Accident analysis and intelligent intervention method and system based on intelligent connected automobile

Abstract

An accident analysis and intelligent intervention method based on an intelligent connected automobile comprises the following steps: acquiring driving data of a first driving user according to an intelligent camera to obtain first acquired data information; performing feature recognition on the first acquisition data information to obtain driving feature information, the driving feature information including a first recognition part feature and a second recognition part feature, the first recognition part feature being a hand feature, and the second recognition part feature being a face feature; performing distributed model training on the driving feature information to construct a first driving feature distribution model; first driving vehicle information of a first driving user is obtained. According to the invention, the hand and face features of the driving user are collected and analyzed in real time through the intelligent camera, and the driving behaviors such as fatigue driving and distracted driving can be identified in a refined manner, so that early warning can be performed in time, and traffic accidents caused by improper driving can be effectively prevented.

Description

Technical Field

[0001] The present invention belongs to the field of automobile technology, and specifically relates to an accident analysis and intelligent intervention method and system based on intelligent networked vehicles. Background Art

[0002] Intelligent Connected Vehicle (ICV) is an emerging automotive system that spans technology and industry. It organically combines the Internet of Vehicles and smart cars, is equipped with advanced on-board sensors, controllers, actuators and other devices, and integrates modern communication and network technologies. It can realize the intelligent information exchange and sharing between cars, people, roads, and background, and achieve safe, comfortable, energy-saving, and efficient driving, and ultimately realize driverless cars that can drive safely on the road. At present, the existing intelligent connected vehicle management system is not perfect, and it cannot effectively evaluate and warn the traffic safety of intelligent connected vehicles in areas prone to traffic accidents, and thus cannot avoid the secondary occurrence of traffic accidents. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an accident analysis and intelligent intervention method and system based on intelligent connected vehicles, so as to at least partially solve the above technical problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The present invention proposes an accident analysis and intelligent intervention method based on an intelligent networked vehicle. The method uses an automobile driving data acquisition system, the system is communicatively connected with an intelligent camera, and the method comprises: collecting driving data of a first driving user according to the intelligent camera to obtain first collected data information;

[0006] Obtain driving feature information by performing feature recognition on the first collected data information, wherein the driving feature information includes a first recognition part feature and a second recognition part feature, the first recognition part feature is a hand feature, and the second recognition part feature is a facial feature;

[0007] By performing distributed model training on the driving characteristic information, a first driving characteristic distribution model is constructed; and first driving vehicle information of the first driving user is obtained;

[0008] Based on the Internet of Things, uploading the first driving vehicle information to obtain first identification element template information of the analog driving vehicle type;

[0009] Determining a first analysis result based on the first recognition element template information and the first driving characteristic distribution model, wherein the first analysis result is adaptive adjustable information;

[0010] The first driving user is reminded according to the adapted adjustable information.

[0011] As a further solution of the present invention: an accident analysis and intelligent intervention method based on an intelligent networked vehicle, further comprising collecting vehicle driving data and vehicle component monitoring data;

[0012] Processing and analyzing the vehicle driving data to obtain the current dangerous driving behavior type; processing and analyzing the vehicle component monitoring data to obtain the current vehicle abnormality type;

[0013] Issue warning information and / or intervene in the vehicle based on the type of dangerous driving behavior and / or vehicle abnormality.

[0014] As a further solution of the present invention: the vehicle driving data includes any one of the following or a combination thereof: vehicle speed information, acceleration information, deceleration information, throttle information, brake information, steering wheel information, turn signal information, engine information, gear information, driving time information, air pressure system information and hydraulic system information;

[0015] The vehicle component monitoring data includes any one of the following or a combination thereof: vehicle braking force information, cumulative braking time information, cumulative braking mileage information, brake pad wear information, brake pad temperature information, cumulative tire mileage information, tire temperature information and tire speed information.

[0016] As a further solution of the present invention: the vehicle driving data is the acceleration information and the throttle information, and the current dangerous driving behavior type is a sudden acceleration dangerous driving behavior;

[0017] The processing and analysis of the vehicle driving data to obtain the current dangerous driving behavior type includes: within a certain preset time period, when the acceleration exceeds a first acceleration threshold, the acceleration duration exceeds a preset value, and the throttle opening exceeds a throttle opening threshold, the number of occurrences reaches a preset number, then determining that the current dangerous driving behavior type is a sudden acceleration dangerous driving behavior;

[0018] Sending out alarm information and intervening in the vehicle according to the type of dangerous driving behavior, including: sending out a sudden acceleration alarm message, and if the sudden acceleration dangerous driving behavior still occurs after the reminder, and the acceleration exceeds a second acceleration threshold, intervening in the vehicle and limiting the acceleration function.

[0019] As a further solution of the present invention: the vehicle driving data is the deceleration information and the braking information, and the current dangerous driving behavior type is a sudden deceleration dangerous driving behavior;

[0020] The processing and analysis of the vehicle driving data to obtain the current dangerous driving behavior type includes: within a certain preset time period, when the deceleration exceeds the deceleration threshold, the deceleration duration exceeds the preset value, and the brake switch quantity is effective, the number of occurrences reaches the preset number, then the current dangerous driving behavior type is determined to be a sudden deceleration dangerous driving behavior;

[0021] Issue warning information based on the type of dangerous driving behavior, including: issuing a sudden deceleration warning information.

[0022] As a further solution of the present invention: an accident analysis and intelligent intervention system based on an intelligent networked vehicle, comprising: a vehicle microcomputer controller, a monocular camera, a steering wheel angle sensor, an on-board display, a 5G CPE, a cloud server, a simulated cockpit and a remote visual transmission system;

[0023] Wherein, the automobile microcomputer controller is respectively connected to the steering wheel angle sensor, the vehicle display, the monocular camera and the cloud server;

[0024] The monocular camera is used to collect the lane deviation of the vehicle and send the lane deviation to the automobile microcomputer controller;

[0025] The steering wheel angle sensor is used to collect the steering wheel angle of the vehicle and send the steering wheel angle to the automobile microcomputer controller;

[0026] The automobile microcomputer controller is installed on the vehicle, and is used to calculate the lane departure standard deviation according to the lane departure amount, calculate the steering wheel turning rate according to the steering wheel angle, and determine whether the lane departure standard deviation and the steering wheel turning rate meet the preset conditions. When the preset conditions are met, a fatigue alarm message is sent to the driver through the vehicle display, and a preset time is counted down at the same time, and the lane departure standard deviation and the steering wheel turning rate within the preset time are calculated. When the lane departure standard deviation and the steering wheel turning rate within the preset time meet the preset conditions, the fatigue alarm message is uploaded to the cloud server through the 5GCPE, and the remote driving function is activated at the same time, and the vehicle wire control function is started. The remote vision transmission system transmits the real car driving field of view to the simulated cockpit, and the cloud server sends takeover information to the simulated cockpit, and the simulated cockpit takes over.

[0027] As a further solution of the present invention: the preset condition is: the lane departure standard deviation is greater than or equal to the lane departure standard deviation threshold, or the steering wheel turning rate is greater than or equal to the steering wheel turning rate threshold.

[0028] As a further solution of the present invention: an accident analysis and intelligent intervention system based on an intelligent networked vehicle, further comprising a monitoring module for collecting images of the rear side of the vehicle, the distance and position of each target object, and sending the collected images of the rear side of the vehicle, the distance and position of each target object to a central processing module;

[0029] The central processing module has a built-in image-based target recognition algorithm, which is used to receive the rear side image of the vehicle, the distance and position of each target object, and identify the category of each target object from the rear side image of the vehicle, and match the category of each identified target object with the collected distance and position of each target object to determine the distance and position of each category of target object; when the occupant triggers the inner door handle to prepare to open the door, the central processing unit receives the trigger signal of the door handle trigger sensor, and determines whether there is a collision risk based on the distance and position of each category of target objects at this time, and if there is a collision risk, sends an alarm signal to the early warning module / intervention module;

[0030] The warning module is used to receive and give corresponding warning prompts to the driver or passengers according to the warning signal;

[0031] The intervention module is used to receive and intervene in the driver's door opening behavior according to the alarm signal.

[0032] As a further solution of the present invention: the monitoring module includes a camera and a laser radar detector; the camera and the laser radar detector both establish communication with the central processing module;

[0033] The cameras include a plurality of cameras, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, and are used to collect the side and rear images of the vehicle, and send the collected side and rear images of the vehicle to the central processing module;

[0034] The laser radar detectors include multiple laser radar detectors, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, for detecting the distance and speed of the target object behind the vehicle, and sending the detected distance and speed of the target object behind the vehicle to the central processor.

[0035] As a further solution of the present invention: the camera and the laser radar detector are installed on the left and right rearview mirrors through an adjusting and fixing device, the control end of the adjusting and fixing device establishes communication with the central processing module, and the central processing module is also used to send a viewing angle adjustment instruction to the control end of the adjusting and fixing device, and the control end of the adjusting and fixing device is used to receive the viewing angle adjustment instruction and adjust the monitoring viewing angle of the camera and the laser radar detector to the optimal monitoring viewing angle according to the viewing angle adjustment instruction.

[0036] Implementing the technical solution of the present invention will have the following beneficial effects:

[0037] The present invention uses smart cameras to collect and analyze the hand and facial features of driving users in real time, and can finely identify driving behaviors, such as fatigue driving and distracted driving, so as to provide timely warnings and effectively prevent traffic accidents caused by improper driving.

[0038] The present invention constructs a driving characteristic distribution model by training a distributed model of driving characteristic information, which can more accurately identify and analyze driving behavior and improve the accuracy and timeliness of early warning and intervention. By using the Internet of Things technology, the driving vehicle information is uploaded and matched with the identification element template to achieve real-time monitoring of the vehicle status, providing comprehensive and accurate data support for intelligent intervention.

[0039] The present invention can identify dangerous driving behaviors and vehicle anomalies by collecting and analyzing vehicle driving data and vehicle component monitoring data, and promptly issue alarm information or conduct intelligent intervention to effectively avoid accidents. The monitoring module collects the rear and side images of the vehicle and target object information in real time, and cooperates with the image recognition algorithm of the processing module to accurately determine the collision risk of door opening behavior, and promptly issue warnings or intervene, significantly improving the safety of passengers.

[0040] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A framework diagram of an accident analysis and intelligent intervention method based on an intelligent connected vehicle proposed in an embodiment of the present invention;

[0043] Figure 2 A framework diagram of an accident analysis and intelligent intervention system based on intelligent connected vehicles proposed in an embodiment of the present invention.

[0044] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0045] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0046] It should be noted that the terms "first", "second", etc. are only used to distinguish between description and position description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the feature defined as "first" etc. may explicitly or implicitly include one or more of the features; similarly, when the quantity of certain features is not limited in the form of words such as "two" or "three", it should be noted that the feature also explicitly or implicitly includes one or more feature quantities;

[0047] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms such as "installation", "connection", "fixation" and the like should be understood in a broad sense; for example, it can be a fixed connection, a detachable connection, or an integral molding; it can be a mechanical connection, a direct connection, welding, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the drawings in the specification and in combination with specific circumstances.

[0048] In the description of the embodiments of the present invention, it should be understood that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the embodiments of the present invention.

[0049] like Figure 1 to Figure 2As shown, an embodiment of the present invention provides an accident analysis and intelligent intervention method based on an intelligent networked vehicle. The method applies a vehicle driving data collection system, and the system is communicatively connected with an intelligent camera. The method includes: collecting driving data of a first driving user according to the intelligent camera to obtain first collected data information; obtaining driving feature information by performing feature recognition on the first collected data information, wherein the driving feature information includes a first identification part feature and a second identification part feature, the first identification part feature is a hand feature, and the second identification part feature is a facial feature; constructing a first driving feature distribution model by performing distributed model training on the driving feature information; obtaining first driving vehicle information of the first driving user; uploading the first driving vehicle information based on the Internet of Things to obtain first identification element template information of an analog driving vehicle type; determining a first analysis result based on the first identification element template information and the first driving feature distribution model, wherein the first analysis result is adaptive adjustable information; and reminding the first driving user according to the adaptive adjustable information.

[0050] In a specific application of the embodiment of the present invention, the extracted driving feature information is input into a distributed model for training. The distributed model can use multiple computing devices to process data simultaneously, thereby improving training efficiency and model accuracy. The trained model can accurately identify and analyze the driver's driving behavior and status.

[0051] Through the Internet of Things technology, the information of the first driving vehicle (such as vehicle location, speed, driving route, etc.) is uploaded to the cloud. The cloud server constructs a first driving characteristic distribution model based on the uploaded information and driving characteristic information to analyze the driving behavior pattern. In the cloud, by comparing a large amount of driving vehicle information stored in the database, the vehicle type template information similar to the current vehicle type is identified. The template information contains the characteristic data of the vehicle of this type under normal driving conditions, which provides a reference for subsequent analysis.

[0052] Based on the first recognition element template information and the first driving characteristic distribution model, the system can analyze whether the current driving behavior deviates from the normal mode and generate a first analysis result, that is, adaptive adjustable information. This information may include the driver's fatigue level, distraction state, etc., which is used to determine whether there is a potential driving risk.

[0053] In a possible implementation, an accident analysis and intelligent intervention method based on an intelligent connected vehicle further includes collecting vehicle driving data and vehicle component monitoring data;

[0054] Process and analyze vehicle driving data to obtain the current type of dangerous driving behavior; process and analyze vehicle component monitoring data to obtain the current vehicle abnormality type;

[0055] Issue warning information and / or intervene in the vehicle based on the type of dangerous driving behavior and / or vehicle abnormality.

[0056] In specific applications, the embodiments of the present invention collect vehicle driving data in real time through various sensors and monitoring equipment installed on the vehicle, including but not limited to speed, acceleration, steering angle, braking status, etc., as well as vehicle component monitoring data, such as engine status, braking system status, tire pressure, etc. The data is transmitted to the data analysis platform in real time through the vehicle network. Combined with the type of dangerous driving behavior and the type of vehicle abnormality, the system can assess the degree of danger of the current driving situation. When a potential danger is detected, the system will immediately issue an alarm message to remind the driver to pay attention to safety. If necessary, the vehicle's intelligent control system will intervene, such as automatic deceleration, automatic braking, etc., to avoid or mitigate the occurrence of accidents. In an emergency, the system can automatically take measures, such as automatic braking, emergency avoidance, automatic deceleration, etc., to reduce the risk of accidents. In addition, the system can also receive real-time traffic information through a connection with a traffic management system, plan safe driving routes in advance, and avoid high-risk areas.

[0057] As a further solution of the present invention: the vehicle driving data includes any one of the following or a combination thereof: vehicle speed information, acceleration information, deceleration information, throttle information, brake information, steering wheel information, turn signal information, engine information, gear information, driving time information, air pressure system information and hydraulic system information;

[0058] The vehicle component monitoring data includes any one of the following or a combination thereof: vehicle braking force information, cumulative braking time information, cumulative braking mileage information, brake pad wear information, brake pad temperature information, cumulative tire mileage information, tire temperature information, and tire speed information;

[0059] The vehicle driving data includes acceleration information and throttle information, and the current dangerous driving behavior type is sudden acceleration dangerous driving behavior;

[0060] Processing and analyzing the vehicle driving data to obtain the current dangerous driving behavior type, including: within a certain preset time period, when the acceleration exceeds a first acceleration threshold, the acceleration duration exceeds a preset value, and the throttle opening exceeds the throttle opening threshold, the number of occurrences reaches a preset number, then determining that the current dangerous driving behavior type is a sudden acceleration dangerous driving behavior;

[0061] Alarm information is issued and the vehicle is intervened according to the type of dangerous driving behavior, including: issuing a sudden acceleration alarm message. If the dangerous driving behavior of sudden acceleration still occurs after the reminder, and the acceleration exceeds the second acceleration threshold, the vehicle will be intervened and the acceleration function will be restricted.

[0062] In specific applications of the embodiments of the present invention, intelligent connected vehicles collect vehicle driving data and component monitoring data in real time through various sensors and monitoring equipment. The driving data includes but is not limited to operating information such as vehicle speed, acceleration, deceleration, throttle, brake, steering wheel, etc.; the component monitoring data covers key information such as the brake system and tire status.

[0063] Real-time analysis of the large amount of data collected. For example, to identify dangerous driving behaviors such as sudden acceleration, the system will monitor acceleration information and throttle information. If it is detected that the acceleration exceeds the first threshold, the acceleration duration exceeds the set value, and the throttle opening exceeds a specific threshold for a preset number of times within a preset time period, the system will determine that there is a dangerous driving behavior of sudden acceleration.

[0064] Through algorithm analysis, the system can identify a variety of dangerous driving behavior types, such as sudden acceleration and sudden braking, to provide a basis for subsequent warnings and interventions. Once a dangerous driving behavior is determined, the system will immediately issue an alarm message to remind the driver to pay attention to driving safety. If the driver continues to engage in dangerous behavior after the warning, such as sudden acceleration exceeding the second acceleration threshold, the system will automatically take intervention measures, such as limiting the acceleration function and forcing deceleration to avoid potential accident risks.

[0065] In a possible implementation, the vehicle driving data is deceleration information and braking information, and the current dangerous driving behavior type is a sudden deceleration dangerous driving behavior;

[0066] Processing and analyzing the vehicle driving data to obtain the current dangerous driving behavior type, including: within a certain preset time period, when the deceleration exceeds the deceleration threshold, the deceleration duration exceeds the preset value, and the brake switch quantity is effective, the number of occurrences reaches the preset number, then the current dangerous driving behavior type is determined to be a sudden deceleration dangerous driving behavior;

[0067] Issue warning information based on the type of dangerous driving behavior, including: issuing a sudden deceleration warning information.

[0068] In a specific application of the embodiment of the present invention, the intelligent networked vehicle continuously collects the vehicle's driving data, including but not limited to deceleration information and braking information, through on-board sensors such as accelerometers and brake sensors. The collected raw data may contain noise or abnormal values, so pre-processing, such as filtering and smoothing, is required to improve the accuracy and reliability of the data.

[0069] Extract key features from the preprocessed data, such as the magnitude of deceleration, brake switch status, etc. These features will be used for subsequent dangerous behavior identification. Within a certain preset time period (such as a few seconds to tens of seconds), if the deceleration exceeds the preset deceleration threshold, and the deceleration duration exceeds the preset value (such as the duration exceeds a certain number of seconds), and the brake switch is valid (that is, the brake is pressed), then count a sudden deceleration event. When the count of such events reaches the preset number (such as three or more), the system determines that there is a dangerous driving behavior of sudden deceleration.

[0070] Once the dangerous driving behavior of sudden deceleration is identified, the system will immediately send out a sudden deceleration alarm message through the in-car alarm system to remind the driver to pay attention to driving safety. In addition, smart connected cars can further take intelligent intervention measures, such as automatic deceleration and emergency braking, to avoid or mitigate accidents.

[0071] In a possible implementation, an accident analysis and intelligent intervention system based on an intelligent connected vehicle includes: a vehicle microcomputer controller, a monocular camera, a steering wheel angle sensor, an on-board display, a 5G CPE, a cloud server, a simulated cockpit, and a remote visual transmission system;

[0072] Among them, the car microcomputer controller is connected to the steering wheel angle sensor, the car display, the monocular camera and the cloud server respectively;

[0073] The monocular camera is used to collect the lane deviation of the vehicle and send the lane deviation to the vehicle microcomputer controller;

[0074] The steering wheel angle sensor is used to collect the steering wheel angle of the vehicle and send the steering wheel angle to the vehicle microcomputer controller;

[0075] The automobile microcomputer controller is installed on the vehicle, and is used to calculate the lane departure standard deviation according to the lane departure amount, calculate the steering wheel turning rate according to the steering wheel angle, and determine whether the lane departure standard deviation and the steering wheel turning rate meet the preset conditions. When the preset conditions are met, a fatigue alarm message is sent to the driver through the vehicle display, and a preset time countdown is performed at the same time. The lane departure standard deviation and the steering wheel turning rate within the preset time are calculated, and when the lane departure standard deviation and the steering wheel turning rate within the preset time meet the preset conditions, the fatigue alarm message is uploaded to the cloud server through the 5G CPE, and the remote driving function is activated at the same time, and the vehicle wire control function is started. The remote visual transmission system transmits the real car driving field of view to the simulated cockpit, and the cloud server sends takeover information to the simulated cockpit, and the simulated cockpit takes over.

[0076] The preset conditions are: the lane departure standard deviation is greater than or equal to the lane departure standard deviation threshold, or the steering wheel turning rate is greater than or equal to the steering wheel turning rate threshold.

[0077] In a specific application of the embodiment of the present invention, the monocular camera installed on the vehicle captures the visual information in front of the vehicle in real time, analyzes the vehicle's driving trajectory through image processing technology, identifies lane lines, and calculates the vehicle's lane deviation. This information is sent to the vehicle microcomputer controller in real time. The steering wheel angle sensor monitors the driver's steering wheel angle changes, and transmits the steering wheel angle data to the vehicle microcomputer controller for analyzing driving behavior.

[0078] As the core processing unit of the system, the automobile microcomputer controller receives and processes the data sent by the monocular camera and the steering wheel angle sensor. It calculates the standard deviation of lane deviation based on the lane deviation amount to evaluate whether the vehicle has deviated from the lane; it calculates the steering wheel turning rate based on the steering wheel angle data to determine the frequency and amplitude of the driver's operation.

[0079] The automobile microcomputer controller determines whether the current driving state meets the characteristics of fatigue driving based on the preset lane departure standard deviation threshold and steering wheel turning rate threshold. If the lane departure standard deviation is greater than or equal to the set threshold, or the steering wheel turning rate is greater than or equal to the set threshold, it is considered that the driver may be in a fatigue state. When the preset conditions are met, the automobile microcomputer controller sends a fatigue driving warning message to the driver through the on-board display, and starts the preset time countdown to further monitor the driving state.

[0080] Within the preset time, if the lane departure standard deviation and steering wheel turning rate continue to meet the preset conditions, the car microcomputer controller uploads fatigue driving alarm information to the cloud server through the 5G CPE. At the same time, the system activates the remote driving function and the vehicle wire control function starts to realize remote control of the vehicle. After receiving the alarm information, the cloud server transmits the vehicle's real-time driving vision to the simulated cockpit through the remote vision transmission system, and the simulated cockpit takes over the operation to realize remote safe driving.

[0081] In a possible implementation, an accident analysis and intelligent intervention system based on an intelligent connected vehicle further includes a monitoring module for collecting rear side images of the vehicle, distances and positions of various target objects, and sending the collected rear side images of the vehicle, distances and positions of various target objects to a central processing module;

[0082] The central processing module has a built-in image-based target recognition algorithm, which is used to receive the rear side images of the vehicle, the distance and position of each target object, and identify the category of each target object from the rear side images of the vehicle, and match the category of each identified target object with the collected distance and position of each target object to determine the distance and position of each category of target objects; when the occupant triggers the inner door handle to open the door, the central processing unit receives the trigger signal of the door handle trigger sensor, and determines whether there is a collision risk based on the distance and position of each category of target objects at this time. If there is a collision risk, an alarm signal is sent to the early warning module / intervention module;

[0083] The warning module is used to receive and give corresponding warning prompts to the driver or passengers according to the warning signal;

[0084] The intervention module is used to receive and intervene in the driver's door opening behavior according to the alarm signal.

[0085] In the specific application of the embodiment of the present invention, the monitoring module is responsible for collecting real-time image information of the rear and side of the vehicle, as well as the distance and position data of each target object. It uses advanced sensor technologies such as millimeter wave radar, laser radar (LiDAR) and camera to obtain surrounding environment information with high precision. This information is then transmitted to the processing module for further analysis.

[0086] The processing module has a built-in deep learning-based image target recognition algorithm that can identify the categories of various target objects, such as pedestrians, bicycles, cars, etc., from the rear and side images of the vehicle. At the same time, the module matches the categories of each identified target object with its corresponding distance and position data to determine the relative distance of each category of target. When the occupant triggers the inner door handle to open the door, the processing module will immediately receive a signal from the door handle trigger sensor. Based on the distance and position information of the target object collected at this time, the processing module analyzes whether there is a collision risk. If it is determined that there is a collision risk, the processing module will send an alarm signal to the early warning module and the intervention module.

[0087] After receiving the alarm signal from the processing module, the early warning module will immediately issue an early warning prompt to the driver or passengers, such as through sound warnings, visual warning lights, etc., to increase the vigilance of the passengers and prevent potential collision accidents.

[0088] After receiving the alarm signal, the intervention module will intelligently intervene in the occupant's door opening behavior, such as temporarily locking the door through electronic lock control to prevent the occupant from opening the door when there is a risk of collision. This intervention measure can effectively prevent the occurrence of "door opening" accidents and ensure the safety of the occupants and surrounding pedestrians.

[0089] In a possible implementation, the monitoring module includes a camera and a laser radar detector; the camera and the laser radar detector both establish communication with the central processing module;

[0090] The cameras include a plurality of cameras, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, and are used to collect the side and rear images of the vehicle, and send the collected side and rear images of the vehicle to the central processing module;

[0091] The laser radar detectors include a plurality of laser radar detectors, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, and are used to detect the distance and speed of the target object behind the vehicle, and send the detected distance and speed of the target object behind the vehicle to the central processor;

[0092] The camera and the laser radar detector are installed on the left and right rearview mirrors through an adjusting fixture. The control end of the adjusting fixture establishes communication with the central processing module. The central processing module is also used to send a viewing angle adjustment instruction to the control end of the adjusting fixture. The control end of the adjusting fixture is used to receive the viewing angle adjustment instruction and adjust the monitoring angle of the camera and the laser radar detector to the optimal monitoring angle according to the viewing angle adjustment instruction.

[0093] In the specific application of the embodiment of the present invention, the camera is responsible for collecting image information from the side and rear of the vehicle. Through high-resolution imaging, it can clearly capture the dynamics of the rear vehicles and pedestrians, etc., and provide visual data support for the system. These image information are transmitted to the processing module in real time for image recognition and analysis to determine whether there is a potential danger.

[0094] The LiDAR detector focuses on detecting the distance and speed of the target objects behind and to the side of the vehicle. By emitting laser pulses and receiving reflected signals, the LiDAR can accurately measure the distance and relative speed of surrounding targets, providing the system with accurate distance measurement and speed information. These data are also sent to the processing module to build a three-dimensional model of the surrounding environment, so as to accurately locate obstacles and determine their speed.

[0095] To ensure the monitoring effect of the camera and lidar detector, the system is also equipped with an adjustment fixture. The control end of the device establishes communication with the processing module and can receive the viewing angle adjustment command from the processing module. The processing module sends the most appropriate viewing angle adjustment command based on real-time environmental information analysis to ensure that the camera and lidar detector are always in the best monitoring state. After receiving the command, the adjustment fixture will automatically adjust the viewing angle of the camera and radar to ensure that the collected data is both comprehensive and accurate, thereby achieving optimal monitoring of the vehicle's surrounding environment.

[0096] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0098] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An accident analysis and intelligent intervention method based on intelligent networked vehicles, characterized in that: The method uses a vehicle driving data collection system, the system is connected to a smart camera for communication, and the method includes: collecting driving data of a first driving user according to the smart camera to obtain first collected data information; Obtain driving feature information by performing feature recognition on the first collected data information, wherein the driving feature information includes a first recognition part feature and a second recognition part feature, the first recognition part feature is a hand feature, and the second recognition part feature is a facial feature; By performing distributed model training on the driving characteristic information, a first driving characteristic distribution model is constructed; and first driving vehicle information of the first driving user is obtained; Based on the Internet of Things, uploading the first driving vehicle information to obtain first identification element template information of the analog driving vehicle type; Determining a first analysis result based on the first recognition element template information and the first driving characteristic distribution model, wherein the first analysis result is adaptive adjustable information; The first driving user is reminded according to the adapted adjustable information.

2. The accident analysis and intelligent intervention method based on intelligent networked vehicles according to claim 1, characterized in that: It also includes collecting vehicle driving data and vehicle component monitoring data; Processing and analyzing the vehicle driving data to obtain the current dangerous driving behavior type; processing and analyzing the vehicle component monitoring data to obtain the current vehicle abnormality type; Issue warning information and / or intervene in the vehicle based on the type of dangerous driving behavior and / or vehicle abnormality.

3. The accident analysis and intelligent intervention method based on intelligent networked vehicles according to claim 1, characterized in that: The vehicle driving data includes any one of the following or a combination thereof: vehicle speed information, acceleration information, deceleration information, throttle information, brake information, steering wheel information, turn signal information, engine information, gear information, driving time information, air pressure system information and hydraulic system information; The vehicle component monitoring data includes any one of the following or a combination thereof: vehicle braking force information, cumulative braking time information, cumulative braking mileage information, brake pad wear information, brake pad temperature information, cumulative tire mileage information, tire temperature information and tire speed information.

4. The accident analysis and intelligent intervention method based on intelligent networked vehicles according to claim 1, characterized in that: The vehicle driving data is the acceleration information and the throttle information, and the current dangerous driving behavior type is a sudden acceleration dangerous driving behavior; The processing and analysis of the vehicle driving data to obtain the current dangerous driving behavior type includes: within a certain preset time period, when the acceleration exceeds a first acceleration threshold, the acceleration duration exceeds a preset value, and the throttle opening exceeds a throttle opening threshold, the number of occurrences reaches a preset number, then determining that the current dangerous driving behavior type is a sudden acceleration dangerous driving behavior; Sending out alarm information and intervening in the vehicle according to the type of dangerous driving behavior, including: sending out a sudden acceleration alarm message, and if the sudden acceleration dangerous driving behavior still occurs after the reminder, and the acceleration exceeds a second acceleration threshold, intervening in the vehicle and limiting the acceleration function.

5. The accident analysis and intelligent intervention method based on intelligent networked vehicles according to claim 1, characterized in that: The vehicle driving data is the deceleration information and the braking information, and the current dangerous driving behavior type is a sudden deceleration dangerous driving behavior; The processing and analysis of the vehicle driving data to obtain the current dangerous driving behavior type includes: within a certain preset time period, when the deceleration exceeds the deceleration threshold, the deceleration duration exceeds the preset value, and the brake switch quantity is effective, the number of occurrences reaches the preset number, then the current dangerous driving behavior type is determined to be a sudden deceleration dangerous driving behavior; Issue warning information based on the type of dangerous driving behavior, including: issuing a sudden deceleration warning information.

6. An accident analysis and intelligent intervention system based on intelligent networked vehicles, characterized in that: include: Automotive microcomputer controller, monocular camera, steering wheel angle sensor, vehicle display, 5G CPE, cloud server, simulated cockpit and remote visual transmission system; Wherein, the automobile microcomputer controller is respectively connected to the steering wheel angle sensor, the vehicle display, the monocular camera and the cloud server; The monocular camera is used to collect the lane deviation of the vehicle and send the lane deviation to the automobile microcomputer controller; The steering wheel angle sensor is used to collect the steering wheel angle of the vehicle and send the steering wheel angle to the automobile microcomputer controller; The automobile microcomputer controller is installed on the vehicle, and is used to calculate the lane departure standard deviation according to the lane departure amount, calculate the steering wheel turning rate according to the steering wheel angle, and determine whether the lane departure standard deviation and the steering wheel turning rate meet the preset conditions. When the preset conditions are met, a fatigue alarm message is sent to the driver through the vehicle display, and a preset time is counted down at the same time, and the lane departure standard deviation and the steering wheel turning rate within the preset time are calculated. When the lane departure standard deviation and the steering wheel turning rate within the preset time meet the preset conditions, the fatigue alarm message is uploaded to the cloud server through the 5GCPE, and the remote driving function is activated at the same time, and the vehicle wire control function is started. The remote vision transmission system transmits the real car driving field of view to the simulated cockpit, and the cloud server sends takeover information to the simulated cockpit, and the simulated cockpit takes over.

7. The accident analysis and intelligent intervention system based on intelligent networked vehicles according to claim 6, characterized in that: The preset condition is: the lane departure standard deviation is greater than or equal to a lane departure standard deviation threshold, or the steering wheel turning rate is greater than or equal to a steering wheel turning rate threshold.

8. The accident analysis and intelligent intervention system based on intelligent networked vehicles according to claim 6, characterized in that: It also includes a monitoring module for collecting images of the rear side of the vehicle, the distance and position of each target object, and sending the collected images of the rear side of the vehicle, the distance and position of each target object to the central processing module; The central processing module has a built-in image-based target recognition algorithm, which is used to receive the rear side image of the vehicle, the distance and position of each target object, and identify the category of each target object from the rear side image of the vehicle, and match the category of each identified target object with the collected distance and position of each target object to determine the distance and position of each category of target object; when the occupant triggers the inner door handle to prepare to open the door, the central processing unit receives the trigger signal of the door handle trigger sensor, and determines whether there is a collision risk based on the distance and position of each category of target objects at this time, and if there is a collision risk, sends an alarm signal to the early warning module / intervention module; The warning module is used to receive and give corresponding warning prompts to the driver or passengers according to the warning signal; The intervention module is used to receive and intervene in the driver's door opening behavior according to the alarm signal.

9. The accident analysis and intelligent intervention system based on intelligent networked vehicles according to claim 8, characterized in that: The monitoring module includes a camera and a laser radar detector; the camera and the laser radar detector both establish communication with the central processing module; The cameras include a plurality of cameras, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, and are used to collect the side and rear images of the vehicle, and send the collected side and rear images of the vehicle to the central processing module; The laser radar detectors include multiple laser radar detectors, which are respectively installed on the inner sides of the outer edges of the left and right rearview mirrors, for detecting the distance and speed of the target object behind the vehicle, and sending the detected distance and speed of the target object behind the vehicle to the central processor.

10. The accident analysis and intelligent intervention system based on intelligent networked vehicles according to claim 8, characterized in that: The camera and the laser radar detector are installed on the left and right rearview mirrors through an adjusting and fixing device. The control end of the adjusting and fixing device establishes communication with the central processing module. The central processing module is also used to send a viewing angle adjustment instruction to the control end of the adjusting and fixing device. The control end of the adjusting and fixing device is used to receive the viewing angle adjustment instruction and adjust the monitoring viewing angle of the camera and the laser radar detector to an optimal monitoring viewing angle according to the viewing angle adjustment instruction.

Images