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the mobile platform incident handling device. Furthermore, the memory 420 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 420 may optionally include memory remotely located relative to the processor 410, and these remote memories may be connected to the mobile platform incident handling device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] Input device 430 can receive input digital or character information and generate signals related to user settings and function control of the mobile platform accident handling device. Output device 440 may include display devices such as a display screen.

[0118] The one or more modules are stored in the memory 420, and when executed by the one or more processors 410, they execute the mobile platform accident handling method in any of the above method embodiments.

[0119] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0120] The computer device in this application embodiment exists in various forms, including but not limited to:

[0121] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0122] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0123] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes audio and video players (such as iPods), handheld game consoles, e-book readers, as well as smart toys and portable car navigation devices.

[0124] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0125] (5) Other electronic devices with data interaction functions.

[0126] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for handling mobile platform incidents, comprising: Acquire multi-angle sensing data collected by the mobile platform at the time of the accident; Multi-angle accident scene images are generated based on the multi-angle sensing data.

2. The method according to claim 1, characterized in that: The multi-angle perception data includes: real-scene videos from multiple angles surrounding the mobile platform, acquired by multiple video acquisition devices mounted on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: synthesizing multi-angle accident scene real-scene videos from multiple angles; and / or, The multi-angle perception data includes: environmental perception data around the mobile platform collected by multiple environmental perception sensors installed on the mobile platform; generating multi-angle accident scene images based on the multi-angle perception data includes: generating simulated images based on the environmental perception data.

3. The method according to claim 1, characterized in that, The multi-angle perception data includes: real-scene videos from multiple angles around the mobile platform collected by multiple video acquisition devices and environmental perception data around the mobile platform collected by multiple environmental perception sensors. The step of generating multi-angle accident scene images based on the multi-angle perception data includes: generating multi-angle accident scene real-scene videos and simulated images based on real-scene videos from multiple angles and environmental perception data.

4. The method according to claim 3, characterized in that, The process of generating multi-angle real-scene videos and simulated images of the accident scene based on real-scene videos and environmental perception data from multiple angles includes: Step 1: Recognize the images in the real-world videos from the multiple angles; Step 2: Based on environmental perception data, obtain the relative distances between static and dynamic obstacles and the vehicle, the relative speeds of static obstacles and the vehicle, the absolute speeds of dynamic obstacles, and the positions of potential obstacles; Step 3: Post-process and optimize the vehicle's surrounding environment data obtained in Step 1 and Step 2; Step 4: Reconstruct the post-processed environmental information data of the vehicle's surroundings into a 3D world to form a simulated image; Step 5: Synthesize a multi-angle real-scene video of the accident scene based on the real-scene videos from multiple angles.

5. The method according to claim 2, characterized in that, Also includes: Generate download links or QR codes corresponding to the real-world video and simulated images of the accident scene for users to download and view.

6. The method according to claim 2, characterized in that, Also includes: Generate local real-scene video of the accident scene corresponding to the direction in which the accident occurred and / or generate local simulated images corresponding to the direction in which the accident occurred; or, The simulated images include at least one of first-view simulated images, second-view simulated images, and third-view simulated images; wherein... The first perspective simulated imagery includes simulated images viewed from the mobile platform itself in the surrounding directions; The second perspective simulated imagery includes simulated images viewed from the surrounding direction by the other mobile platform itself; The third-view simulated imagery includes simulated images viewed from the surrounding environment toward the mobile platform and the other mobile platforms.

7. The method according to claim 2, characterized in that, Also includes: Obtain the real-time driving data of the mobile platform. Obtain real-time driving data of other mobile platforms around the mobile platform; The system displays real-time driving data from both the local platform and the external platform.

8. The method according to claim 1, characterized in that: The acquisition of multi-angle perception data collected by the mobile platform at the time of the accident includes: Detect signals indicating an incident on the mobile platform; When an accident signal is detected, the multi-angle sensing data collected by the mobile platform within a predetermined time period at the time of the accident is acquired.

9. The method according to any one of claims 1-8, characterized in that, Also includes: To determine liability for the accident, the driving data, internal fault data, and location of the accident were obtained from the mobile platform prior to the accident. And / or, After an incident occurs, the system automatically guides the user through the incident handling process.

10. The method according to claim 9, characterized in that, To determine liability for the accident, the following data will be collected: Pre-accident driving data, internal fault data, and the accident location of the mobile platform. The driving data, internal fault data, and accident location of the mobile platform are input into a general accident analysis model to obtain the accident liability determination result; or, An accident analysis model is determined based on the location of the accident; the driving data and internal fault data of the mobile platform are input into the accident analysis model to obtain the accident liability determination result.

11. The method according to claim 9, characterized in that, After an incident occurs, the system automatically guides the user through the incident handling process, including: After an accident occurs, determine the accident level; Based on the determined accident level, determine the corresponding accident handling plan; The user is guided to handle the incident according to the corresponding incident handling plan.

12. The method according to any one of claims 2-8, characterized in that, The determination of the accident level after an accident occurs includes: The vehicle speed before the accident and the changes in acceleration before and after the accident are determined based on the multi-angle real-scene video and / or simulated images of the accident scene. If the vehicle speed is less than the first speed threshold and the acceleration remains unchanged, the accident is classified as a Level 1 accident. If the vehicle speed is less than the first speed threshold and there is a sudden change in acceleration, the accident is classified as a level two accident. If the vehicle speed exceeds the second speed threshold and there is a sudden change in acceleration, the accident is classified as a level three accident.

13. The method according to any one of claims 2-8, characterized in that, The determination of the accident level after an accident occurs includes: The accident level is determined by analyzing the sensing data collected by the mobile platform. The vehicle suddenly decelerated while its speed was low and there were no other fault information, which was determined to be a Level 1 accident. If a vehicle is detected to suddenly accelerate or decelerate, and a collision occurs but there are no other fault information or only a few component failures, it can be determined as a level two accident. If a collision is detected and multiple parts of the vehicle malfunction, it can be classified as a Level 3 accident.

14. The method according to claim 9, characterized in that, Also includes: Record the damage to the vehicle and notify the user.

15. The method according to any one of claims 1-8, characterized in that, Also includes: Images from real-world videos captured by multiple video acquisition devices are identified, and license plate information is recognized through OCR and sensors to obtain key behaviors and key information. The key behaviors and key information are marked in multi-angle real-scene videos and / or simulated images of the accident scene.

16. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-14.

17. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-15.

18. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-15.

19. A mobile platform, characterized in that, The computer device of claim 16 is installed.

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