Data processing method and related equipment

Abstract

The embodiment of the invention provides a data processing method and related equipment. The method comprises the steps that a driving picture collected in the driving process of a first vehicle is acquired; the driving picture is obtained by calling an image acquisition module and shooting at a driving view angle of the first vehicle; performing object analysis processing on the driving picture to obtain a target object; the target object is an object which affects the driving safety of the first vehicle; monitoring the state of the target object according to the driving picture to obtain a state monitoring result; and determining the driving safety of the first vehicle according to the state monitoring result. Through the embodiment of the invention, the driving safety of the vehicle can be more accurately determined, and the method and the device can be suitable for any type of vehicles and are high in expandability.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a data processing method and related devices. Background Art

[0002] With the development of the times, many means of transportation are equipped with intelligent algorithms with assisted driving functions to ensure the driving safety of the means of transportation during the traffic process. For example, some intelligent algorithms can monitor whether the driver is fatigued and monitor the driving speed of the means of transportation, thereby reducing the probability of accidents of the means of transportation and improving driving safety. However, some of the existing assisted driving algorithms have limited applicable scenarios and can only be applied to fixed types of means of transportation. Some means of transportation do not have suitable assisted driving algorithms to better ensure the operation safety of the means of transportation, and the accuracy of the existing assisted driving algorithms for the driving safety of the means of transportation still needs to be improved. Summary of the Invention

[0003] Embodiments of this application provide a data processing method and related devices, which can more accurately determine the driving safety of a means of transportation and can be applicable to any type of means of transportation, with strong scalability.

[0004] On the one hand, embodiments of this application provide a data processing method, which includes:

[0005] Obtain a driving picture collected during the driving process of a first means of transportation; the driving picture is obtained by calling an image acquisition module and shooting from the driving perspective of the first means of transportation;

[0006] Perform object analysis and processing on the driving picture to obtain a target object; the target object refers to an object that affects the driving safety of the first means of transportation;

[0007] Monitor the state of the target object according to the driving picture to obtain a state monitoring result;

[0008] Determine the driving safety of the first means of transportation according to the state monitoring result.

[0009] On the one hand, embodiments of this application provide a data processing device, which includes:

[0010] An acquisition unit, configured to obtain a driving picture collected during the driving process of a first means of transportation; the driving picture is obtained by calling an image acquisition module and shooting from the driving perspective of the first means of transportation;

[0011] A processing unit, configured to perform object analysis and processing on the driving picture to obtain a target object; the target object refers to an object that affects the driving safety of the first means of transportation;

[0012] The processing unit is further configured to monitor the state of the target object according to the driving picture to obtain a state monitoring result;

[0013] The processing unit is further configured to determine the driving safety of the first vehicle according to the state monitoring result.

[0014] On the one hand, an embodiment of the present application provides a computer device, which includes:

[0015] A processor suitable for executing a computer program;

[0016] A computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the above data processing method is implemented.

[0017] On the one hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is loaded and executed by the processor to implement the above data processing method.

[0018] On the one hand, an embodiment of the present application provides a computer program product, which includes a computer program or computer instructions, and when the computer program or computer instructions are executed by the processor, the above data processing method is implemented.

[0019] In the embodiment of the present application, a driving picture collected during the driving of the first vehicle can be obtained. The driving picture is obtained by calling an image acquisition module and shooting from the driving perspective of the first vehicle. That is to say, the driving picture is taken from the first perspective of the first vehicle and during the driving of the first vehicle, and the driving picture can be used as the analysis basis for the assisted driving of the first vehicle. Then, object analysis processing can be performed on the driving picture to obtain a target object, which refers to an object that affects the driving safety of the first vehicle. Further, the state of the target object can be monitored according to the driving picture to obtain a state monitoring result, and finally the driving safety of the first vehicle can be determined according to the state monitoring result. In this way, through the analysis and recognition of the driving picture, any object that affects the driving safety of the first vehicle can be analyzed as a target object, and each target object can be used as an object to be monitored. By monitoring the state of the target object, the impact on the driving safety of the first vehicle can be known, so as to more accurately determine the driving safety of the first vehicle. Moreover, no matter what type of vehicle the first vehicle is, the driving safety can be determined through the above process, which has strong scalability and a wide range of applications. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1a It is an architecture diagram of a data processing system provided by an embodiment of the present application;

[0022] Figure 1b It is a schematic structural diagram of a video stream module provided by an embodiment of the present application;

[0023] Figure 2 It is a schematic flowchart of a data processing method provided by an embodiment of the present application;

[0024] Figure 3 It is a schematic structural diagram of a simulation security algorithm module provided by an embodiment of the present application;

[0025] Figure 4 It is a schematic flowchart of another data processing method provided by an embodiment of the present application;

[0026] Figure 5 It is a schematic structural diagram of a target detection model provided by an embodiment of the present application;

[0027] Figure 6 It is a schematic diagram of a traffic scene provided by an embodiment of the present application;

[0028] Figure 7 It is a schematic flowchart of another data processing method provided by an embodiment of the present application;

[0029] Figure 8 It is a schematic structural diagram of a data processing device provided by an embodiment of the present application;

[0030] Figure 9 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0031] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0032] This application proposes a data processing method that can capture driving images captured by an image acquisition module from the driving perspective of a first vehicle during its travel. By capturing these driving images, the driving environment of the first vehicle in a traffic scene can be recorded in real time, providing a basis for analyzing the driving safety of the first vehicle. Subsequently, object analysis processing can be performed on the driving images to obtain target objects within the driving images. This allows analysis of at least one target object near the first vehicle that may affect its driving safety. Each target object can be considered a monitoring target, and the state of the target object can be monitored based on the driving images to obtain a state monitoring result. The driving safety of the first vehicle can then be determined based on the state monitoring result. State monitoring can determine the impact of the target object's state on the driving safety of the first vehicle, thereby more accurately determining the driving safety of the first vehicle. Furthermore, this solution is applicable to any type of vehicle, has strong scalability, and a wide range of applicability.

[0033] The so-called means of transport refers to a tool used for human travel or transportation. According to the use scenario of the means of transport, the means of transport can include three categories: land, sea and air, such as bicycles, motorcycles, electric vehicles, cars and other means of transport used on land, ships, submarines and other means of transport used at sea, and aircraft and other means of transport used in the air. According to the driving method of the means of transport, the means of transport can include riding vehicles and non-riding vehicles. Riding vehicles include bicycles, motorcycles, electric vehicles, and non-riding vehicles include cars, trucks, trains and so on. According to the starting method of the means of transport, it can include motor vehicles and non-motor vehicles. Motor vehicles include cars, motorcycles and so on, and non-motor vehicles include bicycles and electric vehicles. In the embodiment of the present application, the first means of transport refers to any means of transport, such as the first means of transport is a riding vehicle such as an electric vehicle or motorcycle. Optionally, the first means of transport also has at least the following properties: the first means of transport shares the same traffic scene with other means of transport; the driving of the first means of transport is easily interfered with by target objects in the traffic scene.

[0034] The so-called driving image refers to a frame of image used to depict the traffic scene during driving. Since the driving image is captured from the perspective of the first vehicle, the traffic scene is observed from the perspective of the first vehicle. The driving image may include moving objects participating in traffic in the traffic scene around the first vehicle, such as vehicles and pedestrians, and also includes other static objects in the traffic scene, such as lane lines and traffic signs of the traffic road. In this application, the driving image may be a frame of video image in the driving video recorded by the image acquisition module, or a picture taken by the image acquisition module at regular intervals. The type of the driving image and the number of driving images obtained are not limited in this application.

[0035] The technical solution provided in the embodiments of this application can be applied to the traffic scenes of various vehicles, such as vehicle driving scenes. In the vehicle driving scene, through the solution provided in this application, the captured driving image can be used as the image to be analyzed, and then image recognition can be performed based on the driving image, and then the state monitoring can be performed based on the recognized target object, so as to determine the driving safety of the vehicle based on the state monitoring result to ensure the driving safety of the vehicle on the traffic road.

[0036] Figure 1a It is the architecture diagram of a data processing system provided in the embodiments of this application. As Figure 1a shown, the data processing system includes a data processing device 100 and a service device 101. The data processing device 100 is used to execute the data processing method provided in the embodiments of this application. The data processing device 100 may be a computer device with a shooting function and a data processing function, and the data processing device includes an image acquisition module, such as a vehicle driving record module or other video recording modules that can take pictures. The image acquisition module supports recording the picture during the driving process of the first vehicle, and then obtaining the driving video. The driving video is used to record the road conditions during the driving process of the first vehicle, such as whether the road is slippery and whether there is a lot of traffic on the road. In one implementation, if the data processing device 100 receives a viewing instruction for the driving video, then the data processing device 100 can perform playback processing on the driving video. Based on different types of viewing instructions, the playback processing can be real-time viewing or playback viewing of the video. If the viewing instruction is a real-time viewing instruction, then during the playback processing, the currently recorded driving video can be played in real time, so as to support real-time viewing of the road conditions. If the viewing instruction is a playback viewing instruction, then during the playback processing, the driving video can be played back, that is, it supports video playback after the event.

[0037] In a feasible implementation, the video stream acquisition module includes an image acquisition module. The video stream acquisition module can pull various codec video streams such as IOT (Internet of Things), national standards (i.e., international standards), and Onvif (Open Network Video Interface Forum), and can be compatible with existing video coding formats (such as High Efficiency Video Coding HEVC, Audio Video coding Standard AVS), which can reduce the algorithm access cost. For videos in any video coding format, they can be accessed and analyzed based on the accessed videos. For the structure of the video stream acquisition module, reference can be made to Figure 1b , and the video stream acquisition module includes an image acquisition module, a video gateway, and Internet of Things devices. Among them, the image acquisition module can include at least one network camera (IP camera). A network camera is a new generation of camera produced by combining traditional cameras with network technology. In addition to the image capture function of general traditional cameras, it also has some built-in systems and devices, enabling video data to be sent to terminal devices through the network after being encrypted and compressed. Internet of Things devices include, but are not limited to: Internet of Things access devices (such as welink iot access), message management devices (such as welink iot message), and video streaming devices (such as welinkvedio push). The video gateway includes at least one of the following processes: video acquisition process, message monitoring process, and device status monitoring process. The video acquisition process can be used to monitor the video acquisition progress of the camera, and the message monitoring process is used to monitor whether there are new messages to be sent or received. The device status monitoring process is used to monitor the status of the camera or the status of the video gateway itself. The video gateway also includes a network SDK. The gateway SDK (Software Development Kit) can provide at least one of the following functions: message monitoring, registration and login, message processing callback, and message sending. The video gateway also includes the following functions: detection, multicast monitoring, task allocation, and voting. For the content of the above several parts in the video stream acquisition module, schematically, the following interaction process can be included: During video acquisition, the video data collected by the network camera can be sent to the Internet of Things device through the video gateway, so that the video streaming module in the Internet of Things device can stream the video data to the data processing module, and the data processing module analyzes the video data. If the message management device receives some messages or instructions, they can be sent to the network camera through the video gateway, and the network camera performs corresponding operations according to the instructions. For example, when receiving an instruction to adjust the shooting angle, it can be sent to the network camera by the message monitoring process of the video gateway, and then the network camera can automatically adjust the shooting angle.

[0038] The service device 101 refers to a target device that supports linkage with the first vehicle. The target device can be an item that the first vehicle needs to carry when traveling. For example, if the first vehicle is a motorcycle or an electric vehicle, the service device 101 can be a helmet. It can also be other computer devices, such as a mobile terminal (such as a smart phone or a laptop). The data processing device 100 can call the image acquisition module to capture the driving picture during the driving of the first vehicle, and the data processing device 100 can process the collected driving picture in real time. Specifically, it can perform object analysis on the driving picture, identify the target object in the driving picture, and then monitor the status of the target object according to the driving picture. The driving safety of the first vehicle is determined based on the status monitoring results obtained by monitoring, so as to provide timely warning prompts when the driving safety of the first vehicle is low.

[0039] In one implementation, the driving safety of the first vehicle can be used to indicate whether the first vehicle has a driving safety risk. Optionally, if the driving safety of the first vehicle indicates that the first vehicle has a driving safety risk, indicating that the driving safety of the first vehicle is low, then the data processing device 100 can send an alarm message to the service device 101 to warn of the driving safety risk of the first vehicle. In this way, the first vehicle can link with the target device to issue an alarm prompt to issue an early warning prompt of possible driving safety risks, thereby enhancing the driving safety of the first vehicle. For example, the first vehicle is a bicycle and the service device 101 is a helmet. If there is a risk of collision between the bicycle and other vehicles, the data processing device can send an alarm message to the helmet, and a voice alarm prompt can be output through the helmet to warn that the currently driven bicycle may collide with other vehicles. The voice prompt through the helmet can reduce the interference of external noise.

[0040] The above-mentioned computer devices may include either or both of terminals or servers. Terminals include but are not limited to: smart phones, tablet computers, smart wearable devices, smart voice interaction devices, smart home appliances, personal computers, vehicle-mounted terminals, smart cameras, virtual reality devices, etc., and this application does not impose any restrictions on this. This application does not impose any restrictions on the number of terminals. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, but is not limited to this. This application does not impose any restrictions on the number of servers.

[0041] The data processing system provided by the embodiments of the present application can collect, in real time, a driving image of a first vehicle from its own first perspective during driving through an image acquisition module in the data processing device. By identifying and analyzing the driving image, a target object that affects the driving safety of the first vehicle in the traffic scene where the first vehicle is located can be obtained. Furthermore, by monitoring the state of the target object based on the driving image, a state monitoring result can be obtained to determine the driving safety of the first vehicle. Throughout the process, a target object that may affect the safety of the vehicle during driving is analyzed based on the driving image observed from the perspective of the first vehicle, and the state of the target object is continuously monitored using the driving image, so as to accurately determine the safety conditions of the vehicle during driving. Moreover, if the driving image includes multiple target objects, the state of each target object can be detected, thereby improving the accuracy of driving safety and comprehensively ensuring the safe operation of the vehicle. Further, the data processing device can also be linked with a service device, so as to give an alarm prompt in a timely manner through the service device when it is detected that the first vehicle has a driving safety risk or other abnormalities, thereby assisting the first vehicle in safe driving and better ensuring road traffic safety.

[0042] The data processing method provided by the embodiments of the present application relates to artificial intelligence technology, specifically computer vision processing technology. Computer vision technology (Computer Vision, CV) is a science that studies how to make machines "see". Further, it refers to using cameras and computers to replace human eyes for object recognition, measurement, and other machine vision, and further performing graphic processing to make the computer process images more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. The large model technology has brought important changes to the development of computer vision technology. Pretrained models in the visual field such as swin-transformer, ViT, V-MOE, and MAE can be quickly and widely applied to downstream specific tasks after fine tuning. Computer vision technology usually includes technologies such as image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and also includes common biometric recognition technologies such as face recognition and fingerprint recognition. In this application, various processes performed on the driving image involve technologies such as image processing and image recognition. Through these technologies, the target object can be accurately identified and the state of the target object can be monitored, so as to improve the accuracy of the driving safety of the first vehicle.

[0043] It should be noted that in this application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. It should be understood that there is no logical or chronological dependency among "first", "second", and "nth", nor are the quantity and execution order limited. In this application, the term "at least one" means one or more, and the meaning of "multiple" means two or more.

[0044] In this application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the functions of that module or unit.

[0045] The data processing method provided by the embodiments of this application will be elaborated in detail below.

[0046] Please refer to Figure 2 , which is a schematic flowchart of a data processing method provided by the embodiments of this application. This data processing method can be executed by a computer device (such as Figure 1a the data processing device 100 in the data processing system shown), and this data processing method may include the content described in the following S201 - S204.

[0047] S201, Obtain the driving images collected during the driving of the first vehicle.

[0048] The driving images are obtained by calling an image acquisition module and shooting from the driving perspective of the first vehicle. The driving images are mainly used to reflect the driving environment of the first vehicle, that is, the situation of the traffic scene where the first vehicle is located, such as the road conditions and traffic flow conditions when the vehicle is driving. Since the shooting perspective is the driving perspective of the first vehicle, that is, the driving perspective of the first vehicle is used as the first perspective to collect the driving images, therefore, the first vehicle may not be included in the driving images, that is, the driving images include elements other than the first vehicle in the traffic scene where the first vehicle is located, such as other vehicles with the same driving direction as the first vehicle, pedestrians in front of the first vehicle, foreign objects on the road surface of the traffic road, traffic signs and traffic lights on the traffic road, and so on.

[0049] In one implementation, during the driving of the first vehicle, the computer device can call the image acquisition module to collect the driving images in real time. After the image acquisition module collects the driving images, the image acquisition module can send the driving images to the data processing module in the computer device, or the data processing module can obtain the driving images from the image acquisition module in real time. In this way, the data processing module can obtain the driving images in real time, reduce the latency of driving image analysis, thereby improving the determination speed of the driving safety of the first vehicle, and giving an alarm prompt in time when there is a driving safety risk for the first vehicle. Optionally, the image acquisition module is provided in a computer device with a shooting function, and the computer device can be the computer device executing the embodiments of the present application, such as a shooting device with data processing functions. The computer device can be fixed in at least one of the following ways: fixed on the first vehicle, fixed on the driver of the first vehicle, and fixed on the items that need to be carried during the driving of the first vehicle. For example, if the first vehicle is an electric vehicle, the shooting devices for collecting driving images can include a gopro (a shooting device) fixed on the electric vehicle, a camera fixed on the helmet carried by the driver, a camera fixed on the driver, etc. These devices can access the data processing method provided by the present application. The data processing module can be the processor in the computer device, and the data processing module has the following functions: analyzing the driving images, monitoring the state of the target object, and notifying and prompting the driving safety of the first vehicle in the traffic scene.

[0050] In a feasible implementation, the driving images collected by the image acquisition module can be obtained by shooting a traffic scene, and the traffic scene includes a real traffic scene or a simulated traffic scene. Among them, the simulated traffic scene can be a completely virtual traffic scene simulated by a simulation system (scenario-based simulation), or a virtual traffic scene reproduced by collecting the scene data in the real traffic scene (replay-based simulation), or a semi-simulated and semi-realistic scene implemented by augmented reality technology, such as superimposing virtual objects (such as virtual vehicles) in the real traffic scene to create an augmented reality traffic scene. In the simulated traffic scene, the real traffic scene can be data-modeled by simulating weather conditions, road conditions, vehicle conditions, etc., and the data can be input into the simulation system for detection, thereby further improving the processing accuracy of the present solution.

[0051] S202. Perform object analysis and processing on the driving images to obtain the target object.

[0052] The target object refers to an object that affects the driving safety of the first vehicle. Based on different states of the target object, the impact on the driving safety of the first vehicle varies. Optionally, the influence can be used to represent the magnitude of the impact of the target object on the driving safety of the first vehicle. It is understandable that if the influence of the target object on the driving safety of the first vehicle is zero, it indicates that the target object has no impact on the driving safety of the first vehicle. If the influence of the target object on the driving safety of the first vehicle is less than the preset influence, it indicates that the impact of the target object on the driving safety of the first vehicle is relatively small.

[0053] In a specific implementation, the computer device can identify each object in the driving video and determine the object that meets the preset requirements as the target object from the identified objects. Classified by function, the target object can be a traffic participant object, such as other vehicles, pedestrians, etc. The target object can also be an object that provides a traffic passage basis for traffic participant objects, such as a traffic road. Classified by static and dynamic attributes, the target object can be a moving target or a static object. A moving target refers to an object that moves in the traffic scene where the first vehicle is located and can affect the driving safety of the first vehicle, such as other vehicles (e.g., cars, motorcycles) in the traffic scene, or other dynamic objects (e.g., pedestrians) in the traffic scene; a static object refers to an object that is stationary in the traffic scene, such as an objectively existing and stationary object in the driving environment, such as a road sign, a traffic road, a green belt, etc.

[0054] S203, monitor the state of the target object according to the driving video to obtain the state monitoring result.

[0055] The state of the target object refers to the static or dynamic condition of the target object in the traffic scene. For example, if the target object is a traffic road, the state of the traffic road includes static conditions such as the height difference of the road surface. Another example is that if the target object is a car, the state of the car includes dynamic conditions such as the driving speed of the car. The driving safety of the first vehicle is affected by the state of the target object. For example, the state of the target object may reduce the driving safety of the first vehicle.

[0056] In one implementation, when the state of the target object changes, the driving safety of the first vehicle can change accordingly. For example, if the target object is a traffic road and there is snow accumulation on the road surface, since the snow accumulation on the road surface can cause problems such as skidding during the driving of the first vehicle, the snow accumulation on the road surface has a greater impact on the driving safety of the first vehicle, specifically reducing the driving safety of the first vehicle. When the road surface of the traffic road changes from snow accumulation to dryness, then the traffic road is conducive to the driving of the first vehicle, so the impact on the driving safety of the first vehicle is relatively small. At this time, the driving safety of the first vehicle is higher than that when there is snow accumulation on the road surface. Therefore, the computer device can monitor the state of the target object based on the driving picture, so as to determine the driving safety of the first vehicle based on the monitoring result.

[0057] During the state monitoring process, the monitoring can be implemented based on at least one frame of driving picture. If the target object is detected in a certain frame, starting from the first frame of the driving picture where the target object is located, the target object can be continuously monitored based on this driving picture and the subsequent consecutive frames of driving pictures, and then the state monitoring result of the target object can be obtained. The state monitoring result can be used to indicate the state of the target object. Based on different types of target objects, the state here can be the driving state or the road surface condition. If the target object is a moving object, such as a vehicle like a car, then the state monitoring result can include the driving state of the target object, and this driving state can be used to indicate whether the target object is in a moving state or a stationary state. The state monitoring result can also include the movement trajectory of the target object, etc. If the target object is a static object, such as a traffic road, then the state monitoring result of the traffic road can be used to indicate the road conditions of the traffic road, such as the traffic flow in the traffic road, whether there are height differences on the road surface of the traffic road, whether there are foreign objects on the road surface of the traffic road, and so on. By monitoring the state of the target object, on the one hand, the real-time state of the target object can be obtained, and on the other hand, the future state of the target object can also be predicted, so as to more intelligently assist the driving of the first vehicle. For example, if the first vehicle is an electric vehicle and the target object is a car, by monitoring the speed and position of the car, the driving trajectory of the car can be predicted, and then it can be predicted when the car will stop and start, etc., so as to provide some suggestions for the driving of the electric vehicle.

[0058] S204. Determine the driving safety of the first vehicle according to the state monitoring result.

[0059] In a specific implementation, the state of the target object indicated by the state monitoring result matches the driving safety of the first vehicle. For the same target object, the impact on the driving safety of the first vehicle is different in different states; for different target objects, the impact on the driving safety of the first vehicle can also be different in corresponding states. For example, if the first vehicle is an electric vehicle and it is detected from the driving video that a car appears in front of the electric vehicle, then the running state of the car can be monitored to determine whether the driving behavior of the car will pose a driving risk to the current vehicle. If it is monitored that the car will suddenly start when the electric vehicle approaches, it can be determined that the car has a greater impact on the driving safety of the electric vehicle and the driving safety of the electric vehicle is lower. If it is monitored that the car is driving normally and there is no possibility of collision with the electric vehicle, it can be determined that the car will not have too much impact on the driving safety of the electric vehicle.

[0060] In this application, the driving safety is determined through the state monitoring result, including predicting the driving safety risk. The driving safety can be represented by a risk level. The higher the risk level, the higher the possibility of the occurrence of the driving safety risk and the lower the driving safety; the lower the risk level, the lower the possibility of the occurrence of the driving safety risk and the higher the driving safety. Through the prediction of the driving safety risk, an alarm can be issued for the driving safety risk and other devices can be linked for voice prompts, so as to be able to timely notify the driver of some safety risks that may occur during driving.

[0061] In one implementation manner, the data processing method provided in this application can be the implementation of one or more safety detection algorithms. The safety detection algorithms can be used to detect the target object, monitor the state of the target object, and determine the driving safety of the first vehicle. Optionally, the safety detection algorithms can be optimized by means of simulation safety algorithms, and the simulation safety algorithms can be implemented by a dedicated simulation safety algorithm module. The following combines Figure 3 With the provided structural schematic diagram of the simulation safety algorithm module, the simulation safety algorithm is introduced. The simulation safety algorithm module includes the following components: simulation platform service, data access and scenario construction, simulation core engine, test analysis and evaluation, external access interface, test task management, and the dependent database.

[0062] Among them, taking the first vehicle as an example, the simulation platform service can provide the following types of simulations: scenario-based simulation, playback-based simulation, and virtual city-based simulation. Moreover, the simulation platform service can also provide the following test environments: Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), Processor-in-the-Loop (PiL), Hardware-in-the-Loop (HiL), Vehicle-in-the-Loop (ViL), Driver-in-the-Loop (DiL), etc. In the embodiments of the present application, scenario-based simulation and playback-based simulation can be used for detail optimization. The data access and scenario construction module can provide the following management and editing functions: scenario management, map management, scenario editor, and map editor, etc. Through the scenario editor and map editor, traffic scenarios and maps can be edited to implement the scenario content required for the vehicle to travel. And the data access and scenario construction module can send the data of the real traffic scenario to the simulation platform service, and the simulation platform service performs data modeling, so as to input the corresponding data in the safety detection algorithm into the system for detection. The simulation core engine includes a scheduling system, a vehicle dynamics model, a sensor model, a driver model, a high rendering engine, traffic flow simulation, Vehicle-to-Everything (V2X) simulation, and so on. The data managed by the data access and scenario construction can also be sent to the simulation core engine. The simulation core engine can render the driving trajectory of the vehicle based on the scenario data or map data, etc., in combination with the specific test task and the data transmitted by the external access interface. And the data simulated by the simulation core engine can also be sent to the simulation platform service for testing through different test environments. The test analysis and evaluation module can send the test analysis data to the simulation platform service to adjust the simulation environment for testing. Moreover, the channel (Pass) dependencies include various required database tools, including but not limited to: K8Sserver / client (k8s database), Redis, MySQL, MongoDB, Kafka, spark. These tools can not only provide storage services but also provide computing services to assist the vehicle safety detection algorithm in calculating data or obtaining the data required during use.

[0063] The data processing method provided by the embodiment of the present application can obtain a driving image collected during the driving of a first vehicle. The driving image is captured by calling an image capture module from the driving perspective of the first vehicle. That is to say, the driving image is taken from the first perspective of the first vehicle and during the driving of the first vehicle, and this driving image can be used as the analysis basis for the assisted driving of the first vehicle. Subsequently, object analysis processing can be performed on the driving image to obtain a target object, which refers to an object that affects the driving safety of the first vehicle. Further, the state of the target object can be monitored based on the driving image to obtain a state monitoring result, and finally, the driving safety of the first vehicle can be determined according to the state monitoring result. In this way, through the analysis and recognition of the driving image, any object that affects the driving safety of the first vehicle can be analyzed as a target object, and each target object can be used as an object to be monitored. By monitoring the state of the target object, the impact on the driving safety of the first vehicle can be known, so as to more accurately determine the driving safety of the first vehicle. Moreover, regardless of the type of the first vehicle, the driving safety can be determined through the above process, which has strong scalability and a wide range of applications.

[0064] Please refer to Figure 4 , which is a schematic flowchart of another data processing method provided by the embodiment of the present application. This data processing method can be executed by a computer device (such as Figure 1a the data processing device 100 in the data processing system shown) and can include the content described in the following S401 - S406.

[0065] S401, obtain a driving image collected during the driving of a first vehicle.

[0066] In a feasible embodiment, a first vehicle is associated with a direction control module, and the direction control module is used to control the shooting angle of an image acquisition module. For example, by controlling the shooting angle, the image acquisition module can capture the driving scene directly behind the first vehicle. In this application, the direction control module can be a PTZ (Pan / Tilt / Zoom, which means the pan-tilt head moves omnidirectionally (left / right / up / down) and the lens zooms in and out, and can be simply referred to as pan-tilt control). To avoid the driving record module using a fixed camera and only recording the driving information of the vehicle from a fixed angle, unable to change the picture according to the driving situation of the first vehicle to provide more detailed driving safety information. In the embodiment of this application, through the control function of the shooting angle of the direction control module, the operation of the image acquisition module can be controlled in conjunction with the driving operation of the first vehicle. Therefore, the logic for obtaining the driving scene can include the content shown in steps ① - ③ below.

[0067] Step ①: Detect the lane change requirement of the first vehicle.

[0068] In one implementation, the lane change requirement of the first vehicle can be detected according to the driving operation of the first vehicle. Among them, the driving operation is an operation used to indicate the driving of the first vehicle, and the driving operation of the first vehicle includes but is not limited to: steering prompt operation, deceleration operation, acceleration operation, etc. If the driving operation is a steering prompt operation (such as turning on the right turn signal), then it can be determined that the first vehicle has a lane change requirement. In another implementation, it can be detected whether the first vehicle has a lane change requirement according to the driving direction of the first vehicle. This method is applicable to the situation where the driver does not perform the corresponding steering prompt operation in advance or forgets to perform the steering prompt operation, but the first vehicle has a lane change trend. Specifically, the deflection angle can be determined according to the driving direction. If the driving direction deflects and the deflection angle is greater than the preset angle threshold, then it means that the first vehicle has a lane change requirement; otherwise, if the driving direction does not deflect, or the deflection angle is less than or equal to the preset angle threshold, then it means that the first vehicle does not have a lane change requirement.

[0069] Step ②: If it is detected that the first vehicle has a lane change requirement, then call the direction control module and adjust the operation angle of the image acquisition module according to the lane change direction of the first vehicle.

[0070] Specifically, when it is detected that the first means of transport has a need to change lanes, the lane change direction of the first means of transport can be determined based on the driving direction, for example, the driving direction is the lane change direction of the first means of transport, so that the computer device can call the direction control module to control the shooting angle of the image acquisition module, and specifically can automatically adjust the shooting angle of the image acquisition module according to the lane change direction, such as automatically changing the PTZ direction. For example, the first means of transport is a vehicle, and during driving, the vehicle changes lanes to the right and turns on the right turn signal in advance. Then, the computer device can receive the operation of turning on the right turn signal, and can link the PTZ recording module to automatically perform direction operations on the image acquisition module, adjusting the shooting direction of the image acquisition module to the right. By automatically adjusting the angle during driving, specifically linking the angle adjustment operation with the driving operation of the vehicle, the adjusted angle can be made more accurate, and the transformation of the driving picture can be made more flexible, so as to solve the problem of visual blind spots that exist in fixed-angle shooting during driving of the vehicle.

[0071] Step ③: call the image acquisition module after the angle adjustment to shoot the traffic scene of the first vehicle to obtain a driving picture.

[0072] In a specific implementation, the computer device calls the image acquisition module after the angle is adjusted, and can shoot the traffic scenes in different directions of the first vehicle from the perspective of the first vehicle, such as the traffic scene behind the first vehicle and the traffic scene in front of the first vehicle, so as to obtain one or more frames of driving pictures. The traffic scene here can be a real traffic scene or a simulated traffic scene. Subsequent analysis based on these driving pictures can better detect whether the driving environment of the first vehicle is suitable for changing lanes. The image acquisition module after the angle is adjusted can be used to shoot and obtain the driving picture, and the driving picture shot here can also be called a PTZ video picture. For example, when changing lanes to the right, the shooting angle of the image acquisition module can be adjusted to the right, and the driving picture can be changed after shooting, so that the lane change conditions can be better viewed based on the changed driving picture, and the safe distance can be judged to ensure lane change safety and driving safety.

[0073] S402: Perform object analysis on the driving image to obtain a target object.

[0074] In one implementation, when the computer device performs object analysis on the driving picture and obtains the target object, it can specifically execute the following steps 1.1 to 1.2.

[0075] Step 1.1: Perform category recognition processing on the driving image to obtain a recognition result.

[0076] In a specific implementation, a target detection model can be called to perform class recognition processing on the driving scene image. The target detection model can be a Faster RCNN neural network convolution model or other neural networks. Among them, Faster RCNN is mainly composed of four main parts: convolutional layers, Region Proposal Networks, Roi Pooling, and Classification. The data processing device can include a target recognition module, which is used to recognize the target objects in the driving scene image. The target detection model can be a processing model in the target class recognition module and supports the recognition of objects in the driving scene image to obtain at least one object class in the driving scene image.

[0077] The recognition result obtained through the class recognition processing is used to indicate the object class in the driving scene image. Optionally, in traffic safety, it involves multiple categories such as pedestrians, bicycles, electric vehicles, motorcycles, cars, etc. Therefore, the object class indicated by the recognition result can include one or more of the above categories. For example, the recognition result can include vehicles, roads, etc. in the driving scene image.

[0078] Taking the Faster RCNN neural network convolution model as an example below, and combined with Figure 5Schematic structure of the target detection model. The target detection model includes a feature extraction sub-network, a region generation sub-network, a region of interest pooling layer, a fully connected layer, a target classification layer, and a bounding box regression layer. Among them, the region generation sub-network and the region of interest pooling layer share the same feature extraction sub-network (such as a convolutional layer), and these sub-networks are jointly trained. The region generation sub-network acts as an attention director to determine the best bounding boxes of various scales and aspect ratios for object classification evaluation. In other words, the RPN tells the classification layer where to look. Based on the above structure, the general logic of the category recognition process can be as follows: Call the feature extraction sub-network in the target detection model to perform feature extraction on the driving image, and obtain the image features of the driving image. This image feature is an initial feature map, which can be used to reflect the basic features in the driving image, such as the object contours in the driving image, etc. Then, on the one hand, the image features of the driving image can be input into the region of interest pooling layer. By performing ROI pooling operations on the image features, the objects in the driving image can be extracted as feature maps of a fixed size. On the other hand, the image features of the driving image can be sent to the region extraction sub-network to calculate the offsets to obtain more accurate candidate detection boxes. Optionally, the region generation sub-network in the image recognition network can be called to generate target detection boxes (Anchor box). Here, the target detection box is a bounding box used to detect objects. Perform cropping and filtering on the target detection box, and then perform binary classification discrimination. Through binary classification discrimination, it can be determined whether the image content within the target detection box is foreground or background, that is, to determine whether the image content within the target detection box is an object or a non-object, so as to determine whether the target detection box covers the target. At the same time, the target detection box is corrected through the bounding box regression layer to obtain a candidate detection box. Here, the correction is specifically to correct the coordinates of the target detection box, and the obtained candidate detection box is a more accurate extraction region (proposal). Feature refinement is performed based on the image features and the candidate detection box to obtain candidate image features. Specifically, the interest pooling layer can extract candidate feature maps (proposal feature maps) as candidate image features according to the obtained image features (feature maps) and candidate detection boxes (proposal feature maps), and then the fully connected layer integrates the candidate image features. Then, the target classification layer performs category discrimination based on the output of the fully connected layer to obtain the object category. During the training process, the target classification layer can output classification probabilities, so that joint training of the classification probabilities and the bounding box regression can be performed to improve the accuracy of category recognition.

[0079] In one implementation, before feature extraction of the driving scene, the size of the driving scene can be scaled to obtain a driving scene with a target size. For example, for any video image of size PxQ, it can be scaled to a fixed size MxN, that is, the target size is MxN. Then, the driving scene with the target size is fed into the feature extraction sub-network (such as the convolutional layer CNN basic network) to perform feature extraction processing on the driving scene with the target size to obtain image features. After the image features are pooled, they can be fed into the fully connected layer for classification and location regression. The classification layer can map the feature map to the probability of each possible object category, and the location regression can predict the bounding box position of each object. The classification and location regression tasks can be optimized through the multi-task function.

[0080] Step 1.2: Determine the target object according to the object category indicated by the recognition result.

[0081] The target object includes at least one of the following: moving object and static object. In one implementation, the computer device can directly determine each object category indicated in the recognition result as the target category, so that the object of the target category is the target object. In another implementation, the object categories indicated in the recognition result can be filtered to obtain one or more target categories, and the objects of the filtered target categories can be determined as the target objects. Exemplarily, the object categories recognized in the driving scene include: pedestrians, vehicles, traffic lights, roads. Since the main function of traffic lights is to coordinate and dispatch the driving of various vehicles in the traffic scene and does not directly affect the driving safety of vehicles, the objects other than traffic lights, including vehicles, roads, pedestrians, etc., can be determined as target objects.

[0082] The above Step 1.1 - Step 1.2 can be applied to vehicles and other means of transportation. The data processing method provided in this application can include a vehicle safety detection algorithm, and this vehicle safety detection algorithm includes the above recognition logic. Specifically, this vehicle safety detection algorithm can identify the target category in the driving scene based on the driving scene, so as to determine the target object.

[0083] In one implementation, the number of driving scenes is N frames, where N is an integer greater than 1. It can be understood that the computer device can perform object analysis processing on each frame of the N frames of driving scenes to identify the target object. Subsequently, the state of the target object can also be monitored based on the N frames of images to obtain a more accurate state monitoring result. Regarding the specific implementation of Step 1.2, the following Step 1.2.1 - Step 1.2.2 can be referred to.

[0084] Step 1.2.1: Perform motion difference analysis based on the N frames of driving scenes to obtain at least one candidate moving object.

[0085] In a specific implementation, due to the continuous nature of the video sequence collected by the camera, if there are no moving objects in the scene, the change between consecutive frames is very weak. If there are moving objects, there are obvious changes between consecutive frames. For any object in the driving scene, there will be some differences between two adjacent video images during the movement process. Therefore, in this application, the inter-frame difference method is used to determine the candidate moving objects, and then the moving targets are selected. Specifically, the computer device can perform motion difference analysis based on M consecutive driving images out of N driving images, where M ∈ [2, N] and M is an integer. That is to say, using the inter-frame difference method, motion difference analysis can be performed based on two consecutive driving images in time, or based on more than two consecutive driving images in time (such as three images). By subtracting the pixel values of the corresponding pixel points in different frames and based on the threshold of pixel difference, the moving objects can be discriminated, thereby realizing the detection of moving targets.

[0086] In one implementation, two adjacent driving images among the N driving images include: the k-th driving image and the (k - 1)-th driving image, where k ∈ [2, N]; the computer device performs motion difference analysis based on the two adjacent driving images. The general implementation logic may include: first, perform difference processing on the pixel values at each position in the k-th driving image and the pixel values at the corresponding position in the (k - 1)-th driving image to obtain a difference image, and then perform binarization processing on the difference image according to a preset difference threshold to obtain a binary image; then perform connectivity analysis on the binary image to obtain at least one candidate moving object.

[0087] Specifically, each position in the driving image can be represented by coordinates (i, j), where i represents the abscissa and j represents the ordinate. Here, the abscissa and ordinate respectively correspond to the length and height of the driving image. The position in the driving image can also be understood as a pixel point, and the value of each position is a pixel value. The pixel value can be a grayscale value, a chromaticity value, a brightness value, etc., and this application does not limit it here. By performing differential processing on the pixel values at the same position between consecutive frames of images, the absolute value of the difference between the pixel values can be obtained, that is, the pixel difference value. This pixel difference value can be used to reflect whether the same object in two adjacent frames of driving images is moving. The differential image obtained by performing differential processing on the k-th frame of the driving image and the (k - 1)-th frame of the driving image includes the pixel difference value at each position. After obtaining the differential image, the differential image can be binarized according to the magnitude relationship between a preset difference threshold and the pixel difference value at each position in the differential image, so as to determine the latest pixel value at each position and obtain a binary image. Among them, the preset difference threshold can be set according to experience or determined based on other factors (such as frame rate). During the binarization process, it specifically includes the following two situations: ① If the pixel difference value at the corresponding position is less than or equal to the preset difference threshold, the pixel value at the corresponding position is set to a first value (for example, the value "0"). ② If the pixel difference value at the corresponding position is greater than the preset difference threshold, the pixel value at the corresponding position is set to a second value (for example, the value "1").

[0088] Based on the preset difference threshold, when the difference between the pixel values at the same position in two consecutive frames of driving images is small, it is considered that there is no moving target corresponding to the corresponding position in the driving image; when the difference between the pixel values at the same position in two consecutive frames of driving images is large, it is considered that there is a moving target corresponding to the corresponding position in the driving image. For each position in the differential image, the new pixel value can be determined according to the magnitude relationship between the pixel difference value and the preset difference threshold in the above manner, so as to obtain a binary image. In this way, in the binary image, if the pixel value at a position is set to the first value, it means that the pixel difference value at the corresponding position in two adjacent frames of driving images is less than or equal to the preset difference threshold; if the pixel value at a position is set to the second value, it means that the pixel difference value at the corresponding position in two adjacent frames of driving images is greater than the preset difference threshold. Schematically, the above binarization process can be implemented according to the following expression:

[0089]

[0090] Among them, f k (i, j) represents the pixel value at the position (i, j) in the k-th frame of the driving image, f k-1(i, j) represents the pixel value at position (i, j) in the k - 1 frame driving image, Th is a preset difference threshold, and d(i, j) represents the pixel value at position (i, j) in the binary image.

[0091] After obtaining the binary image, since the positions corresponding to the pixel values with the second value in the binary image are the positions where a candidate moving object is located. The computer device can perform connectivity analysis on the binary image to identify the candidate moving object. The specific logic of the connectivity analysis may include: determining the connected region formed by the positions corresponding to the pixel values with the second value in the binary image as a motion candidate object. Among them, the connected region refers to an image region composed of pixel points with the same pixel value (here it is the second value) and adjacent positions in the binary image.

[0092] Step 1.2.2: Screen at least one candidate moving object according to the recognition result to obtain a moving target, and determine the moving target as the target object.

[0093] Since the recognition result can indicate the object category, and at least one candidate moving object involves one or more categories, based on the recognition result, the objects of the target category in at least one candidate moving object can be screened out, and the one or more screened candidate moving objects are the moving targets. For example, the recognition result includes: cars, pedestrians, and traffic signs, and the candidate moving objects obtained based on the inter - frame image difference processing include: cars and pedestrians, then the cars and pedestrians can be determined as the moving targets.

[0094] S403: Monitor the state of the target object according to the driving image to obtain a state monitoring result.

[0095] In one embodiment, the target object is a moving target, for example, the target object is a car. The number of driving images includes N frames, and N is an integer greater than 1. The implementation manner of the above step S403 specifically includes the following contents shown in (1)-(3).

[0096] (1) Monitor the target object according to N driving images to obtain the displacement and motion duration of the target object.

[0097] In a specific implementation, the moving distances of the target object in consecutive multiple frames of driving images are accumulated to obtain the displacement of the target object. The moving distance between any two frames of driving images of the target object can be determined by the motion vectors between the inter-frame images. Based on the relationship between the frame rate and time, the duration of each frame can be determined by the frame rate. Therefore, the motion duration of the target object can be calculated according to the number of frames of the driving image where the target object is located and the frame rate. Exemplarily, the number of consecutive frames of driving images where the target object is located is 10, and the frame rate of the video sequence is 25 fps (i.e., frames per second), and the motion duration of the target object can be determined to be 10 / 25 = 0.4 seconds.

[0098] (2) Determine the motion speed of the target object according to the displacement and motion duration of the target object, and perform trajectory analysis based on the position and motion speed of the target object to obtain the motion trajectory of the target object.

[0099] Based on the calculation principle of speed: v = s / t, after obtaining the displacement and motion duration of the target object, the motion speed of the target object can be determined. This motion speed can be combined with the position of the target object, and the motion trajectory of the target object can be determined through trajectory analysis. During the trajectory analysis process, the second position of the target object can be estimated according to the first position and motion speed of the target object, and then the third position of the target object can be estimated according to the second position and motion speed, and so on, to obtain h (an integer greater than 1) positions of the target object. Thus, based on these h positions, the motion trajectory of the target object can be obtained. This motion trajectory can be used to indicate the motion intention and motion direction of the target object, etc., and can be used as part of the status monitoring result to provide a reference for determining the driving safety of the first vehicle.

[0100] (3) Add the motion speed of the target object and the motion trajectory of the target object to the status monitoring result.

[0101] Since the motion speed of the target object is too fast (such as greater than the preset speed), or the motion trajectory of the target object indicates that the target object will pull over and stop or coincide with the motion trajectory of the first vehicle at a future moment, it will affect the driving safety of the first vehicle. Therefore, the computer device can add the motion speed of the target object and the motion trajectory of the target object to the status monitoring result, so as to comprehensively judge the driving safety of the first vehicle based on the motion speed of the target object and the motion trajectory of the target object, and obtain the result of "whether there is a safety risk for the first vehicle".

[0102] The state monitoring methods shown in (1)-(3) above mainly monitor the moving speed, position, etc. of a moving target. During the monitoring process, based on the moving speed and the current position, the possible moving trajectory of the moving target can be predicted. Thus, based on the moving trajectory, the possible impact of the moving target on the first vehicle can be preliminarily determined, and the driving safety of the first vehicle can be determined in a timely manner.

[0103] In another embodiment, the target object includes a traffic road, which refers to the road on which the first vehicle travels. Since the road surface condition of the traffic road has a great impact on the driving safety of the first vehicle, therefore, the specific implementation of step S403 above can be to monitor the road surface condition of the traffic road according to the driving picture, and the monitoring dimensions of the road surface condition can include at least one of the following (4)-(6).

[0104] (4) Monitor the road surface condition of the traffic road according to the driving picture to obtain a first monitoring result. The first monitoring result is used to indicate the road surface condition of the traffic road.

[0105] The road surface condition of the traffic road refers to conditions that can affect the driving safety of the first vehicle, such as whether the road surface is slippery and whether there are foreign objects on the road surface. During the monitoring process, the road surface condition of the traffic road can be analyzed through the driving picture to obtain the first monitoring result. Therefore, the first monitoring result can be used to indicate the road surface condition of the traffic road. For example: if an object with a size meeting the preset size is analyzed to appear on the traffic road through the driving picture, then the first monitoring result can be used to indicate that there are foreign objects on the road surface of the traffic road. If it is analyzed through the driving picture that the traffic road is slippery due to weather reasons, then the obtained first monitoring result is used to indicate that the traffic road has a slippery phenomenon.

[0106] (5) Monitor the road surface height difference of the traffic road according to the driving picture to obtain a second monitoring result. The second monitoring result is used to indicate the road surface height difference of the traffic road.

[0107] In one implementation, the computer device can analyze the pixel values in the image area where the traffic road is located in the driving image. Since there is a large height difference on the road surface of the traffic road, there are abrupt changes in the pixel values (such as grayscale values, chromaticity values, or brightness values) in the driving image. For example, the brightness value in the image area where the ground with a higher road surface is located is relatively large, while the brightness value in the image area where the ground with a lower road surface is located is relatively small, and on the boundary of the road surface change from high to low, the difference between the brightness values is also relatively obvious. Therefore, it is possible to determine whether there is a phenomenon of height difference on the traffic road by analyzing whether the pixel value difference at the corresponding position in the driving image is greater than the difference threshold. Further, the height difference of the road surface can be determined based on the pixel value difference, such as directly determining the pixel value difference as the height difference of the road surface.

[0108] (6) Monitor the relative position between the first vehicle and the lane markings of the traffic road according to the driving image to obtain a third monitoring result. This third monitoring result is used to indicate the distance between the driving position of the first vehicle and the lane markings of the traffic road.

[0109] In a specific implementation, since the lane markings of the traffic road are usually higher than the normal road surface and are prone to form height differences, and due to the special nature of the lane marking material, it is relatively easy for the first vehicle to slip when driving on the lane markings, thus affecting the safe driving of the first vehicle. Therefore, the computer device can determine the driving position of the first vehicle in the traffic scene according to the driving image and monitor the relative position between the driving position of the first vehicle and the lane markings of the traffic road. When the relative position indicates that the distance between the two is too close, since the first vehicle may drive on the lane markings, it may affect the safe driving of the first vehicle. The relative position between the first vehicle and the lane markings can be reflected by the relative distance between the two. Therefore, the obtained third monitoring result can be used to indicate the relative distance between the first vehicle and the lane markings.

[0110] The monitoring of the state of the traffic road shown in the above (4)-(6) is equivalent to the monitoring of the state of one or more dimensions of the traffic road. The state monitoring results obtained through the monitoring of the state of the traffic road include one or more of the first monitoring result, the second monitoring result, and the third monitoring result. For example, the state monitoring results include the three monitoring results: the first monitoring result, the second monitoring result, and the third monitoring result. When problems such as road slipperiness caused by rain, foreign objects on the road surface, oil stains, and height differences on the road surface occur, timely warning prompts can be given through the indication of the state monitoring results, thereby improving the driving safety of the first vehicle on the traffic road. When the first vehicle is a riding vehicle such as a motorcycle or an electric vehicle, since the driving balance is greatly affected by the road surface conditions, especially when driving at night, the vehicle driving safety is greatly affected by the road surface conditions, and the vehicle is prone to slipping or rolling over, thus having a greater impact on the safe driving performance of the vehicle. By monitoring the state of the traffic road conditions and other dimensions through the driving video, these information can be warned in advance, and driving safety alarms can be supported under corresponding conditions to ensure the traffic safety of the vehicle.

[0111] S404. Determine the driving safety of the first vehicle according to the state monitoring results.

[0112] In one embodiment, when the computer device executes the above S404, it can specifically execute the following steps 2.1-step 2.3.

[0113] Step 2.1. Determine the collision possibility between the target object and the first vehicle according to the state monitoring results and the driving state of the first vehicle.

[0114] In a specific implementation, the target object is a moving target, and the state monitoring results include the movement trajectory of the target object and the movement speed of the target object. The computer device can obtain the driving state of the first vehicle, which refers to the operating state of the first vehicle during driving, including but not limited to at least one of the following: driving trajectory, driving speed, driving direction, etc. The state monitoring results include the movement speed and movement trajectory of the moving target; the movement trajectory of the moving target can be used to indicate the driving direction of the moving target. The computer device can determine the possibility of collision between the two by comparing the data of the same dimension based on the state monitoring results of the moving target and the driving state of the first vehicle.

[0115] In one implementation, the driving state includes a driving trajectory, and the collision possibility can be determined according to the trajectory coincidence degree between the movement trajectory of the target object and the driving trajectory of the first vehicle. The greater the trajectory coincidence degree, the greater the collision possibility; the smaller the trajectory coincidence degree, the smaller the collision possibility. In another implementation, the driving state includes a driving direction, and the collision possibility can be determined according to the direction coincidence degree between the driving direction indicated by the movement trajectory of the target object and the driving direction of the first traffic trajectory. The greater the direction coincidence degree, the greater the collision possibility; the smaller the direction coincidence degree, the smaller the collision possibility. In yet another implementation, the driving state includes a driving speed, and the collision possibility between the target object and the first vehicle can be determined according to the speed difference between the driving speed of the target object and the driving speed of the first vehicle. The greater the speed difference, the smaller the collision possibility; the smaller the speed difference, the greater the collision possibility.

[0116] In a feasible implementation, the target object is a moving target. When monitoring the state of the moving target, it is also possible to obtain whether the state of the moving target is a stationary state or a moving state. Therefore, when the computer device executes the above step 2.1, it can specifically execute the following content shown in ①-②: ① If the state monitoring result indicates that the target object is in a stationary state, determine the collision possibility between the target object and the first vehicle according to the stationary duration of the target object and the driving state of the first vehicle. ② If the state monitoring result indicates that the target object is in a moving state, determine the collision possibility between the target object and the first vehicle according to the driving state of the first vehicle and the moving speed of the target object.

[0117] In a specific implementation, the target object is a second vehicle, and the state monitoring result can be used to indicate the state of the second vehicle. The state here can include a stationary state or a moving state. When the state monitoring result indicates that the target object is in a stationary state, the computer device can count the stationary duration of the target object. If the driving state of the first vehicle indicates that the trajectory coincidence degree between the driving trajectory of the first vehicle and the driving trajectory of the target object is greater than a preset threshold, the collision possibility can be determined according to the time difference between the stationary duration and the preset duration. Further, when the stationary duration of the target object is greater than the preset duration, it means that the stationary duration of the target object is relatively long, and there may be some sudden driving operations, such as a vehicle suddenly opening its door. Thus, it can be determined that the collision possibility between the second vehicle and the first vehicle is greater than the possibility threshold. When the stationary duration of the second vehicle is less than the preset duration threshold, it means that the second vehicle is temporarily stationary, and during the driving process of the first vehicle, the second vehicle can start moving again without affecting the driving of the first vehicle. Thus, it can be determined that the collision possibility between the second vehicle and the first vehicle is less than the possibility threshold.

[0118] In another specific implementation, when the state monitoring result indicates that the target object is in a moving state, the target object travels on the traffic road together with the first vehicle, and the computer device can obtain the moving speed of the target object. If the target object is the second vehicle, then the moving speed is specifically the driving speed of the second vehicle. According to the moving speed of the target object, the driving speed of the first vehicle, and the relative driving direction between the target object and the first vehicle, the collision possibility between the two is determined. Further, if the speed difference between the moving speed of the target object and the driving speed of the first vehicle is greater than the preset difference and their driving directions are the same, then it can be determined that the distance between the two vehicles will become larger and larger, so that the collision possibility between the second vehicle and the first vehicle can be determined to be less than or equal to the possibility threshold. On the contrary, if the speed difference between the driving speed of the second vehicle and the driving speed of the first vehicle is less than the preset difference and their driving directions are the same, or the speed difference is greater than the preset difference and their driving directions are opposite (such as the target object is a vehicle coming from the opposite direction), then it can be determined that the collision possibility between the second vehicle and the first vehicle is greater than the possibility threshold.

[0119] In the above embodiment, the computer device can identify the target object and monitor the state of the target object through the analysis of the driving video, and comprehensively determine the collision possibility between the first vehicle and the target object based on at least one feature of the target object (such as the staying duration, category), improving the accuracy of the collision possibility.

[0120] In the embodiment of the present application, the collision possibility refers to the possibility of a collision between the target object and the first vehicle, which can be specifically represented by a collision probability. The collision probability is positively correlated with the collision possibility: the greater the collision probability, the greater the collision possibility, and the smaller the collision probability, the smaller the collision possibility. Further, after calculating the collision possibility between the target object and the first vehicle, according to the magnitude relationship between the collision possibility and the possibility threshold, the driving safety of the first vehicle can be determined. Specifically, it can be determined whether there is a collision risk between the first vehicle and the target object, and then one of the following steps 2.2-step 2.3 is executed.

[0121] Step 2.2: If the collision possibility is greater than the possibility threshold, it is determined that the driving safety of the first vehicle includes a collision risk.

[0122] Step 2.3: If the collision possibility is less than or equal to the possibility threshold, it is determined that the driving safety of the first vehicle does not include a collision risk.

[0123] Optionally, the likelihood threshold for determining the likelihood of a collision is also a probability value. If the likelihood of a collision is greater than the likelihood threshold, it indicates that the probability of a collision between the first vehicle and the target object is relatively high, and thus it can be determined that there is a risk of collision between the first vehicle and the target object. If the likelihood of a collision is less than or equal to the likelihood threshold, it indicates that the probability of a collision between the first vehicle and the target object is relatively low, and thus it can be determined that there is no risk of collision between the first vehicle and the target object.

[0124] Based on the above steps 2.1 - 2.3, the likelihood of a collision between the first vehicle and the moving object can be quantified based on the driving trajectory and driving speed of the moving object included in the status monitoring result, so that the driving safety of the first vehicle can be determined more accurately.

[0125] In another embodiment, the target object is a traffic road. When the computer device executes the above S404, it can specifically execute the following contents shown in ① - ③.

[0126] ① If the status monitoring result includes the first monitoring result, then when the road surface condition of the traffic road indicated by the first monitoring result does not meet the safe driving condition, it is determined that the driving safety of the first vehicle includes road safety risks.

[0127] Specifically, the safe driving condition refers to the road surface condition required to ensure the safe driving of the first vehicle on the traffic road. For example, the safe driving condition includes: there are no obstacles blocking the passage on the road surface of the traffic road, and the road surface of the traffic road is not slippery, etc. When the road surface condition of the traffic road indicated by the first monitoring result does not meet the safe driving condition, it means that the road surface condition of the traffic road cannot well ensure the driving safety of the first vehicle on the traffic road, and thus it can be determined that the driving safety of the first vehicle includes road safety risks. Exemplarily, if the first monitoring result indicates that the traffic road has a slippery road surface, oil stains or foreign objects, etc., it can be determined that the road surface condition does not meet the safe driving condition. On the contrary, when the road surface condition of the traffic road indicated by the first monitoring result meets the safe driving condition, it is determined that the driving safety of the first vehicle does not include road safety risks.

[0128] ② If the status monitoring result includes the second monitoring result, then when the height difference of the road surface of the traffic road indicated by the second monitoring result is greater than the preset height difference, it is determined that the driving safety of the first vehicle includes road safety risks.

[0129] Specifically, the preset height difference is a threshold value used to ensure the driving safety of the first vehicle on the traffic road. Within the preset height difference, the first vehicle can drive safely, and the height difference of the road surface of the traffic road has a relatively small impact on the driving safety of the first vehicle. Therefore, if the road surface height difference indicated by the second monitoring result is greater than the preset height difference, it means that the height difference of the road surface of the traffic road has a greater impact on the driving safety of the first vehicle. For example, if the road surface height difference is too large, it may cause risks such as the vehicle rolling over, so it can be determined that the driving safety of the first vehicle includes road safety risks, and the road safety risk here is specifically the road height difference risk. On the contrary, when the road surface height difference of the traffic road indicated by the second monitoring result is less than or equal to the preset height difference, it is determined that the driving safety of the first vehicle does not include road safety risks.

[0130] ③ If the status monitoring result includes the third monitoring result, when the distance between the driving position of the first vehicle indicated by the third monitoring result and the lane marking of the traffic road is less than the preset distance threshold, it is determined that the driving safety of the first vehicle includes road safety risks.

[0131] Specifically, if the distance between the driving position of the first vehicle and the lane marking of the traffic road is less than the preset distance threshold, it means that the first vehicle may have run over the lane marking or is relatively close to the lane marking, and the lane marking poses certain risks to the driving safety of the first vehicle, especially in weather conditions such as rain, and coupled with the fact that the road surface conditions of the traffic road are not very favorable, so it can be determined that the driving safety of the first vehicle includes road safety risks, and the road safety risk here can include the risk of the first vehicle slipping due to running over the lane marking. On the contrary, when the distance between the driving position of the first vehicle indicated by the third monitoring result and the lane marking of the traffic road is greater than the preset distance threshold, it is determined that the driving safety of the first vehicle does not include road safety risks.

[0132] It can be seen that through the content indicated by the monitoring results of each dimension of the above traffic road, it is possible to more comprehensively determine whether the first vehicle has road safety risks, and then obtain the driving safety of the first vehicle.

[0133] In another embodiment, if the driving image processed above is the driving image collected when the first vehicle has a lane-changing requirement, the target object obtained by analyzing the object in the driving image may be other vehicles driving behind the first vehicle. When the computer device executes S404 above, it may specifically execute the following: Determine the lane-changing instruction information according to the status monitoring result, where the lane-changing instruction information is used to indicate whether the lane-changing condition of the first vehicle meets the preset lane-changing condition. Then, determine whether the first vehicle includes a lane-changing safety risk according to the lane-changing instruction information. The judgment logic based on the lane-changing instruction information is as follows: If the lane-changing instruction information indicates that the lane-changing condition of the first vehicle does not meet the preset lane-changing condition, it is determined that the driving safety of the first vehicle includes a lane-changing safety risk; if the lane-changing instruction information indicates that the lane-changing condition of the first vehicle meets the preset lane-changing condition, it is determined that the driving safety of the first vehicle does not include a lane-changing safety risk.

[0134] In one implementation, the status monitoring result is used to indicate the distance between the target object and the first vehicle. If the distance between the target object and the first vehicle is greater than the preset distance, the lane-changing instruction information can be used to indicate that the lane-changing condition of the first vehicle meets the preset lane-changing condition, so that it can be determined that the driving safety of the first vehicle includes a lane-changing safety risk. If the distance between the target object and the first vehicle is less than the preset distance, the lane-changing instruction information is used to indicate that the lane-changing condition of the first vehicle does not meet the preset lane-changing condition, so that it can be determined that the driving safety of the first vehicle does not include a lane-changing safety risk.

[0135] It can be seen that based on the driving image, the lane-changing condition of the first vehicle can be detected to obtain the lane-changing instruction information, which is used to indicate whether the lane-changing condition of the first vehicle meets the preset lane-changing condition. By analyzing the lane-changing condition of the first vehicle, it is possible to prevent the rear-view video angle of the first vehicle from being centered and the unsafe hidden danger caused by the visual blind area during lane-changing, thus ensuring "vehicle lane-changing safety".

[0136] In an embodiment, the first vehicle is a vehicle, and the computer device can detect the parking requirement of the first vehicle. Here, the parking requirement can be detected according to one or both of the driving operation of the first vehicle and the driving state of the first vehicle. For example, if the first vehicle has a deceleration operation and a tendency to pull over to the right, it can be determined that the first vehicle has a parking requirement. If it is detected that the first vehicle has a parking requirement, the following steps 4.1 - step 4.4 may be included.

[0137] Step 4.1: Obtain the reference parking area of the first vehicle according to the driving image.

[0138] The reference parking area is set based on the spatial characteristics of the first vehicle. The spatial characteristics of the first vehicle are used to indicate the space occupied after the first vehicle stops driving. For different types of vehicles, since the space occupied by parking is different, there can be different reference parking areas. For example, if the first vehicle is an electric vehicle, since the body of the electric vehicle will tilt when parking and more space will be occupied during the process of people getting off the vehicle, the reference parking area can be determined according to the space occupied when the electric vehicle tilts and the size of the electric vehicle itself. The reference parking area can be positively correlated with the spatial characteristics of the first vehicle. The larger the space indicated by the spatial characteristics of the first vehicle, the larger the reference parking area can be set. In addition to the space occupied by the first vehicle, the reference parking area also needs to include the space for other operations of accommodating the first vehicle. For example, when an electric vehicle parks, in addition to the space for the body to tilt, the space required for people to get off the vehicle also needs to be reserved. Another example is that for a car, in addition to the size of the vehicle itself, the space for the vehicle door to open to enable people to get off safely also needs to be reserved. The reference parking area can be a multi-directional area, including not only the front parking area, but also the rear parking area and the left and right side parking areas. Exemplarily, as Figure 6 the schematic diagram of the traffic scenario shown. This traffic scenario is a traffic scenario from a top-down perspective. The first vehicle is the electric vehicle marked 601. The electric vehicle is driving on the traffic road, and there are also other types of vehicles such as cars and trucks driving on the traffic road. If the electric vehicle has a parking requirement, a reference parking area can be determined according to the spatial characteristics of the electric vehicle. This reference parking area can be used to detect whether there is a collision risk between the first vehicle and other moving targets.

[0139] Step 4.2: Perform target detection on the reference parking area to obtain the detection result.

[0140] In one implementation, the target detection of the reference parking area mainly detects whether there is a moving target approaching within the reference parking area (such as behind the first vehicle), and whether the distance between the moving target and the first vehicle is too close to cause a possible collision. Therefore, the detection result obtained through target detection is used to indicate whether there is a moving target within the reference parking area, and the collision possibility between the moving target and the first vehicle. The moving target can be a moving target traveling in the direction of approaching the first vehicle, or a moving target traveling in the direction of moving away from the first vehicle. For example, if the first vehicle is a car, the moving targets within the reference parking area include: other cars traveling in the same driving direction as the car and behind the car, and other cars approaching from the opposite direction with a different driving direction from the car. According to the detection result, it can be determined whether the parking environment of the first vehicle meets the safe parking conditions. The computer device performs the following step 4.3 or step 4.4 based on the indication of the detection result.

[0141] Step 4.3: If the detection result indicates that there is a moving target within the reference parking area and the collision possibility between the moving target and the first vehicle is greater than the possibility threshold, it is determined that the driving safety of the first vehicle includes a parking safety risk.

[0142] In a specific implementation, the moving target existing within the reference parking area can be a moving target traveling in the direction of approaching the first vehicle, or a moving target traveling in the direction of moving away from the first vehicle. If there is a moving target traveling towards the first vehicle within the reference parking area and the distance between the moving target and the first vehicle is less than the distance threshold, it indicates that the collision possibility between the first vehicle and the moving target is greater than the possibility threshold, and there is a collision risk between the first vehicle and the moving target. Thus, it can be determined that the reference parking area is a non-safe parking area, the parking environment indicated by the reference parking area does not meet the safe parking conditions, and the first vehicle has a parking safety risk. Among them, the safe parking condition is a condition for ensuring that the first vehicle can park safely, such as there being no moving target within the reference parking area, or the collision possibility between other objects being relatively small.

[0143] Step 4.4: If the detection result indicates that there is no moving target within the reference parking area, or the collision possibility between the moving target and the first vehicle is less than or equal to the possibility threshold, it is determined that the driving safety of the first vehicle does not include a parking safety risk.

[0144] In a specific implementation, if there are moving objects in the reference parking area, but the possibility of collision between the moving objects and the first vehicle is relatively small, it can be determined that the reference parking area is a safe parking area, and the parking environment of the first vehicle meets the safe parking conditions. Furthermore, it can be determined that the first vehicle has no parking safety risk. Optionally, when it is determined that the parking environment of the first vehicle meets the safe parking conditions, the computer device may send safety information to the associated target device, so that the target device outputs a safety prompt to inform the driver that parking is safe at this time.

[0145] In the above manner, when the first vehicle needs to park itself, it can obtain a driving image from the first perspective of the first vehicle before parking. The driving image is a scene image used to reflect the parking environment. Then, based on the driving image, the parking environment of the first vehicle can be analyzed to assist the first vehicle in parking safely. The parking conditions of the first vehicle can be monitored through the reference parking area to timely give an alarm prompt for the existing collision risk and ensure parking safety.

[0146] In one embodiment, the first vehicle is associated with the target device. According to the functions of the target device, the target device may include one or more of the following: items to be carried during the driving of the first vehicle, terminal devices, etc. Among them, the items to be carried during the driving of the first vehicle are, for example, helmets, and the terminal devices are, for example, in-vehicle terminals in vehicles, or mobile phones, or Bluetooth speakers, or Bluetooth headsets that support establishing connections with mobile terminals. The present application does not limit this. According to whether the target device is movable, the target device may include mobile terminals and fixed terminals. Mobile terminals include, for example, smartphones, tablets, or personal computers, etc., and fixed terminals include, for example, in-vehicle terminals, etc. Based on this, the computer device may execute the content shown in S405 - S406 as follows.

[0147] S405, if the driving safety of the first vehicle includes driving safety risks, send an alarm message matching the type of driving safety risks to the target device, so that the target device gives a risk alarm prompt based on the alarm message.

[0148] Among them, the driving safety risks include at least one of the following: collision risk, road safety risk, lane change safety risk, and parking safety risk. Correspondingly, the warning messages matching the types of driving safety risks may include: collision warning message, road warning message, lane change prompt message, and parking prompt message. Among them, the collision warning message is used to indicate the collision risk between the target object and the first vehicle; the road warning message is used to indicate the abnormality of the traffic road; the lane change prompt message is used to indicate whether the lane change conditions meet the expectations; the parking prompt message is used to prompt whether the parking environment meets the expectations. In a specific implementation, if the driving safety of the first vehicle includes one or more driving safety risks, the computer device may link with the target device to output a warning prompt message, so as to timely prompt the driver of the driving safety risks existing in the first vehicle.

[0149] If the computer device detects a collision risk between the first vehicle and the target object, it may send a collision warning message to the target device to prompt the driver of the collision risk between the first vehicle and the target object. The collision warning message may be represented by preset values, characters, etc. The target device may include the preset representation method of the collision warning message. Thus, after receiving the collision warning message, the target device may perform a risk warning prompt based on the collision warning message. The risk warning prompt may include one of the following: an alarm voice message output by voice, an alarm light output by light, etc. The risk warning prompt may be used to prompt the driver that a collision will occur between the first vehicle and the target object. For example, when the first vehicle is an electric vehicle and the target device is a helmet, when the camera fixed on the first vehicle captures the driving picture and detects a high possibility of collision between the electric vehicle and a nearby car based on the driving picture, it may send a message to the linked helmet, so as to output a voice prompt through the helmet. Since the contact between the helmet and the driver is relatively close, this can reduce the influence of external noise and better prompt the driver audibly. Similarly, if the target device is a Bluetooth headset connected to a mobile terminal, when the driver wears the Bluetooth headset, by sending a message to the mobile terminal, a voice prompt may be output through the Bluetooth headset.

[0150] If the computer device detects that there is a road safety risk for the first vehicle, such as detecting foreign objects on the road surface of the traffic road, the road surface of the traffic road being slippery, or there being height differences on the road surface of the traffic road, or the first vehicle being relatively close to the lane line of the traffic road, etc., the computer device can send a road warning message to the target device. Since the road safety risk can be caused by different reasons, the road warning message can include warning content, and the warning content can be specifically represented by values or characters, etc. The warning content can be used to indicate the type of abnormality that occurs on the road. For example, if the warning content is 1, it means that the road safety risk is specifically that there are foreign objects on the road surface, and if the warning content is 2, it means that the height difference of the road surface is greater than the preset height difference. After receiving the road warning message, the target device can output a risk warning prompt based on the road warning message, and the risk warning prompt can include one of the following: a road warning voice message output by voice, an alarm light output by light, etc. This can visually prompt the driver of the first vehicle that there is a road safety risk, so that the driver can perform corresponding driving operations on the first vehicle (such as decelerating, steering, etc.) to better ensure their own safety.

[0151] If the computer device detects that there is a lane change safety risk for the first vehicle, it means that the lane change condition of the first vehicle does not meet the preset lane change condition. For example, when the vehicle is preparing to change lanes, other vehicles with the same driving direction behind are relatively close. Then, if the vehicle changes lanes at this time, there will be corresponding risks. Furthermore, the computer device can send a lane change prompt message to the target device. After receiving the lane change prompt message, the target device can output a risk warning prompt, and the risk warning prompt can include one of the following: a lane change prompt voice message output by voice, an alarm light output by light, etc. This can better prompt the first vehicle that there is a lane change safety risk both audibly and visually.

[0152] If the computer device detects that the first vehicle presents a parking safety risk, indicating that the parking environment of the first vehicle does not meet safe parking conditions, the computer device may send an alert message to the linked target device, causing the target device to output an alert. Optionally, for example, a voice prompt may be output through the target device's voice module, and a light prompt may be output through the target device's light module, to alert the driver that the reference parking area currently in the vehicle is unsafe and that it is unsafe to exit the vehicle. Furthermore, if an alert is output, the first vehicle may not park and may continue driving. Since the driving image of the first vehicle constantly changes during driving, the reference parking area of the first vehicle is continuously updated based on the new driving image, and the corresponding reference parking area may also change accordingly. The computer device may perform target detection on the updated reference parking area and, if it detects that the parking environment of the first vehicle meets safe parking conditions, cancel the alert. Discontinuing the alert may include one or more of the following: the target device ceasing to output the voice prompt or the light prompt. It can be seen that when it is detected that the parking environment of the first vehicle does not meet the safe parking conditions, the reference parking area can continue to be updated until the reference parking area is a safe parking area, that is, there is no dangerous target in the reference parking area, and the alarm prompt can be automatically lifted.

[0153] The several voice warning methods described above can be implemented by the target device's built-in voice module, and various light prompts can be implemented by the target device's built-in light prompt module. Thus, while the target device outputs various voice prompts via the voice module, it can also provide light prompts via the light prompt module. Light prompts can be output with different lighting effects based on different driving safety risks. For example, the type of driving safety risk can be matched to the flashing frequency of the light. When a first vehicle presents a lane change safety risk, the target device can output a low-frequency flashing light; when a road safety risk presents a road safety risk, the target device can output a solid light; and when a collision risk presents a high-frequency flashing light. Light prompts can also effectively provide visual warnings to drivers who cannot hear. Alternatively, if the voice module accidentally fails and malfunctions, the light prompt module can provide an alternative. This diverse range of prompt methods can ensure the driving safety of the first vehicle.

[0154] S406: If an abnormality occurs in the first vehicle after the vehicle stops traveling, an abnormality warning message is sent to the target device to issue an abnormality warning prompt on the target device.

[0155] Among them, the abnormalities that occur in the first vehicle include at least one of the following: the position of the first vehicle changes, the attitude of the first vehicle changes, and a collision occurs between the second vehicle and the first vehicle. In a specific implementation, the first vehicle is a vehicle. If the parking environment of the first vehicle meets the safe parking conditions, the first vehicle is allowed to park within the reference parking area. And based on the linkage between the first vehicle and the target device, if the parking position of the first vehicle is abnormal after it parks within the reference parking area (for example, the vehicle is abnormally moved, and the GPS position is different from the position when it parked before), or the parking attitude of the first vehicle is abnormal (for example, an electric vehicle falls down after parking), or a collision occurs between the second vehicle (such as a moving car) and the parked first vehicle. For the above situations, a remote alarm can be issued on the linked target device. Specifically, the computer device can send an abnormal alarm message to the target device, and then an abnormal alarm prompt can be output on the target device. The abnormal alarm prompt is a remote alarm message, which can be used to alarm the specific content of the abnormality of the first vehicle, so that the user can remotely view the situation of the first vehicle through the target device and cancel the alarm. In addition, an alarm prompt can also be output on the first vehicle, such as one or more of an alarm prompt and a light alarm prompt. Through the above method, if problems such as abnormal movement of the first vehicle occur, not only can an alarm prompt be output through the computer device, but also a remote alarm can be issued on the linked target device, so as to monitor the parking status of the first vehicle in real time.

[0156] In a feasible embodiment, different target devices support association, which is applicable to the association between the items required for driving the first vehicle and the target device. For example, a helmet and a terminal support association. If the position of the helmet changes, an alarm prompt can be output on the helmet (such as a voice alarm prompt output by the voice module of the helmet and / or a light prompt output by the light module of the helmet), and the helmet can also send a message to the terminal to view the position of the helmet on the terminal for auxiliary monitoring. In the case of the helmet being lost, the position of the helmet can be monitored in real time based on the terminal to facilitate the recovery of the helmet.

[0157] In an embodiment, when the first vehicle is associated with the target device, the user can set the trigger condition of the alarm prompt through the target device. Specifically, the computer device can receive the setting control instruction sent by the target device, and then reset the trigger condition of the alarm prompt according to the setting control instruction.

[0158] In a specific implementation, a setting control instruction can be used to indicate setting content, which can include triggering conditions for giving an alarm prompt to a first vehicle. The form of the setting content can be voice, image, text, video, etc. Optionally, a voice module built into the target device can receive a voice control instruction input by a user, and thus send a setting control instruction to a computer device. The triggering conditions for the alarm prompt include at least one of the following: the triggering condition for a risk alarm prompt and the triggering condition for an abnormal alarm prompt. Optionally, the triggering condition can be related to the discrimination criteria for driving safety risks, and the triggering conditions for the alarm prompt can include various safety thresholds required for discriminating driving safety risks. For example: a probability threshold for discriminating the possibility of collision, a preset distance threshold for discriminating the distance between a target object and the first vehicle, a preset height difference for discriminating the road height difference, and so on. The computer device can provide some safety thresholds for the user to select, so as to ensure driving safety while allowing the user to flexibly set the safety thresholds. For example, the target device is a helmet. Through the voice module in the helmet, a voice instruction for setting the alarm level can be received. Further, the helmet can send a setting control instruction to the computer device. The setting content indicated by the setting control instruction is "alarm level 3", which means that the triggering condition for outputting an alarm prompt when the original default alarm level is 1. After re-setting, an alarm prompt needs to be output until the alarm level reaches 3. Optionally, alarm level 3 can correspond to corresponding safety thresholds, and the computer device can adjust the safety thresholds, so as to re-determine the driving safety level through the safety thresholds to output an alarm prompt only when the alarm level is reached. For another example, it can be set that an abnormal alarm prompt is given only when the moving distance of the first vehicle reaches a preset distance.

[0159] Based on the content introduced in the above embodiments, taking the first vehicle as a vehicle as an example, the following can be provided Figure 7Flow schematic diagram of another data processing method shown. The technical structure of data processing mainly includes the following: vehicle safety video access module, vehicle safety detection algorithm module, linkage module, vehicle PTZ recorder linkage module, and road safety monitoring algorithm module. Among them, the vehicle safety video access module can trigger the execution of the data processing method provided in the embodiments of the present application through video access. After video access, the vehicle safety video access module can also support video viewing, including real-time viewing or playback viewing of the accessed video. During the operation of various detection algorithms, based on the accessed video as the analysis basis, each frame of the video is used as a driving picture for recognition and analysis. Specifically, through the vehicle safety detection algorithm module, two processes of target category recognition and target movement recognition can be performed on the driving picture, and through range judgment, it can be analyzed whether the vehicle will collide with the target object and whether the vehicle can safely stop. In addition, the above process can also perform data modeling through the accessed video to create a virtual traffic scenario for simulation safety optimization. That is to say, in addition to using AI (Artificial Intelligence) methods for calculation, the algorithm can also be input into the simulation system for safety performance training to further optimize the algorithm details. The details optimized here can be various safety thresholds required by the algorithm, such as the possibility threshold for determining whether there is a collision risk between the first transportation vehicle and the target object, the height difference threshold for determining whether the height difference of the traffic road is too large, the distance threshold for determining whether the lane change condition is met in the case of lane change, etc. When the first transportation vehicle is an electric vehicle, the linkage module can link target devices such as helmets and mobile terminals. During the helmet linkage process, warning prompts and voice control can be performed through the helmet, and the algorithm can be set through voice control; during the mobile terminal linkage process, functions such as abnormal warning and position monitoring can be provided. Among them, the abnormal warning can be a warning prompt when the vehicle appears abnormal, and based on the position monitoring, the real-time monitoring of the vehicle position can be performed when the vehicle is lost, and abnormal warning information can be output, etc. The vehicle PTZ recorder linkage module can not only record the driving of the vehicle to detect the driving safety of the vehicle, but also detect the lane change safety of the vehicle to determine whether the vehicle has sufficient safety lane change conditions and avoid the visual blind area during lane change. The road safety monitoring algorithm module can be used to monitor road surface foreign objects, road height differences, etc. of the traffic road. For the above algorithm modules, when an abnormality is detected, the target device can be linked for abnormal warning. For example, when the vehicle safety detection module detects that the current vehicle has a high possibility of colliding with other vehicles, or when the road safety detection algorithm monitors that there are foreign objects in the traffic road, or when the recorder linkage module detects that there is a safety risk in the vehicle lane change, etc., an abnormal warning can be output, which can be specifically output on a computer device and / or remotely output on the target device.

[0160] The data processing solution provided by the embodiments of the present application proposes various algorithms to assist in the driving of the first vehicle for the driving safety of the first vehicle. Specifically, the PTZ recording method can be used to assist in detecting the lane-changing safety of the first vehicle. The road safety situation can be automatically recognized according to the driving picture. By combining the necessary items required for driving (such as helmets required for driving electric vehicles) and the mobile terminal for linkage, warning prompts can be given in a timely manner, reducing the safety risk of the first vehicle driving, improving traffic driving safety, and comprehensively improving traffic road safety.

[0161] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a data processing device provided by the embodiments of the present application. This data processing device can be set in the computer device provided by the embodiments of the present application. Figure 8 The data processing device shown can be a computer program (including program code) running in a computer device. This data processing device can be used to execute Figure 2 or Figure 4 part or all of the steps in the method embodiments shown. Please refer to Figure 8 , this data processing device may include the following units:

[0162] An acquisition unit 801, configured to acquire a driving picture collected during the driving of the first vehicle; the driving picture is obtained by calling an image acquisition module and shooting from the driving perspective of the first vehicle;

[0163] A processing unit 802, configured to perform object analysis and processing on the driving picture to obtain a target object; the target object refers to an object that affects the driving safety of the first vehicle; monitor the state of the target object according to the driving picture to obtain a state monitoring result; determine the driving safety of the first vehicle according to the state monitoring result.

[0164] In one embodiment, when the processing unit 802 performs object analysis and processing on the driving picture to obtain a target object, it is specifically configured to: perform category recognition processing on the driving picture to obtain a recognition result; the recognition result is used to indicate the object category in the driving picture; determine the target object according to the object category indicated by the recognition result.

[0165] In one embodiment, the number of driving pictures includes N frames, where N is an integer greater than 1; when the processing unit 802 determines the target object according to the object category indicated by the recognition result, it is specifically configured to: perform motion difference analysis on the N driving pictures to obtain at least one candidate motion object; screen the at least one candidate motion object according to the recognition result to obtain a motion target, and determine the motion target as the target object.

[0166] In one embodiment, two adjacent driving images among the N driving images include: the k-th driving image and the (k - 1)-th driving image, where k ∈ [2, N]; when the processing unit 802 performs motion difference analysis based on the N driving images to obtain at least one candidate moving object, it is specifically configured to: perform difference processing on the pixel values at each position in the k-th driving image and the pixel values at the corresponding positions in the (k - 1)-th driving image to obtain a difference image, where the difference image includes the pixel difference values at each position; perform binarization processing on the difference image according to a preset difference threshold to obtain a binary image; perform connectivity analysis on the binary image to obtain at least one candidate moving object.

[0167] In one embodiment, the target object includes a moving target, the number of driving images is N frames, and N is an integer greater than 1; when the processing unit 802 monitors the state of the target object based on the driving images to obtain a state monitoring result, it is specifically configured to: monitor the target object based on the N driving images to obtain the displacement and movement duration of the target object; determine the movement speed of the target object according to the displacement and movement duration of the target object, and perform trajectory analysis according to the position and movement speed of the target object to obtain the movement trajectory of the target object; add the movement speed of the target object and the movement trajectory of the target object to the state monitoring result.

[0168] In one embodiment, when the processing unit 802 determines the driving safety of the first vehicle according to the state monitoring result, it is specifically configured to: determine the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle; if the collision possibility is greater than the possibility threshold, determine that the driving safety of the first vehicle includes a collision risk; if the collision possibility is less than or equal to the possibility threshold, determine that the driving safety of the first vehicle does not include a collision risk.

[0169] In one embodiment, the target object is a moving target; when the processing unit 802 determines the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle, it is specifically configured to: if the state monitoring result indicates that the target object is in a stationary state, determine the collision possibility between the target object and the first vehicle according to the stationary duration of the target object and the driving state of the first vehicle; if the state monitoring result indicates that the target object is in a moving state, determine the collision possibility between the target object and the first vehicle according to the driving state of the first vehicle and the movement speed of the target object.

[0170] In one embodiment, the target object includes a traffic road; when the processing unit 802 monitors the state of the target object according to the driving image to obtain a state monitoring result, it is specifically configured to: monitor the road surface condition of the traffic road according to the driving image to obtain a first monitoring result, where the first monitoring result is used to indicate the road surface condition of the traffic road; or, monitor the road surface height difference of the traffic road according to the driving image to obtain a second monitoring result, where the second monitoring result is used to indicate the road surface height difference of the traffic road; or, monitor the relative position between the first vehicle and the lane markings of the traffic road according to the driving image to obtain a third monitoring result, where the third monitoring result is used to indicate the distance between the driving position of the first vehicle and the lane markings of the traffic road; wherein, the state monitoring result includes one or more of the first monitoring result, the second monitoring result, and the third monitoring result.

[0171] In one embodiment, when the processing unit 802 determines the driving safety of the first vehicle according to the state monitoring result, it is specifically configured to: if the state monitoring result includes the first monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the road surface condition of the traffic road indicated by the first monitoring result does not meet the safe driving condition; if the state monitoring result includes the second monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the road surface height difference of the traffic road indicated by the second monitoring result is greater than a preset height difference; if the state monitoring result includes the third monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the distance between the driving position of the first vehicle and the lane markings of the traffic road indicated by the third monitoring result is less than a preset distance threshold.

[0172] In one embodiment, the first vehicle is associated with a direction control module; when the acquisition unit 801 acquires the driving image collected during the driving of the first vehicle, it is specifically configured to: detect the lane change requirement of the first vehicle; if it is detected that the first vehicle has a lane change requirement, call the direction control module to adjust the shooting angle of the image acquisition module according to the lane change direction of the first vehicle; call the image acquisition module with the adjusted angle to shoot the traffic scene of the first vehicle to obtain the driving image.

[0173] In one embodiment, when determining the driving safety of the first vehicle according to the status monitoring result, the processing unit 802 is specifically configured to: determine lane change indication information according to the status monitoring result, where the lane change indication information is used to indicate whether the lane change condition of the first vehicle meets the preset lane change condition; if the lane change indication information indicates that the lane change condition of the first vehicle does not meet the preset lane change condition, determine that the driving safety of the first vehicle includes a lane change safety risk; if the lane change indication information indicates that the lane change condition of the first vehicle meets the preset lane change condition, determine that the driving safety of the first vehicle does not include a lane change safety risk.

[0174] In one embodiment, the first vehicle is a vehicle, and the processing unit 802 is further configured to: if it is detected that the first vehicle has a parking requirement, obtain a reference parking area of the first vehicle according to the driving picture; the reference parking area is set based on the spatial characteristics of the first vehicle; perform target detection on the reference parking area to obtain a detection result; the detection result is used to indicate whether there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle; if the detection result indicates that there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle is greater than the possibility threshold, determine that the driving safety of the first vehicle includes a parking safety risk.

[0175] In one embodiment, the first vehicle is associated with a target device; the transceiver unit 803 is further configured to: if the driving safety of the first vehicle includes a driving safety risk, send an alarm message matching the type of the driving safety risk to the target device, so that the target device performs a risk alarm prompt based on the alarm message; where the driving safety risk includes at least one of the following: collision risk, road safety risk, lane change safety risk, and parking safety risk; if an abnormality occurs after the first vehicle stops driving, send an abnormality alarm message to the target device to perform an abnormality alarm prompt on the target device; where the abnormality of the first vehicle includes at least one of the following: the position of the first vehicle changes, the attitude of the first vehicle changes, and a collision occurs between the second vehicle and the first vehicle.

[0176] In one embodiment, the first vehicle is associated with a target device, and the transceiver unit 803 is further configured to: receive a setting control instruction sent by the target device; the processing unit 802 is configured to: set the trigger condition of the alarm prompt according to the setting control instruction.

[0177] It can be understood that the specific functions of the units of the data processing device described in the embodiments of the present application can be specifically implemented according to the methods in the above method embodiments, and the specific implementation process can refer to the relevant descriptions of the above method embodiments, which will not be elaborated here. In addition, the description of the beneficial effects of using the same method will not be elaborated either.

[0178] Next, the computer device provided in the embodiments of the present application will be described in detail.

[0179] The embodiments of the present application also provide a schematic structural diagram of a computer device. The schematic structural diagram of the computer device can be seen in Figure 9 ; the computer device can correspond to Figure 1a the data processing device shown in. The computer device may include: a processor 901, an input device 902, an output device 903, and a memory 904. The above-mentioned processor 901, input device 902, output device 903, and memory 904 are connected through a bus. The memory 904 is used to store a computer-readable storage medium, and the computer-readable storage medium includes a computer program. The processor 901 is used to execute the computer program stored in the memory 904.

[0180] In one embodiment, the processor 901 performs the following operations by running the computer program in the memory 904: obtaining a driving image captured during the driving of a first vehicle; the driving image is obtained by calling an image capture module and shooting from the driving perspective of the first vehicle; performing object analysis and processing on the driving image to obtain a target object; the target object refers to an object that affects the driving safety of the first vehicle; monitoring the state of the target object according to the driving image to obtain a state monitoring result; and determining the driving safety of the first vehicle according to the state monitoring result.

[0181] In one embodiment, when the processor 901 performs object analysis and processing on the driving image to obtain a target object, it is specifically used for: performing category recognition processing on the driving image to obtain a recognition result; the recognition result is used to indicate the object category in the driving image; and determining the target object according to the object category indicated by the recognition result.

[0182] In one embodiment, the number of driving images includes N frames, where N is an integer greater than 1; when the processor 901 determines the target object according to the object category indicated by the recognition result, it is specifically used for: performing motion difference analysis on the N driving images to obtain at least one candidate moving object; screening the at least one candidate moving object according to the recognition result to obtain a moving target, and determining the moving target as the target object.

[0183] In one embodiment, two adjacent driving images among the N driving images include: the k-th driving image and the (k - 1)-th driving image, where k ∈ [2, N]; when the processor 901 performs motion difference analysis based on the N driving images to obtain at least one candidate moving object, it is specifically configured to: perform difference processing on the pixel values at each position in the k-th driving image and the pixel values at the corresponding positions in the (k - 1)-th driving image to obtain a difference image, where the difference image includes the pixel difference values at each position; perform binarization processing on the difference image according to a preset difference threshold to obtain a binary image; perform connectivity analysis on the binary image to obtain at least one candidate moving object.

[0184] In one embodiment, the target object includes a moving target, the number of driving images is N frames, and N is an integer greater than 1; when the processor 901 monitors the state of the target object based on the driving images to obtain a state monitoring result, it is specifically configured to: monitor the target object based on the N driving images to obtain the displacement and motion duration of the target object; determine the motion speed of the target object according to the displacement and motion duration of the target object, and perform trajectory analysis based on the position and motion speed of the target object to obtain the motion trajectory of the target object; add the motion speed of the target object and the motion trajectory of the target object to the state monitoring result.

[0185] In one embodiment, when the processor 901 determines the driving safety of the first vehicle according to the state monitoring result, it is specifically configured to: determine the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle; if the collision possibility is greater than the possibility threshold, determine that the driving safety of the first vehicle includes a collision risk; if the collision possibility is less than or equal to the possibility threshold, determine that the driving safety of the first vehicle does not include a collision risk.

[0186] In one embodiment, the target object is a moving target; when the processor 901 determines the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle, it is specifically configured to: if the state monitoring result indicates that the target object is in a stationary state, determine the collision possibility between the target object and the first vehicle according to the stationary duration of the target object and the driving state of the first vehicle; if the state monitoring result indicates that the target object is in a moving state, determine the collision possibility between the target object and the first vehicle according to the driving state of the first vehicle and the motion speed of the target object.

[0187] In one embodiment, the target object includes a traffic road; when the processor 901 monitors the state of the target object according to the driving image to obtain a state monitoring result, it is specifically configured to: monitor the road surface condition of the traffic road according to the driving image to obtain a first monitoring result, where the first monitoring result is used to indicate the road surface condition of the traffic road; or, monitor the road surface height difference of the traffic road according to the driving image to obtain a second monitoring result, where the second monitoring result is used to indicate the road surface height difference of the traffic road; or, monitor the relative position between the first vehicle and the lane markings of the traffic road according to the driving image to obtain a third monitoring result, where the third monitoring result is used to indicate the distance between the driving position of the first vehicle and the lane markings of the traffic road; where the state monitoring result includes one or more of the first monitoring result, the second monitoring result, and the third monitoring result.

[0188] In one embodiment, when the processor 901 determines the driving safety of the first vehicle according to the state monitoring result, it is specifically configured to: if the state monitoring result includes the first monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the road surface condition of the traffic road indicated by the first monitoring result does not meet the safe driving condition; if the state monitoring result includes the second monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the road surface height difference of the traffic road indicated by the second monitoring result is greater than a preset height difference; if the state monitoring result includes the third monitoring result, determine that the driving safety of the first vehicle includes a road safety risk when the distance between the driving position of the first vehicle and the lane markings of the traffic road indicated by the third monitoring result is less than a preset distance threshold.

[0189] In one embodiment, the first vehicle is associated with a direction control module; when the processor 901 acquires the driving image collected during the driving of the first vehicle, it is specifically configured to: detect the lane change requirement of the first vehicle; if it is detected that the first vehicle has a lane change requirement, call the direction control module to adjust the shooting angle of the image acquisition module according to the lane change direction of the first vehicle; call the image acquisition module with the adjusted angle to shoot the traffic scene of the first vehicle to obtain the driving image.

[0190] In one embodiment, when determining the driving safety of the first vehicle according to the status monitoring result, the processor 901 is specifically configured to: determine lane change indication information according to the status monitoring result, where the lane change indication information is used to indicate whether the lane change condition of the first vehicle meets the preset lane change condition; if the lane change indication information indicates that the lane change condition of the first vehicle does not meet the preset lane change condition, determine that the driving safety of the first vehicle includes a lane change safety risk; if the lane change indication information indicates that the lane change condition of the first vehicle meets the preset lane change condition, determine that the driving safety of the first vehicle does not include a lane change safety risk.

[0191] In one embodiment, the first vehicle is a vehicle, and the processor 901 is further configured to: if it is detected that the first vehicle has a parking requirement, obtain a reference parking area of the first vehicle according to the driving picture; the reference parking area is set based on the spatial characteristics of the first vehicle; perform target detection on the reference parking area to obtain a detection result; the detection result is used to indicate whether there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle; if the detection result indicates that there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle is greater than the possibility threshold, determine that the driving safety of the first vehicle includes a parking safety risk.

[0192] In one embodiment, the first vehicle is associated with a target device; the output device 903 is further configured to: if the driving safety of the first vehicle includes a driving safety risk, send an alarm message matching the type of the driving safety risk to the target device, so that the target device performs a risk alarm prompt based on the alarm message; where the driving safety risk includes at least one of the following: collision risk, road safety risk, lane change safety risk, and parking safety risk; if an abnormality occurs after the first vehicle stops driving, send an abnormal alarm message to the target device to perform an abnormal alarm prompt on the target device; where the abnormality of the first vehicle includes at least one of the following: the position of the first vehicle changes, the attitude of the first vehicle changes, and a collision occurs between the second vehicle and the first vehicle.

[0193] In one embodiment, the first vehicle is associated with a target device, and the processor 901 is further configured to: receive a setting control instruction sent by the target device; set the trigger condition of the alarm prompt according to the setting control instruction.

[0194] It should be understood that the computer device described in the embodiments of the present application can execute the description of the data processing method in the corresponding previous embodiments, and can also execute the description of the data processing device in the corresponding previous embodiments, which will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.

[0195] In addition, it should be noted here that: The embodiments of the present application also provide a computer-readable storage medium, and a computer program is stored in the computer-readable storage medium. The computer program includes program instructions. When the processor executes the above program instructions, it can execute the methods in the corresponding embodiments described above. Therefore, details will not be repeated here. Figure 2 and Figure 4 Therefore, details will not be repeated here.

[0196] According to one aspect of the present application, a computer program product is provided. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device can execute the methods in the corresponding embodiments described above. Therefore, details will not be repeated here. Figure 2 and Figure 4 Therefore, details will not be repeated here.

[0197] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0198] The above-disclosed is only a preferred embodiment of the present application. Of course, the scope of rights of the present application cannot be limited thereby. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A data processing method, characterized in that, The method includes: Obtaining a driving image captured during the driving of a first vehicle; the driving image is captured by invoking an image capture module from the driving perspective of the first vehicle; Performing object analysis and processing on the driving image to obtain a target object; the target object refers to an object that affects the driving safety of the first vehicle; Monitoring the state of the target object according to the driving image to obtain a state monitoring result; Determining the driving safety of the first vehicle according to the state monitoring result.

2. The method according to claim 1, characterized in that The performing object analysis and processing on the driving image to obtain a target object includes: Performing category recognition processing on the driving image to obtain a recognition result; the recognition result is used to indicate the object category in the driving image; Determining a target object according to the object category indicated by the recognition result.

3. The method according to claim 2, wherein The number of the driving images is N frames, where N is an integer greater than 1; the determining a target object according to the object category indicated by the recognition result includes: Performing motion difference analysis on N frames of driving images to obtain at least one candidate moving object; Filtering the at least one candidate moving object according to the recognition result to obtain a moving target, and determining the moving target as the target object.

4. The method according to claim 3, wherein Two adjacent frames of the N frames of driving images include: the k-th frame of driving image and the (k - 1)-th frame of driving image, where k ∈ [2, N]; the performing motion difference analysis on N frames of driving images to obtain at least one candidate moving object includes: Performing difference processing on the pixel value of each position in the k-th frame of driving image and the pixel value of the corresponding position in the (k - 1)-th frame of driving image to obtain a difference image, and the difference image includes the pixel difference value of each position; Performing binarization processing on the difference image according to a preset difference threshold to obtain a binary image; Performing connectivity analysis on the binary image to obtain at least one candidate moving object.

5. The method according to claim 1, characterized in that The target object includes a moving target, and the number of the driving images is N frames, where N is an integer greater than 1; the monitoring the state of the target object according to the driving image to obtain a state monitoring result includes: Monitoring the target object according to N frames of driving images to obtain the displacement and motion duration of the target object; Determining the motion speed of the target object according to the displacement and motion duration of the target object, and performing trajectory analysis according to the position and motion speed of the target object to obtain the motion trajectory of the target object; Adding the motion speed of the target object and the motion trajectory of the target object to the state monitoring result.

6. The method according to claim 5, wherein The determining the driving safety of the first vehicle according to the state monitoring result includes: Determining the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle; If the collision possibility is greater than a possibility threshold, determining that the driving safety of the first vehicle includes a collision risk; If the collision possibility is less than or equal to the possibility threshold, it is determined that the driving safety of the first vehicle does not include a collision risk.

7. The method according to claim 6, characterized in that, The target object is a moving target; determining the collision possibility between the target object and the first vehicle according to the state monitoring result and the driving state of the first vehicle includes: If the state monitoring result indicates that the target object is in a stationary state, determine the collision possibility between the target object and the first vehicle according to the stationary duration of the target object and the driving state of the first vehicle; If the state monitoring result indicates that the target object is in a moving state, determine the collision possibility between the target object and the first vehicle according to the driving state of the first vehicle and the moving speed of the target object.

8. The method according to claim 1, wherein The target object includes a traffic road; monitoring the state of the target object according to the driving image to obtain a state monitoring result includes: Monitoring the road surface condition of the traffic road according to the driving image to obtain a first monitoring result, where the first monitoring result is used to indicate the road surface condition of the traffic road; or, Monitoring the road surface height difference of the traffic road according to the driving image to obtain a second monitoring result, where the second monitoring result is used to indicate the road surface height difference of the traffic road; or, Monitoring the relative position between the first vehicle and the lane markings of the traffic road according to the driving image to obtain a third monitoring result, where the third monitoring result is used to indicate the distance between the driving position of the first vehicle and the lane markings of the traffic road; Wherein, the state monitoring result includes one or more of the first monitoring result, the second monitoring result, and the third monitoring result.

9. The method according to claim 8, wherein Determining the driving safety of the first vehicle according to the state monitoring result includes: If the state monitoring result includes the first monitoring result, when the road surface condition of the traffic road indicated by the first monitoring result does not meet the safe driving condition, it is determined that the driving safety of the first vehicle includes a road safety risk; If the state monitoring result includes the second monitoring result, when the road surface height difference of the traffic road indicated by the second monitoring result is greater than the preset height difference, it is determined that the driving safety of the first vehicle includes a road safety risk; If the state monitoring result includes the third monitoring result, when the distance between the driving position of the first vehicle and the lane markings of the traffic road indicated by the third monitoring result is less than the preset distance threshold, it is determined that the driving safety of the first vehicle includes a road safety risk.

10. The method according to claim 1, wherein The first vehicle is associated with a direction control module; obtaining the driving image collected during the driving process of the first vehicle includes: Detecting the lane change requirement of the first vehicle; If it is detected that the first vehicle has a lane-changing requirement, the direction control module is called to adjust the shooting angle of the image acquisition module according to the lane-changing direction of the first vehicle; The image acquisition module with the adjusted angle is called to shoot the traffic scene of the first vehicle to obtain a driving image.

11. The method according to claim 10, wherein Determining the driving safety of the first vehicle according to the status monitoring result includes: Determining a lane-changing instruction message according to the status monitoring result, where the lane-changing instruction message is used to indicate whether the lane-changing condition of the first vehicle meets a preset lane-changing condition; If the lane-changing instruction message indicates that the lane-changing condition of the first vehicle does not meet the preset lane-changing condition, it is determined that the driving safety of the first vehicle includes a lane-changing safety risk; If the lane-changing instruction message indicates that the lane-changing condition of the first vehicle meets the preset lane-changing condition, it is determined that the driving safety of the first vehicle does not include a lane-changing safety risk.

12. The method according to claim 1, wherein The first vehicle is a vehicle, and the method further includes: If it is detected that the first vehicle has a parking requirement, a reference parking area of the first vehicle is obtained according to the driving image; the reference parking area is set based on the spatial characteristics of the first vehicle; Performing target detection on the reference parking area to obtain a detection result; the detection result is used to indicate whether there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle; If the detection result indicates that there is a moving target in the reference parking area and the collision possibility between the moving target and the first vehicle is greater than a possibility threshold, it is determined that the driving safety of the first vehicle includes a parking safety risk.

13. The method according to any one of claims 1 to 12, characterized in that, The first vehicle is associated with a target device; the method further includes: If the driving safety of the first vehicle includes a driving safety risk, an alarm message matching the type of the driving safety risk is sent to the target device, so that the target device performs a risk alarm prompt based on the alarm message; where the driving safety risk includes at least one of the following: collision risk, road safety risk, lane-changing safety risk, and parking safety risk; If an abnormality occurs after the first vehicle stops driving, an abnormality alarm message is sent to the target device to perform an abnormality alarm prompt on the target device; where the abnormality that occurs to the first vehicle includes at least one of the following: the position of the first vehicle changes, the attitude of the first vehicle changes, and a collision occurs between the second vehicle and the first vehicle.

14. The method according to claim 13, wherein The method further includes: Receiving a setting control instruction sent by the target device; Setting the trigger condition of the alarm prompt according to the setting control instruction.

15. A data processing device, characterized in that, Includes: An acquisition unit, configured to acquire a driving image collected during the driving of the first vehicle; The driving image is obtained by calling an image acquisition module and shooting from the driving perspective of the first vehicle. A processing unit for performing object analysis and processing on the driving screen to obtain a target object; the target object refers to an object that affects the driving safety of the first vehicle. The processing unit is further configured to monitor the state of the target object according to the driving screen to obtain a state monitoring result. The processing unit is further configured to determine the driving safety of the first vehicle according to the state monitoring result.

16. A computer device, characterized in that, It includes: A processor suitable for executing a computer program. A computer-readable storage medium storing a computer program, which when executed by the processor, executes the data processing method according to any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which when executed by the processor, executes the data processing method according to any one of claims 1-14.

18. A computer program product, characterized in that, The computer program product includes a computer program or computer instructions, and the computer program or computer instructions are executed by the processor to implement the data processing method according to any one of claims 1-14.

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