Outside-vehicle real-condition optimization method, device, cloud server, vehicle and system

Abstract

The embodiment of the invention provides an out-of-vehicle live optimization method and device, a cloud server, a vehicle and a system. Relates to the technical field of vehicles. The method comprises the following steps: acquiring real-time acquired real-time external real-time condition sensing data of a vehicle in a real-time external real-time condition checking process of the vehicle; a target model is called to execute quality optimization processing on the out-of-vehicle actual condition sensing data based on the out-of-vehicle actual condition sensing data, optimized actual condition data are obtained, and the target model is a visual model matched with the data format selected from a plurality of visual models based on the data format of the out-of-vehicle actual condition sensing data; and sending the optimized live data to the client, wherein the client is used for providing an outside-vehicle live real-time viewing function of the vehicle based on the optimized live data. The method is used for achieving the effect of accurately checking the surrounding environment of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, cloud server, vehicle, and system for optimizing real-time conditions outside a vehicle. Background Technology

[0002] Real-time vehicle exterior view is a feature that allows users to view the vehicle's surroundings in real time via their mobile phones. By viewing photos and / or live videos of the vehicle's surroundings, it can enhance the vehicle's safety and security capabilities. Currently, real-time vehicle exterior view is gradually becoming more widespread.

[0003] In related technologies, photos and / or videos of the vehicle's surrounding environment are acquired through direct communication between the client and the vehicle to enable real-time viewing of the external environment. Specifically, the vehicle configures camera information according to the access method of the camera video stream, initiates the vehicle service program to respond to the client's video request, and provides the client with a unified video stream monitoring and control service; the client loads the configuration information and parses it into a configuration object, completes the interaction with the vehicle's identity metadata, and establishes end-to-end video data transmission based on World Wide Web Real-Time Communication (WebRTC) to complete video control.

[0004] However, the above method suffers from poor quality of the surrounding environment photos and videos displayed on the client, making it impossible to accurately view the vehicle's surroundings and affecting the user experience. Summary of the Invention

[0005] This application provides a method, apparatus, cloud server, vehicle, and system for optimizing the external environment of a vehicle, in order to achieve the effect of accurately viewing the surrounding environment of the vehicle.

[0006] In a first aspect, embodiments of this application provide a method for optimizing real-time vehicle exterior conditions, applied to a cloud server, comprising:

[0007] During the real-time observation of the vehicle's external conditions, the vehicle acquires real-time external condition perception data.

[0008] Based on the real-time perception data outside the vehicle, the target model is called to perform quality optimization processing on the real-time perception data outside the vehicle to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format based on the data format of the real-time perception data outside the vehicle.

[0009] The system sends optimized real-time data to the client, which then provides a real-time view of the vehicle's external conditions based on this optimized data.

[0010] In one possible implementation, based on real-time external scene perception data, a target model is invoked to perform image optimization processing on the real-time external scene perception data to obtain optimized real-time scene data, including:

[0011] If the viewing type of the real-time view of the outside situation is panoramic view, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing and data fitting processing to obtain the optimized real-time view data from the bird's-eye view.

[0012] If the viewing type of the real-time view of the external situation is image viewing or video viewing, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing to obtain optimized real-time data.

[0013] The quality optimization process includes brightness adjustment and / or color optimization.

[0014] In one possible implementation, the real-time external perception data is anonymized data.

[0015] In one possible implementation, the triggering conditions for real-time viewing of the external environment include:

[0016] Receive and respond to commands from the client to view the real-time situation outside the vehicle;

[0017] Correspondingly, the method for optimizing the external real-time situation also includes: sending a data request instruction corresponding to the external real-time situation viewing instruction to the vehicle. The data request instruction is used to instruct the cloud server to transmit the external real-time situation perception data corresponding to the external real-time situation viewing instruction in real time.

[0018] One possible implementation also includes:

[0019] Based on the optimized real-time data, the anomaly detection model is invoked to perform environmental anomaly detection, and the environmental anomaly detection results corresponding to the vehicle are obtained.

[0020] If the environmental anomaly detection results indicate that there are abnormal conditions in the environment around the vehicle, an alarm message will be generated for the abnormal conditions.

[0021] Send alarm information to the client.

[0022] Secondly, embodiments of this application provide a method for optimizing real-time external conditions, applied to a vehicle, including:

[0023] During the real-time observation of the vehicle's external conditions, real-time perception data of the external conditions is acquired.

[0024] The system sends real-time external situation perception data to the cloud server. The cloud server then calls the target model to perform quality optimization processing on the real-time external situation perception data and sends the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format of the real-time external situation perception data. The client is used to provide real-time viewing of the vehicle's external situation based on the optimized real-time data.

[0025] One possible implementation involves acquiring real-time external perception data, including:

[0026] Based on the viewing type of real-time view of the outside of the vehicle, the target camera corresponding to the viewing type is matched among multiple cameras;

[0027] Based on the data collected by the target camera, real-time perception data of the vehicle's external environment is obtained.

[0028] In one possible implementation, real-time external perception data is obtained based on data collected by the target camera, including:

[0029] The data collected by the target camera is anonymized to obtain real-time perception data of the vehicle's external environment.

[0030] In one possible implementation, the triggering conditions for real-time viewing of the external environment include:

[0031] It receives and responds to data request instructions sent by the client. The data request instructions are generated by the cloud server in response to the real-time view instructions sent by the client. The data request instructions are used to instruct the cloud server to transmit the real-time view data corresponding to the real-time view instructions to the cloud server.

[0032] Thirdly, embodiments of this application provide a vehicle exterior real-time optimization device, deployed on a cloud server, comprising:

[0033] The acquisition module is used to acquire real-time external situation perception data of the vehicle during the real-time viewing of the vehicle's external situation.

[0034] The processing module is used to perform quality optimization processing on the vehicle's external real-time perception data by calling the target model to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format of the vehicle's external real-time perception data. The optimized real-time data is then sent to the client, which provides the function of real-time viewing of the vehicle's external real-time situation based on the optimized real-time data.

[0035] In one possible implementation, the processing module is specifically used for:

[0036] If the viewing type of the real-time view of the outside situation is panoramic view, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing and data fitting processing to obtain the optimized real-time view data from the bird's-eye view.

[0037] If the viewing type of the real-time view of the external situation is image viewing or video viewing, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing to obtain optimized real-time data.

[0038] The quality optimization process includes brightness adjustment and / or color optimization.

[0039] In one possible implementation, the real-time external perception data is anonymized data.

[0040] In one possible implementation, the triggering conditions for real-time viewing of the external environment include:

[0041] Receive and respond to commands from the client to view the real-time situation outside the vehicle;

[0042] Correspondingly, the processing module is also used to: send a data request instruction corresponding to the vehicle's external real-time viewing instruction to the vehicle. The data request instruction is used to instruct the cloud server to transmit the external real-time perception data corresponding to the vehicle's external real-time viewing instruction in real time.

[0043] In one possible implementation, the processing module is further configured to:

[0044] Based on the optimized real-time data, the anomaly detection model is invoked to perform environmental anomaly detection, and the environmental anomaly detection results corresponding to the vehicle are obtained.

[0045] If the environmental anomaly detection results indicate that there are abnormal conditions in the environment around the vehicle, an alarm message will be generated for the abnormal conditions.

[0046] Send alarm information to the client.

[0047] Fourthly, embodiments of this application provide an external real-time situation optimization device, deployed in a vehicle, comprising:

[0048] The acquisition module is used to acquire real-time external situation perception data during the real-time viewing of the vehicle's external situation;

[0049] The processing module is used to send the vehicle's external real-time perception data to the cloud server. The cloud server is used to call the target model to perform quality optimization processing on the vehicle's external real-time perception data and send the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format of the vehicle's external real-time perception data. The client is used to provide the vehicle's external real-time viewing function based on the optimized real-time data.

[0050] In one possible implementation, the acquisition module is specifically used for:

[0051] Based on the viewing type of real-time view of the outside of the vehicle, the target camera corresponding to the viewing type is matched among multiple cameras;

[0052] Based on the data collected by the target camera, real-time perception data of the vehicle's external environment is obtained.

[0053] In one possible implementation, the acquisition module is further configured to:

[0054] The data collected by the target camera is anonymized to obtain real-time perception data of the vehicle's external environment.

[0055] In one possible implementation, the triggering conditions for real-time viewing of the external environment include: receiving and responding to a data request instruction sent by a client. The data request instruction is generated by the cloud server in response to the client's real-time viewing instruction and is used to instruct the cloud server to transmit the real-time perception data of the external environment corresponding to the real-time viewing instruction to the cloud server.

[0056] Fifthly, embodiments of this application provide a cloud server, including: a memory and a processor;

[0057] The memory stores the instructions that the computer executes;

[0058] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0059] In a sixth aspect, embodiments of this application provide a vehicle, including: the vehicle being capable of performing the second aspect and / or various possible implementations of the second aspect as described above.

[0060] In a seventh aspect, embodiments of this application provide an exterior real-time optimization system, comprising: a cloud server as described in the fifth aspect and / or various possible implementations of the fifth aspect, and a vehicle as described in the sixth aspect and / or various possible implementations of the sixth aspect.

[0061] Eighthly, 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.

[0062] Ninthly, 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.

[0063] The vehicle exterior real-time situation optimization method, device, cloud server, vehicle, and system provided in this application's embodiments acquire real-time exterior real-time perception data during the real-time viewing of the vehicle's exterior, providing foundational data support for subsequent exterior real-time situation optimization. Based on the exterior real-time perception data, the target model is invoked in the cloud server to perform quality optimization processing on the exterior real-time perception data, obtaining optimized real-time data. This significantly improves the quality optimization processing effect, making the output optimized real-time data clearer and more accurate, thereby enhancing the user's viewing experience. Simultaneously, by performing quality optimization processing in the cloud service, consistent and advanced optimization processing is ensured for exterior real-time data collected from different vehicles at different times. This also reduces hardware costs and power consumption on the vehicle side, improving vehicle safety and stability. The optimized real-time data is sent to the client, enabling users to view the surrounding environment of the vehicle in real-time and clearly, further enhancing the user experience. Attached Figure Description

[0064] 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.

[0065] Figure 1 A schematic diagram illustrating a scenario for the vehicle exterior real-time optimization method provided in an embodiment of this application;

[0066] Figure 2 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 1 ;

[0067] Figure 3 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 2 ;

[0068] Figure 4 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 3 ;

[0069] Figure 5 Schematic diagram of the exterior real-time situation optimization device provided in the embodiments of this application Figure 1 ;

[0070] Figure 6 Schematic diagram of the exterior real-time situation optimization device provided in the embodiments of this application Figure 2

[0071] Figure 7 This is a schematic diagram of the structure of a cloud server provided in an embodiment of this application.

[0072] 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

[0073] 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.

[0074] In related technologies, photos and / or videos of the vehicle's surrounding environment are acquired through direct communication between the client and the vehicle to enable real-time viewing of the external environment. Specifically, the vehicle configures its camera information according to the access method of the camera video stream, initiates a vehicle service program to respond to the client's video request, and provides the client with a unified video stream monitoring and control service; the client loads the configuration information and parses it into a configuration object, completes the interaction with the vehicle's identity metadata, and establishes end-to-end video data transmission based on World Wide Web Real-Time Communication (WebRTC) to complete video control. However, the above method suffers from poor quality of the surrounding environment photos and videos displayed by the client, resulting in an inaccurate view of the vehicle's surroundings and affecting the user experience.

[0075] The vehicle exterior real-time situation optimization method provided in this application optimizes the vehicle exterior real-time situation data by calling a target model on a cloud server based on the data. This significantly improves the quality optimization effect, resulting in clearer and more accurate output data, thus enhancing the user's viewing experience. Furthermore, cloud-based optimization ensures consistent and advanced optimization of exterior real-time data collected from different vehicles at different times, while reducing hardware costs and power consumption on the vehicle side and improving vehicle safety and stability. The optimized real-time data is then sent to the client, enabling users to view the vehicle's surroundings clearly and in real-time, further enhancing the user experience.

[0076] Figure 1 This is a schematic diagram of a scenario for the vehicle exterior real-time optimization method provided in the embodiments of this application, such as... Figure 1 As shown, the specific application scenario of this application embodiment includes user 11, cloud server 12, vehicle 13, and client 14, wherein:

[0077] User 11 initiates a real-time view of the vehicle's external conditions via client 14. Client 14, through a communication connection with cloud service 12, sends the corresponding real-time view command to cloud server 12. Cloud server 12 receives and responds to the real-time view command sent by client 14, triggering the real-time view. Through a communication connection with vehicle 13, it sends a data request command corresponding to the real-time view command to vehicle 13. Upon receiving the data request command, vehicle 13 transmits the corresponding real-time external condition perception data to cloud server 12 in real time.

[0078] During the real-time viewing of the vehicle's external conditions, the cloud server 12 continuously receives external condition perception data transmitted by the vehicle 13. The cloud server 12 executes an external condition optimization method to obtain optimized condition data, which is then sent to the client 14. Upon receiving the optimized condition data, the client 14 provides the user 11 with a real-time view of the vehicle's external conditions.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 2 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 1 ,like Figure 2As shown, this method is applied to a cloud server and includes:

[0081] S201. During the real-time observation of the vehicle's external conditions, acquire the real-time external condition perception data obtained by the vehicle.

[0082] Real-time external perception data reflects the current external environment in which the vehicle is located. This data is collected in real-time by sensors such as cameras and radar installed on the vehicle to assess the surrounding environment. Optionally, real-time external perception data may include at least one of the following environmental data types: images, video, audio, and radar data.

[0083] During the real-time monitoring of the vehicle's external environment, the cloud server communicates with the vehicle to obtain real-time perception data collected by sensors installed on the vehicle. By acquiring this data in real time, the cloud server can understand the vehicle's surrounding environment, providing foundational data support for subsequent optimization of the external environment.

[0084] For example, during real-time monitoring of the vehicle's external environment, the cloud server establishes communication with the vehicle. The vehicle transmits video streams captured by multiple cameras in real time to the vehicle's local processing unit via its in-vehicle network. The vehicle's local processing unit performs preliminary preprocessing on the video streams and uploads them to the cloud server via a wireless communication module. The cloud server receives the video stream data and obtains the external environment perception data.

[0085] For example, during real-time monitoring of the vehicle's external environment, the cloud server directly communicates with the acquisition module inside the vehicle. The acquisition module directly uploads the acquired video stream to the cloud server via a wireless communication module. The cloud server receives and caches the video stream, obtaining the real-time external environment perception data.

[0086] S202. Based on the real-time perception data outside the vehicle, call the target model to perform quality optimization processing on the real-time perception data outside the vehicle to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format based on the data format viewed outside the vehicle.

[0087] The target model refers to the visual model selected from multiple pre-trained visual models that is suitable for processing the current data format, based on the data format of the real-time perception data outside the vehicle.

[0088] Optionally, the data format includes at least one of the following: video stream format, image format, and point cloud format. For example, the video stream format may further include at least one of the following: Moving Picture Experts Group 4 (MP4), Audio Video Interleave (AVI), Windows Media Video (WMV), and QuickTime File Format (MOV). For example, the image format may further include at least one of the following: Bitmap Image File (BMP), Joint Photographic Experts Group (JPEG / JPG), and Portable Network Graphics (PNG).

[0089] Furthermore, if the data formats for viewing real-time conditions outside the vehicle include multiple formats, each data format corresponds to a target model. Based on each data format of the real-time condition perception data, the corresponding target model is called to perform quality optimization processing to obtain optimized real-time data for each data format.

[0090] Optionally, when the data format is a video stream, the target model is a video visual model. When the data format is an image, the target model is an image visual model. When the data format is a point cloud, the target model is a point cloud visual model.

[0091] Quality optimization processing refers to enhancing, denoising, and sharpening the external real-time perception data through quality optimization methods within the target model, in order to improve the quality of the external real-time perception data.

[0092] After receiving the real-time situational awareness data outside the vehicle, the cloud server first identifies the data format of the data. Then, based on the data format, it selects a target model suitable for processing the data from multiple pre-trained visual models. Finally, it calls the target model to perform quality optimization processing on the real-time situational awareness data to obtain optimized real-time data. For example, the visual model includes at least one of the ViT model, the Swing Transformer model, etc.

[0093] By selecting a target model with a suitable data format within a cloud server for quality optimization, the optimization effect can be significantly improved, resulting in clearer and more accurate output data, thereby enhancing the user's viewing experience.

[0094] Because cloud servers possess powerful computing resources, they can run more complex and advanced image or video quality optimization algorithms. This allows for the detailed reconstruction of low-quality vehicle exterior perception data, restoring more details and resulting in high-quality, clear optimized data. This optimized data provides users with a clearer viewing experience of the vehicle's exterior. On the vehicle side, however, limitations in the computing power and power consumption of the vehicle's processing center limit the use of simpler quality optimization algorithms, making it difficult to achieve the same optimization effects as cloud servers.

[0095] Performing quality optimization on the vehicle side requires the vehicle to be equipped with corresponding computing hardware to run the optimization algorithms, increasing the vehicle's production costs. Furthermore, the operation of this computing hardware also consumes the vehicle's electrical energy, increasing power consumption. For electric vehicles, performing quality optimization on the vehicle side will affect their driving range. For gasoline vehicles, when performing quality optimization on the vehicle side, there is a risk that the vehicle will run out of power and be unable to start safely and stably, due to the limited battery capacity. However, by using cloud servers for quality optimization, the vehicle only needs basic data acquisition and transmission functions, eliminating the need for high-performance computing hardware. This reduces on-vehicle hardware costs and power consumption, improving vehicle safety and stability.

[0096] Meanwhile, the cloud server can centrally manage and uniformly update multiple vision models. When a new vision model is available or the parameters of an existing vision model are adjusted, only one update is needed on the cloud server. All vehicles connected to the cloud server can automatically apply the latest vision model when transmitting real-time external data, ensuring that real-time external data collected from different vehicles at different times can receive consistent and advanced optimization processing, avoiding potential differences and inconsistencies that may occur due to independent update and optimization strategies on the vehicle side.

[0097] S203. Send optimized real-time data to the client, which is used to provide real-time viewing of the vehicle's external conditions based on the optimized real-time data.

[0098] A client is a device or software used by a user to view the real-time situation outside the vehicle. Optionally, a client may include at least one of the following: a mobile app, an in-vehicle display screen, or a webpage.

[0099] After completing quality optimization processing and obtaining optimized real-time data, the cloud server sends the optimized real-time data to the client via a wireless communication network. Upon receiving the optimized real-time data, the client performs corresponding decoding and rendering operations according to the type of optimized real-time data, ultimately displaying the vehicle's external conditions accurately and clearly on the client's user interface. This allows users to view the vehicle's surrounding environment in real time and clearly, improving the user's viewing experience and the vehicle's safety and defense capabilities.

[0100] The vehicle exterior real-time situation optimization method provided in this application acquires real-time exterior situation perception data during the real-time viewing of a vehicle, providing foundational data support for subsequent exterior situation optimization. Based on the exterior situation perception data, a target model is invoked in a cloud server to perform quality optimization processing on the exterior situation perception data, obtaining optimized real-time data. This significantly improves the quality optimization processing effect, making the output optimized real-time data clearer and more accurate, thereby enhancing the user's viewing experience. Simultaneously, by performing quality optimization processing in the cloud service, consistent and advanced optimization processing is ensured for exterior situation data collected from different vehicles at different times. This also reduces hardware costs and power consumption on the vehicle side, improving vehicle safety and stability. The optimized real-time data is then sent to the client, enabling users to view the surrounding environment of the vehicle in real-time and clearly, further enhancing the user experience.

[0101] Figure 3 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the examples, the method for optimizing the external conditions of the vehicle is described in detail. The method includes:

[0102] In one possible implementation, step S202 may further include:

[0103] S2021. If the viewing type of the real-time view of the external situation is panoramic view, the real-time perception data of the external situation is input into the corresponding target model. In the target model, the real-time perception data of the external situation is subjected to quality optimization processing and data fitting processing to obtain the optimized real-time data of the bird's-eye view.

[0104] Panoramic view allows you to view the environment around the vehicle in 360 degrees or over a specific large area. Bird's-eye view is a top-down perspective that provides a comprehensive overview of the vehicle's surroundings. Data fitting processing refers to fusing image or video data collected from multiple sensors to generate optimized real-time data with a coherent and unified bird's-eye view.

[0105] Quality optimization processing includes brightness adjustment and / or color optimization, designed to improve the visual quality of images and / or videos, making them clearer and brighter to enhance the user's visual experience.

[0106] When a user selects panoramic view, the received real-time external perception data is input into a target model specifically designed for panoramic viewing. In the target model, firstly, the brightness and / or color of the real-time external perception data are adjusted to improve image quality. Then, a data fitting algorithm fuses the data into a bird's-eye view panoramic image, resulting in optimized real-time data from a bird's-eye view perspective. Through panoramic viewing and the bird's-eye view, users can gain a more comprehensive understanding of the environment surrounding the vehicle, improving driving safety and convenience. Simultaneously, the application of quality optimization and data fitting further enhances the clarity and coherence of the panoramic image, improving the user's viewing experience.

[0107] S2022. If the viewing type of the real-time view of the external situation is image viewing or video viewing, the real-time perception data of the external situation is input into the corresponding target model. In the target model, the quality optimization processing of the real-time perception data of the external situation is performed to obtain the optimized real-time data.

[0108] When viewing the real-time exterior view as an image, users want to see specific images of the environment surrounding the vehicle. When viewing the real-time exterior view as a video, users want to see a specific video stream of the environment surrounding the vehicle. Unlike panoramic viewing, image or video viewing focuses more on detailed observation of specific areas.

[0109] When viewing the real-time external environment as an image or video, the received real-time external perception data is input into the corresponding target model. Within the target model, the input real-time external perception data undergoes quality optimization processing. By adjusting parameters such as brightness, contrast, and color saturation, the image clarity and color reproduction are improved, resulting in optimized real-time data. This ensures that users can more clearly observe specific areas or targets in the vehicle's surrounding environment, enhancing driving safety and accuracy.

[0110] In one possible implementation, the real-time external perception data is anonymized data.

[0111] Anonymized data is data that has been processed to remove or replace sensitive information. Sensitive information includes vehicle license plate numbers, passenger facial features, and detailed information about private residences or commercial premises. When vehicle exterior perception data is anonymized, it can protect personal privacy and trade secrets, while allowing the data to be used for analysis and application within legal and compliant boundaries.

[0112] Optionally, after collecting the real-time external perception data, the data is anonymized before being input into the target model for processing. Using the anonymized data as the input to the target model ensures that personal privacy or trade secrets are not leaked during data processing and analysis. For example, anonymization methods include at least one of the following: blurring, obscuring, replacing, or deleting sensitive information.

[0113] Optionally, the collected real-time external perception data has been anonymized before being uploaded to the server to avoid privacy leaks during the upload process.

[0114] In one possible implementation, the triggering conditions for real-time viewing of the external environment include: receiving and responding to a real-time viewing command sent by a client; correspondingly, the real-time viewing optimization method further includes: sending a data request command corresponding to the real-time viewing command to the vehicle, wherein the data request command is used to instruct the cloud server to transmit the real-time perception data of the external environment corresponding to the real-time viewing command to the cloud server in real time.

[0115] A real-time external view command is a specific command sent by the client to the system requesting to view the real-time external situation of the vehicle. The real-time external view command includes at least one of the following: the real-time external view mode and the data format of the external view perception data. Optionally, the viewing type for real-time external view includes panoramic view, image view, or video view.

[0116] The data request instruction is generated by the cloud server based on the real-time external view instruction. It is used to instruct the vehicle to transmit the real-time external view perception data corresponding to the real-time external view instruction to the cloud server in real time.

[0117] The cloud server receives a real-time external view command from the client, responds by generating and sending a corresponding data request command to the vehicle. Upon receiving the data request command, the vehicle transmits the relevant external view perception data to the cloud server in real time, according to the command's requirements. This allows users to actively trigger real-time external view functionality via the client, enabling them to obtain timely environmental information about the vehicle's surroundings, improving their control over the vehicle's environment, and enhancing driving safety and convenience.

[0118] In one possible implementation, the vehicle exterior real-time optimization method further includes:

[0119] Step A: Based on the optimized real-time data, call the anomaly detection model to perform environmental anomaly detection and obtain the environmental anomaly detection results corresponding to the vehicle.

[0120] The optimized real-time data is input into a pre-trained anomaly detection model. The anomaly detection model processes and analyzes the optimized real-time data, judges whether there are any anomalies in the vehicle's surrounding environment according to preset rules and algorithms, and outputs the corresponding environmental anomaly detection results for the vehicle. This can promptly detect potential dangers in the vehicle's surrounding environment, provide users with early warning information, and improve vehicle safety.

[0121] Step B: If the environmental anomaly detection result indicates that there is an abnormal situation in the environment around the vehicle, generate alarm information for the abnormal situation.

[0122] Alarm messages are generated based on environmental anomaly detection results and can alert the user to abnormal conditions in the vehicle's surroundings. Alarm messages include at least one of the following: environmental anomaly detection results, anomaly type, anomaly location, and anomaly severity.

[0123] If the environmental anomaly detection result indicates that there is an abnormal situation in the vehicle's surrounding environment, the cloud server generates alarm information for the abnormal situation according to preset rules and templates, so as to promptly and accurately convey the abnormal situation in the vehicle's surrounding environment to the user, enabling the user to react quickly.

[0124] Step C: Send alarm information to the client.

[0125] After generating alarm information, the cloud server sends the alarm information to the user's client via network communication technology. Upon receiving the alarm information, the client displays it to the user in an appropriate manner, such as showing text prompts, playing sound alarms, or vibration, enabling the user to be aware of abnormal situations in the vehicle's surrounding environment as soon as possible, thus improving the user experience.

[0126] Figure 4 A flowchart illustrating the vehicle exterior real-time optimization method provided in this application embodiment. Figure 3 ,like Figure 4 As shown, this method is applied to a vehicle and includes:

[0127] S401. During the real-time observation of the vehicle's external conditions, real-time perception data of the external conditions is acquired.

[0128] After a user initiates a real-time view of the external environment, the vehicle's sensor system begins to work continuously, collecting various data about the external environment in real time according to the predetermined sampling frequency and collection range. This results in real-time perception data of the external environment, providing a data foundation for users to understand the dynamic changes around the vehicle in a timely manner.

[0129] S402. Send the vehicle exterior real-time perception data to the cloud server. The cloud server calls the target model to perform quality optimization processing on the vehicle exterior real-time perception data and sends the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format based on the vehicle exterior real-time viewing data. The client is used to provide the vehicle's real-time exterior real-time viewing function based on the optimized real-time data.

[0130] The vehicle transmits real-time external situational perception data to a cloud server via its in-vehicle communication module. Upon receiving the data, the cloud server selects a suitable target model from a pool of pre-prepared visual models based on the data format, performing quality optimization on the external situational perception data to obtain optimized real-time data. The cloud server then sends the optimized real-time data to the client via the network. Upon receiving the data, the client displays it according to a pre-defined interface and display method, allowing users to view the optimized external situation of the vehicle in real time.

[0131] The real-time external perception data is sent to a cloud server for processing. Leveraging the powerful computing capabilities of the cloud server, the data can be quickly and efficiently optimized, improving its clarity and accuracy. Furthermore, selecting a target model that matches the data format of the real-time perception data allows for better adaptation to different data formats, ensuring optimal optimization results.

[0132] In one possible implementation, step S401 may further include:

[0133] Step 1: Based on the viewing type of the real-time view outside the vehicle, match the target camera corresponding to the viewing type among multiple cameras.

[0134] A multi-camera system consists of multiple cameras installed in different locations on a vehicle, each with different functions. These cameras allow for the capture of images of the vehicle's exterior from various angles.

[0135] The target camera is a specific camera selected from multiple cameras based on the real-time viewing type of the external environment. It meets the viewing requirements corresponding to the viewing type.

[0136] The vehicle's internal control unit, based on the real-time viewing type of the external environment, matches and searches among multiple cameras using a pre-defined mapping between viewing types and the desired camera to determine the target camera corresponding to that viewing type. For example, if the viewing type is an image of the area behind the vehicle, then the rear camera is identified as the target camera.

[0137] By matching the target camera according to the viewing type, image information of the area that the user needs to view can be accurately obtained, avoiding the collection and transmission of unnecessary data, improving the efficiency and targeting of data collection, while reducing the bandwidth occupation of data transmission and reducing the consumption of communication resources and vehicle processing resources.

[0138] Step 2: Obtain real-time perception data of the vehicle's exterior based on the data collected by the target camera.

[0139] Based on the mapping table between viewing type and data format, the data format corresponding to the viewing type is determined. Then, data reflecting the real-time situation in a specific area outside the vehicle is collected by the target camera, resulting in data with the same format as the viewing type, thus obtaining the vehicle's external situation perception data.

[0140] For example, if the viewing type is an image view of the area behind the vehicle, the data format corresponding to the viewing type is determined to be image based on the viewing type and data format mapping table. Then, image data is selected from the data collected by the target camera to obtain real-time external perception data.

[0141] Optionally, the data collected by the target camera can be anonymized to obtain real-time perception data of the vehicle's external environment.

[0142] After the target camera collects the data, the data processing unit inside the vehicle performs data anonymization processing and uses the anonymized data as the real-time perception data outside the vehicle.

[0143] By anonymizing data within the vehicle, the privacy of occupants and the vehicle itself can be effectively protected, preventing sensitive information from being leaked during data transmission and processing, and improving data security and compliance.

[0144] In one possible implementation, the triggering conditions for real-time viewing of the external environment include: receiving and responding to a data request instruction sent by a client. The data request instruction is generated by the cloud server in response to the client's real-time viewing instruction and is used to instruct the cloud server to transmit the real-time perception data of the external environment corresponding to the real-time viewing instruction to the cloud server.

[0145] When a user initiates a live view command on the client, the client sends the command to the cloud server. Upon receiving the command, the cloud server generates a data request command and sends it to the vehicle.

[0146] After receiving the data request command, the vehicle triggers real-time viewing of the external situation and initiates the corresponding external situation perception data collection and transmission process, transmitting the external situation perception data to the cloud server in real time.

[0147] Figure 5This is a schematic diagram of the structure of the vehicle exterior real-time optimization device provided in the embodiments of this application, as shown below. Figure 5 As shown, the vehicle exterior real-time optimization device 50 provided in this embodiment is deployed on a cloud server and includes:

[0148] The acquisition module 501 is used to acquire the real-time external situation perception data of the vehicle during the real-time viewing of the vehicle's external situation.

[0149] The processing module 502 is used to perform quality optimization processing on the vehicle exterior real-time perception data by calling the target model to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format of the vehicle exterior real-time perception data. The optimized real-time data is sent to the client, which is used to provide the vehicle's real-time external condition viewing function based on the optimized real-time data.

[0150] In one possible implementation, the processing module 502 is specifically used for:

[0151] If the viewing type of the real-time view of the outside situation is panoramic view, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing and data fitting processing to obtain the optimized real-time view data from the bird's-eye view.

[0152] If the viewing type of the real-time view of the external situation is image viewing or video viewing, the real-time view perception data is input into the corresponding target model. In the target model, the real-time view perception data is subjected to quality optimization processing to obtain optimized real-time data.

[0153] The quality optimization process includes brightness adjustment and / or color optimization.

[0154] In one possible implementation, the real-time external perception data is anonymized data.

[0155] In one possible implementation, the triggering conditions for real-time viewing of the external environment include:

[0156] Receive and respond to commands from the client to view the real-time situation outside the vehicle;

[0157] Correspondingly, the processing module 502 is also used to: send a data request instruction corresponding to the vehicle's external real-time viewing instruction to the vehicle. The data request instruction is used to instruct the cloud server to transmit the external real-time perception data corresponding to the vehicle's external real-time viewing instruction in real time.

[0158] In one possible implementation, the processing module 502 is further configured to:

[0159] Based on the optimized real-time data, the anomaly detection model is invoked to perform environmental anomaly detection, and the environmental anomaly detection results corresponding to the vehicle are obtained.

[0160] If the environmental anomaly detection results indicate that there are abnormal conditions in the environment around the vehicle, an alarm message will be generated for the abnormal conditions.

[0161] Send alarm information to the client.

[0162] The vehicle exterior real-time optimization device 50 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0163] Figure 6 This is a schematic diagram of the structure of the vehicle exterior real-time optimization device provided in the embodiments of this application, as shown below. Figure 6 As shown, the vehicle exterior real-time optimization device 60 provided in this embodiment is deployed in a vehicle and includes:

[0164] The acquisition module 601 is used to acquire real-time external situation perception data during the real-time viewing of the vehicle's external situation.

[0165] The processing module 602 is used to send the vehicle exterior real-time perception data to the cloud server. The cloud server is used to call the target model to perform quality optimization processing on the vehicle exterior real-time perception data and send the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format based on the data format of the vehicle exterior real-time perception data. The client is used to provide the vehicle exterior real-time viewing function based on the optimized real-time data.

[0166] In one possible implementation, the acquisition module 602 is specifically used for:

[0167] Based on the viewing type of real-time view of the outside of the vehicle, the target camera corresponding to the viewing type is matched among multiple cameras;

[0168] Based on the data collected by the target camera, real-time perception data of the vehicle's external environment is obtained.

[0169] In one possible implementation, the acquisition module 602 is further configured to:

[0170] The data collected by the target camera is anonymized to obtain real-time perception data of the vehicle's external environment.

[0171] In one possible implementation, the triggering conditions for real-time viewing of the external environment include: receiving and responding to a data request instruction sent by a client. The data request instruction is generated by the cloud server in response to the client's real-time viewing instruction and is used to instruct the cloud server to transmit the real-time perception data of the external environment corresponding to the real-time viewing instruction to the cloud server.

[0172] The vehicle exterior real-time optimization device 60 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0173] Figure 7 This is a schematic diagram of the structure of a cloud server provided in an embodiment of this application. Figure 7 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0174] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 501 to perform the above-described method.

[0175] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0176] 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.

[0177] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0178] 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.

[0179] This application also provides a vehicle, including a vehicle capable of implementing the above-described method.

[0180] This application provides an exterior real-time optimization system, including: a cloud server as described in various possible embodiments of this application, and a vehicle as described in various possible embodiments of this application.

[0181] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0182] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0188] 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 application, in essence, 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 application. 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.

[0189] 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.

[0190] Finally, it should be noted that other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application 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 alterations may be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for optimizing real-time conditions outside a vehicle, characterized in that, Applied to cloud servers, including: During the real-time observation of the vehicle's external conditions, the vehicle acquires real-time external condition perception data. Based on the real-time external perception data, the target model is invoked to perform quality optimization processing on the real-time external perception data to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format based on the data format of the real-time external perception data. The optimized real-time data is sent to the client, which is used to provide a real-time viewing function of the vehicle's exterior based on the optimized real-time data.

2. The method for optimizing vehicle exterior conditions according to claim 1, characterized in that, The step of calling a target model to perform image optimization processing on the real-time external perception data based on the vehicle exterior perception data to obtain optimized real-time data includes: If the real-time view of the external situation is a panoramic view, the real-time external situation perception data is input into the corresponding target model. In the target model, the real-time external situation perception data is subjected to quality optimization processing and data fitting processing to obtain optimized real-time data from a bird's-eye view. If the viewing type of the real-time view of the external situation is image viewing or video viewing, the real-time view data is input into the corresponding target model. In the target model, the real-time view data is subjected to quality optimization processing to obtain optimized real-time data. The quality optimization process includes brightness adjustment and / or color optimization.

3. The method for optimizing vehicle exterior conditions according to claim 1 or 2, characterized in that, The external real-time perception data is anonymized.

4. The method for optimizing vehicle exterior conditions according to claim 1 or 2, characterized in that, The triggering conditions for real-time viewing of the external environment include: Receive and respond to the real-time outside view command sent by the client; Correspondingly, the method for optimizing the external real-time situation further includes: sending a data request instruction corresponding to the external real-time situation viewing instruction to the vehicle, wherein the data request instruction is used to instruct the cloud server to transmit the external real-time situation perception data corresponding to the external real-time situation viewing instruction in real time.

5. The method for optimizing vehicle exterior conditions according to claim 1 or 2, characterized in that, Also includes: Based on the optimized real-time data, the anomaly detection model is invoked to perform environmental anomaly detection, and the environmental anomaly detection result corresponding to the vehicle is obtained. If the environmental anomaly detection result indicates that there is an abnormal situation in the vehicle's surrounding environment, an alarm message is generated for the abnormal situation; The alarm information is sent to the client.

6. A method for optimizing real-time conditions outside a vehicle, characterized in that, Applied to vehicles, including: During the real-time observation of the vehicle's external conditions, real-time external condition perception data is acquired. The system sends the real-time external situation perception data to a cloud server. The cloud server is used to call a target model to perform quality optimization processing on the real-time external situation perception data and send the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format of the real-time external situation perception data. The client is used to provide the real-time external situation viewing function of the vehicle based on the optimized real-time data.

7. The method for optimizing vehicle exterior conditions according to claim 6, characterized in that, The real-time acquisition of external situation perception data includes: Based on the viewing type of real-time view of the outside of the vehicle, the target camera corresponding to the viewing type is matched among multiple cameras; The real-time perception data outside the vehicle is obtained based on the data collected by the target camera.

8. The method for optimizing vehicle exterior conditions according to claim 6 or 7, characterized in that, The step of obtaining the real-time external perception data based on the data collected by the target camera includes: The data collected by the target camera is anonymized to obtain the real-time perception data of the vehicle exterior.

9. The method for optimizing vehicle exterior conditions according to claim 6 or 7, characterized in that, The triggering conditions for real-time viewing of the external environment include: The cloud server receives and responds to a data request instruction sent by the client. The data request instruction is generated by the cloud server in response to the real-time external view instruction sent by the client. The data request instruction is used to instruct the cloud server to transmit the real-time external view perception data corresponding to the real-time external view instruction to the cloud server in real time.

10. A vehicle exterior real-time optimization device, characterized in that, Deployed on cloud servers, including: The acquisition module is used to acquire the real-time external situation perception data of the vehicle during the real-time viewing of the vehicle's external situation. The processing module is used to perform quality optimization processing on the vehicle exterior real-time perception data by calling a target model to obtain optimized real-time data. The target model is a visual model selected from multiple visual models that matches the data format based on the vehicle exterior real-time viewing data format. The optimized real-time data is then sent to a client, which is used to provide the vehicle's real-time exterior real-time viewing function based on the optimized real-time data.

11. A vehicle exterior real-time optimization device, characterized in that, Deployed in vehicles, including: The acquisition module is used to acquire real-time external situation perception data during the real-time viewing of the vehicle's external situation. The processing module is used to send the vehicle exterior real-time perception data to the cloud server. The cloud server is used to call the target model to perform quality optimization processing on the vehicle exterior real-time perception data and send the optimized real-time data to the client. The target model is a visual model selected from multiple visual models that matches the data format based on the vehicle exterior real-time viewing data format. The client is used to provide the vehicle exterior real-time viewing function based on the optimized real-time data.

12. A cloud server, characterized in that, The cloud server is capable of executing the method of any one of claims 1-5, comprising: a memory and a processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-5.

13. A vehicle, characterized in that, include: The vehicle is capable of performing the method according to any one of claims 6-9.

14. A vehicle exterior real-time optimization system, characterized in that, include: The cloud server as claimed in claim 12, and the vehicle as claimed in claim 13.

15. 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 method as described in any one of claims 1-9.

16. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-9.

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