CN114446010A - A vehicle-mounted robot system and its driving management method - Google Patents

Abstract

An in-vehicle robot system includes an OBD collection terminal, a video collection terminal, an intelligent robot and a cloud system, wherein the OBD collection terminal and the video collection terminal are both connected to an intelligent robot, and the intelligent robot is connected to a cloud system; the intelligent robot comprises: a driving behavior recognition module, Image processing module, local storage module, wireless communication module, voice playback module and screen display module; the cloud system includes: message queue, cache, database and IoT gateway. The driving management method of the vehicle-mounted robot system includes: conventional vehicle driving behavior management, dangerous driving behavior management, driving safety management, voice broadcast and light reminder, real-time ornament monitoring, violation prototype analysis and energy supply reminder. The system and its driving management method realize the functions of comprehensive vehicle monitoring, safe driving and intelligent management, and can effectively monitor the running vehicle.

Description

A vehicle-mounted robot system and its driving management method

technical field

The invention relates to the technical field of automobile auxiliary driving systems, in particular to a vehicle-mounted robot system and a driving management method thereof.

Background technique

The company's existing vehicle management and monitoring system manages the use process of the vehicle, monitoring the location of the vehicle through the GPS track of the vehicle, and the management method is relatively simple. In the process of driving a vehicle, the driver makes different control behaviors on the vehicle according to the driving conditions of the vehicle and the surrounding road environment, which has a very important and direct impact on the safety of the vehicle and the people on the vehicle.

The current general monitoring and management methods are relatively simple, either only monitoring the driving status of the driver, or only monitoring the driving status of the road, or only monitoring the location information of the vehicle, or only a single video monitoring, which cannot be stored with the cloud system. It is impossible to integrate multiple data for analysis and early warning.

With the requirements of refined management of enterprises and the requirements of driving safety, the needs are becoming more and more diversified. Therefore, it is an urgent need for enterprises to develop a vehicle-mounted robot system to realize comprehensive monitoring, safe driving and intelligent management of vehicles.

Through a public patent search, the following comparative documents were found:

CN112085347A-Disclosed is a system and method for digital allocation management of container truck fleet. Fleet end, user end and driver end together form a digital container truck transportation platform for data docking processing. There is a focus, and the target users of the three-terminal system are different. The car user terminal distributes the order, the fleet terminal accepts the order, and the driver terminal implements the order. The interactive page consists of three parts: real-time order grabbing, fleet management and qualification certification, and the real-time order grabbing page. Including map information and order management information, the demand-side digital field consists of three parts: cargo owners, third-party logistics and freight forwarders, and fleets. The container truck fleet digital allocation management system and method provide dispatchers with functions such as business acquisition, task distribution, task tracking, vehicle management, driver management, and qualification certification, which significantly improves the management efficiency of the fleet and reduces labor costs on the management side.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the deficiencies of the prior art, and to provide a vehicle-mounted robot system and a driving management method thereof. Vehicles are effectively monitored.

An in-vehicle robot system includes an OBD terminal acquisition terminal, a video acquisition terminal, an in-vehicle robot and a cloud system; the in-vehicle robot has a built-in driving behavior recognition module, an image processing module, a voice playback module and a screen display module; the driving behavior recognition module receives and processes the OBD The terminal collects the mileage and fuel consumption information, vehicle position information, vehicle status information and vehicle fault information sent through the vehicle CAN bus; the image processing module receives and processes the road status of the vehicle during the driving process sent by the video acquisition terminal, as well as pedestrians, lane lines, traffic signs , traffic light status and driver information; the voice playback module and the screen display module broadcast and display the information processed by the driving behavior recognition module and the image processing module, and send them to the cloud system for queuing and storage.

Preferably, the cloud system includes a message queue, a cache, a database and an IoT gateway; the data information remotely sent by the vehicle-mounted robot enters the cloud system through the IoT gateway for unified access and data interaction of the vehicle-mounted robot; the message queue transmits the user cloud system message; cache is used for data cache of cloud system; database stores data of cloud system.

Preferably, the vehicle-mounted robot also has a built-in local storage module and a wireless communication module; the local storage module stores data information collected by the OBD terminal and the video collection terminal; the wireless communication module is remotely connected to the IoT gateway of the cloud system.

Preferably, the driving behavior recognition module processes and judges the driving safety and the dangerous driving behavior of the driver; the image processing module processes the images and videos returned by the high-definition camera to obtain the driving road condition target information including lane information and traffic sign information; The image processing module is used to process the image or video returned by the high-definition camera to obtain the target information of driving road conditions, which includes lane information and traffic sign information; the local storage module stores the data collected by the vehicle-mounted robot and the results of analysis; wireless The communication module is remotely connected to the IoT gateway of the cloud system and communicates; the voice playback module provides real-time voice reminders and broadcasts to the cab; the screen display module displays the control interface and alarm information of the vehicle-mounted robot.

A driving management method for a vehicle-mounted robot system, the driving management method includes the following management contents: conventional vehicle driving behavior management, dangerous driving behavior management, driving safety management, voice broadcast and light reminder, real-time video monitoring, violation prototype analysis and energy supply reminder .

Preferably, the conventional vehicle driving behavior management includes the following steps:

Step 1.1: During the driving process of the vehicle, the OBD terminal collects the speed information and direction information of the vehicle in real time, and sends it to the driving behavior recognition module for analysis and calculation;

Step 1.2: The driving behavior recognition module calculates the acceleration of the vehicle through the change of the vehicle speed per unit time. When the acceleration change of the vehicle exceeds the predefined thresholds of rapid acceleration and rapid deceleration, it is determined that the vehicle has driving behavior of rapid acceleration and rapid deceleration ; Further calculate the vehicle turning speed through the change of the vehicle direction per unit time, and when the speed of the vehicle direction change exceeds the predefined sharp turning threshold, it is determined that the vehicle has a sharp turning driving behavior;

Step 1.3: The recognized driving behavior of the vehicle, through the voice playback module and the screen display module for voice broadcast and light flashing reminder;

Step 1.4: The vehicle driving behavior record is further connected to the IoT gateway through the wireless communication module, and uploaded to the cloud system for storage.

Preferably, the management of dangerous driving behavior includes the following steps:

Step 2.1: During the driving process of the vehicle, the video collection terminal collects the high-definition video camera of the driver's information, collects the driver's driving image information in advance, and transmits the image information to the image processing module for processing;

Step 2.2: The image processing module preprocesses the image and performs image enhancement to reduce the noise of the image in the image and change the brightness, color distribution, contrast and other parameters of the original image; image enhancement improves the clarity and quality of the image. , to make the outline of the object in the image clearer and the details more obvious; the image processing module transmits the processed image to the driving behavior recognition module for driving behavior recognition;

Step 2.3: The driving behavior recognition module first identifies whether there is a human body in the image. If no human body is detected, it continues to process the subsequent image information. If at least one human body is detected, the human body with the largest target is used as the driver to further identify the driver's Attribute behavior, through the built-in model, to determine the various behaviors of the driver, including: by identifying whether the driver has a mobile phone in the image, to identify whether the driver is using a mobile phone in the process of driving; by identifying the driver in the image. Whether there is smoke in the hands or mouth, to identify whether the driver is smoking during driving; by identifying the distance between the driver's hands and the steering wheel, to determine whether the driver's hands are off the steering wheel; through the degree of eye closure and mouth closure in multiple consecutive images The degree of opening and closing of the head is used to identify whether the driver is driving fatigued; the direction of the head in multiple consecutive images is used to determine whether the driver is not looking straight ahead, and there is a bowing behavior;

Step 2.4: For the identified dangerous driving behavior, through the voice playback module and the screen display module, voice broadcast and light flashing reminders;

Step 2.5: Further record the dangerous driving behavior through the wireless communication module, connect to the IoT gateway, and upload it to the cloud system for storage.

Preferably, the driving safety management includes the following steps:

Step 3.1: During the driving of the vehicle, a high-definition video camera that collects the road conditions during the driving of the vehicle in the video collection terminal collects the image information in front of the vehicle in advance, and transmits the image information to the image processing module for processing;

Step 3.2: the image processing module preprocesses the image and performs image enhancement to reduce the noise of the image in the image; the image processing module transmits the processed image to the driving behavior recognition module for driving behavior recognition;

Step 3.3: The driving behavior recognition module recognizes the objects in the image, and judges the driving safety of the vehicle through the built-in model, which specifically includes: processing the image of the lane in front of the vehicle to obtain the number of lanes and lane edge information, and further processing to obtain the information of the lane in the lane. The current car position of the vehicle can be determined by judging whether the vehicle is pressed against the sideline of the road lane within a predetermined period of time to determine whether the car deviates from the lane; the driving image in front of the vehicle is processed to identify obstacles such as vehicles and pedestrians ahead, within a certain time or distance. , to identify whether the vehicle has a potential collision hazard;

Step 3.4: For the recognized driving safety reminder, the voice broadcast module and the screen display module are used for voice broadcast and light flashing reminder;

Step 3.5: Further record the driving safety reminder through the wireless communication module, connect to the IoT gateway, and upload it to the cloud system for storage.

The advantages and technical effects of the present invention are:

Compared with the traditional vehicle management system, the vehicle-mounted robot system and the driving management method thereof of the present invention have the following advantages:

1. The video or image in front of the driver and the vehicle is collected through the video collection terminal, and the video and image are analyzed and processed on the terminal side to judge the driver's dangerous driving behavior, vehicle collision risk and lane departure.

2. Collect vehicle operation data through the OBD terminal, and analyze the driving behavior of the vehicle in real time.

3. The collected data is stored in the cloud, combined with the data of vehicle management and operation in the cloud, and comprehensively analyzes diversified data based on big data to manage, monitor and warn the vehicle in an all-round way.

Description of drawings

FIG. 1 is a system frame diagram of the vehicle-mounted robot of the present invention.

Detailed ways

In order to further understand the content, features and effects of the present invention, the following embodiments are exemplified and described in detail with the accompanying drawings. protection scope of the present invention.

As shown in FIG. 1 , the present invention is a vehicle-mounted robot system, and the system includes: OBD terminal acquisition 1, video acquisition terminal 2, intelligent robot, and cloud system, wherein OBD terminal acquisition 1, video acquisition terminal 2 are related to the intelligent robot. Connect, the intelligent robot is connected with the cloud system.

The intelligent robot includes: a driving behavior recognition module 3 , an image processing module 4 , a local storage module 5 , a wireless communication module 6 , a voice playback module 7 , and a screen display module 8 . The cloud system includes: an IoT gateway 9 , a message queue 10 , a cache 11 , and a database 12 .

Moreover, OBD terminal acquisition 1 collects vehicle data through the vehicle CAN bus, including four types of data:

Mileage and fuel consumption information: dashboard mileage, engine speed, dynamic fuel consumption, static fuel consumption, and average fuel consumption.

Vehicle location information: location, speed, direction.

Vehicle status information: battery voltage, temperature, fuel volume, tire pressure, etc.

Vehicle fault information.

Moreover, the video collection terminal 2 collects video through two high-definition video cameras, and collects the road status during the driving process of the vehicle, including passing vehicles, pedestrians, lane lines, traffic signs, traffic lights, etc.; The road environment and the state of the driver are collected.

Moreover, the driving behavior recognition module 3 is used to process and judge the driving behavior of driving safety and whether the driver has dangerous driving behavior.

Moreover, the image processing module 4 is used to process the image or video returned by the high-definition camera to obtain the target information of driving road conditions, and the target information of driving road conditions includes lane information and traffic sign information.

Moreover, the local storage module 5 is used for the intelligent robot to store the collected data and analysis results.

Moreover, the wireless communication module 6 is used for the intelligent robot to communicate with the new statistics in the cloud.

Moreover, the voice playing module 7 is used for real-time voice prompting and broadcasting.

Moreover, the screen display module 8 is used for interface display and alarm and reminder of the intelligent robot.

Moreover, the IoT gateway 9 is used to realize unified access and data interaction of the vehicle-mounted robot.

Moreover, the message queue 10 user cloud system message transmission realizes the decoupling, asynchrony, and peak shaving of the system.

Moreover, the cache 11 is used for data cache of the cloud system to improve the operating efficiency of the system.

Also, the database 12 is used for storage of cloud system data.

Moreover, judging the driving behavior of the vehicle specifically includes the following steps:

Step 1.1: During the driving process of the vehicle, the OBD terminal collects 1, collects the speed information and direction information of the vehicle in real time, and sends it to the driving behavior recognition module 3 for analysis and calculation.

Step 1.2: The driving behavior recognition module 3 calculates the acceleration of the vehicle through the change of the vehicle speed per unit time. When the acceleration change of the vehicle exceeds the predefined thresholds of rapid acceleration and rapid deceleration, it is determined that the vehicle is driving with rapid acceleration and rapid deceleration. Behavior. Further, the vehicle turning speed is calculated by the change of the vehicle direction in a unit time, and when the speed of the vehicle direction change exceeds a predefined sharp turning threshold, it is determined that the vehicle has a sharp turning driving behavior.

Step 1.3: The recognized driving behavior of the vehicle, through the voice playback module 7 and the screen display module 8, performs voice broadcast and light flashing reminders.

Step 1.4: Further record the driving behavior of the vehicle through the wireless communication module 6, connect to the IoT gateway 9, and upload it to the cloud system for storage.

Moreover, judging whether the driver has dangerous driving behaviors specifically includes the following steps:

Step 2.1: During the driving of the vehicle, the high-definition video camera that collects driver information in the video collection terminal 2 collects the driver's driving image information in advance, and transmits the image information to the image processing module 4 for processing.

Step 2.2: The image processing module 4 preprocesses the image and performs image enhancement to reduce the noise of the image in the image, and to change the brightness, color distribution, contrast and other parameters of the original image. Image enhancement improves the clarity and quality of the image, and makes the outline of the object in the image clearer and the details more obvious. The image processing module 4 transmits the processed image to the driving behavior recognition module 3 for driving behavior recognition.

Step 2.3: Driving behavior recognition module 3, first identify whether there is a human body in the image, if no human body is detected, continue to process the subsequent image information, if at least one human body is detected, the human body with the largest target is used as the driver to further identify the driver The attribute behavior of the driver is used to judge the various behaviors of the driver through the built-in model, which includes: identifying whether the driver is using a mobile phone during driving by identifying whether the driver has a mobile phone in the image; Whether there is smoke in the driver's hands or mouth to identify whether the driver is smoking while driving; by identifying the distance between the driver's hands and the steering wheel, to determine whether the driver's hands are off the steering wheel; through the degree of eye closure in consecutive images The degree of opening and closing of the mouth and the mouth are used to identify whether the driver is driving fatigued; the direction of the head in multiple consecutive images is used to determine whether the driver is not looking straight ahead, and there is a bowing behavior.

Step 2.4: For the identified dangerous driving behavior, through the voice playback module 7 and the screen display module 8, voice broadcast and light flashing reminders are performed.

Further, the dangerous driving behavior record is connected to the Internet of Things gateway 9 through the wireless communication module 6, and uploaded to the cloud system for storage.

Moreover, judging driving safety includes the following steps:

Step 3.1: During the driving process of the vehicle, the video acquisition terminal 2 collects a high-definition video camera of the road conditions during the driving process, pre-collects the image information in front of the vehicle, and transmits the image information to the image processing module 4 for processing.

Step 3.2: Similarly, the image processing module 4 preprocesses the image and performs image enhancement to reduce the noise of the image in the image. The image processing module 4 transmits the processed image to the driving behavior recognition module 3 for driving behavior recognition.

Step 3.3: The driving behavior recognition module 3, recognizes the objects in the image, and judges the driving safety of the vehicle through the built-in model, which specifically includes: processing the image of the lane in front of the vehicle to obtain the number of lanes and lane edge information, and further processing to obtain the lane The current car position in the vehicle is determined by judging whether the vehicle is pressed against the sideline of the road lane within a predetermined period of time to determine whether the car deviates from the lane; the driving image in front of the vehicle is processed, and obstacles such as vehicles and pedestrians in front are identified. , to identify if the vehicle has a potential collision hazard.

Step 3.4: For the identified driving safety reminder, the voice broadcast module 7 and the screen display module 8 are used for voice broadcast and light flashing reminder.

Step 3.5: The driving safety reminder record is further connected to the IoT gateway 9 through the wireless communication module 6, and uploaded to the cloud system for storage.

Moreover, voice broadcast and light reminder functions are provided, specifically including: for dangerous driving behaviors recognized by the vehicle-mounted robot, lane departure, and collision events, driving safety events, voice broadcast through the voice playback module 7, and light reminders through the screen display module 8; The illegal travel analysis and energy supply reminders identified by the cloud system will be notified by voice broadcast through the voice playback module 7.

Moreover, real-time video monitoring is provided, which specifically includes: managers choose to view the monitoring video of the driver of the designated vehicle driving or driving in front of the vehicle in the cloud system, the cloud system sends instructions to the intelligent robot through the Internet of Things gateway 9, and the intelligent robot receives the instructions. Then, the corresponding video information is collected through the video collection terminal 2, and sent to the cloud system through the intelligent robot, and the administrator monitors it in real time through the cloud system.

In addition, the analysis of illegal travel includes: when the cloud system receives the vehicle trajectory and determines that the vehicle travels, it analyzes the cloud vehicle travel application data in real time, and determines whether the vehicle has a vehicle application. If not, the cloud system passes the Internet of Things gateway. 9. Send a travel warning without a work order to the intelligent robot, and the intelligent robot will give a voice reminder through the voice playback module 7 to remind the driver to travel illegally; the cloud system receives the vehicle trajectory, analyzes the vehicle trajectory in real time, and compares the pre-defined coordinates of sensitive locations , within a predefined fixed time, when the vehicle trajectory is within the fixed range of the coordinates of the sensitive location, it is judged that the vehicle has a driving behavior of parking overtime in a sensitive location, and the cloud system issues a parking warning to the intelligent robot through the IoT gateway The robot makes a voice reminder through the voice playback module 7 to remind the driver to travel illegally.

Moreover, providing an energy supply reminder specifically includes: when the cloud system receives a vehicle application and determines that the vehicle needs to travel, pre-calculating the vehicle task itinerary based on the departure and destination in the vehicle application. When the fuel vehicle travels, the historical fuel consumption information of the vehicle is collected through the cloud system, and the OBD terminal collects the real-time upload of the vehicle fuel quantity, and calculates in real time whether the fuel meets the travel requirements. For the warning of insufficient fuel output, the intelligent robot will give a voice reminder through the voice playback module 7 to remind to refuel in time; when the electric vehicle travels, the historical power consumption information of the vehicle is collected through the cloud system, and the OBD terminal collects the real-time upload of the remaining power of the vehicle. Calculate in real time whether the power meets the travel requirements. If not, the cloud system sends a warning of insufficient power generation to the intelligent robot through the IoT gateway 9, and the intelligent robot sends a voice reminder through the voice playback module 7 to remind it to charge in time.

In addition, the specific models of the preferred modules in the present invention are as follows: OBD terminal acquisition, Y-BOX400; video acquisition terminal, HDL-6624HC; local storage module, Kingston SDC10; wireless communication module, Huawei ME909S-821; voice playback module, LM1875T; Screen display module, SDWn035T63T.

Finally, the parts not mentioned in the present invention all adopt mature products and mature technical means in the prior art.

It should be understood that for those skilled in the art, improvements or changes can be made according to the above description, and all these improvements and changes should fall within the protection scope of the appended claims of the present invention.

Claims (8)

1. a vehicle-mounted robot system, is characterized in that: comprise OBD terminal collection, video collection terminal, vehicle-mounted robot and cloud system; Described vehicle-mounted robot is built-in with driving behavior recognition module, image processing module, voice playback module and screen display module; The driving behavior recognition module receives and processes the mileage and fuel consumption information, vehicle location information, vehicle status information and vehicle fault information sent by the OBD terminal collected through the vehicle CAN bus; the image processing module receives and processes the vehicle driving process sent by the video collection terminal. Road status, as well as pedestrians, lane lines, traffic signs, traffic lights status and driver information; the voice playback module and screen display module broadcast and display the information processed by the driving behavior recognition module and the image processing module and send them to the cloud system Queue and store. 2. A vehicle-mounted robot system according to claim 1, characterized in that: the cloud system comprises a message queue, a cache, a database and an IoT gateway; the data information remotely sent by the vehicle-mounted robot enters the cloud via the IoT gateway The system is used for unified access and data interaction of the vehicle-mounted robot; the message queue transmits the messages of the user cloud system; the cache is used for the data cache of the cloud system; the database stores the data of the cloud system. 3. A vehicle-mounted robot system according to claim 2, characterized in that: the vehicle-mounted robot also has a built-in local storage module and a wireless communication module; the local storage module stores the data collected by the OBD terminal and the video collection terminal. information; the wireless communication module is remotely connected to the IoT gateway of the cloud system. 4. A vehicle-mounted robot system according to claim 3, characterized in that: the driving behavior recognition module processes and judges driving safety and the dangerous driving behavior of the driver; The video is processed to obtain the driving road condition target information including lane information and traffic sign information; the image processing module is used to process the images or videos returned by the high-definition camera to obtain the driving road condition target information, the driving road condition target information. The information includes lane information and traffic sign information; the local storage module stores the data collected by the vehicle-mounted robot and the analysis results; the wireless communication module is remotely connected to the Internet of Things gateway of the cloud system and communicates; Voice reminders and broadcasts are provided indoors; the screen display module displays the control interface and alarm information of the vehicle-mounted robot. 5. The driving management method of the vehicle-mounted robot system as claimed in claim 1, wherein the driving management method comprises the following management contents: conventional vehicle driving behavior management, dangerous driving behavior management, driving safety management, voice broadcast And lighting reminders, real-time video surveillance, violation prototype analysis and energy supply reminders. 6. The driving management method of a vehicle-mounted robot system according to claim 5, wherein the conventional vehicle driving behavior management comprises the following steps: Step 1.1: During the driving process of the vehicle, the OBD terminal collects the speed information and direction information of the vehicle in real time, and sends it to the driving behavior recognition module for analysis and calculation; Step 1.2: The driving behavior recognition module calculates the acceleration of the vehicle through the change of the vehicle speed per unit time. When the acceleration change of the vehicle exceeds the predefined thresholds of rapid acceleration and rapid deceleration, it is determined that the vehicle has driving behavior of rapid acceleration and rapid deceleration ; Further calculate the turning speed of the vehicle through the change of the vehicle direction per unit time, and when the speed of the vehicle direction change exceeds the predefined sharp turning threshold, it is determined that the vehicle has a sharp turning driving behavior; Step 1.3: The recognized driving behavior of the vehicle, through the voice playback module and the screen display module for voice broadcast and light flashing reminder; Step 1.4: The vehicle driving behavior record is further connected to the IoT gateway through the wireless communication module, and uploaded to the cloud system for storage. 7. The driving management method of a vehicle-mounted robot system according to claim 5, wherein the dangerous driving behavior management comprises the following steps: Step 2.1: During the driving process of the vehicle, the video collection terminal collects the high-definition video camera of the driver's information, collects the driver's driving image information in advance, and transmits the image information to the image processing module for processing; Step 2.2: The image processing module preprocesses the image and performs image enhancement to reduce the noise of the image in the image and change the brightness, color distribution, contrast and other parameters of the original image; image enhancement improves the clarity and quality of the image. , to make the outline of the object in the image clearer and the details more obvious; the image processing module transmits the processed image to the driving behavior recognition module for driving behavior recognition; Step 2.3: The driving behavior recognition module first identifies whether there is a human body in the image. If no human body is detected, it continues to process the subsequent image information. If at least one human body is detected, the human body with the largest target is used as the driver to further identify the driver. Attribute behavior, through the built-in model, to determine the various behaviors of the driver, including: identifying whether the driver uses a mobile phone during driving by identifying whether the driver has a mobile phone in the image; identifying the driver in the image Whether there is smoke in the hands or mouth, to identify whether the driver is smoking during driving; by identifying the distance between the driver's hands from the steering wheel, to determine whether the driver's hands are off the steering wheel; through the degree of eye closure and mouth closure in multiple consecutive images The degree of opening and closing of the head is used to identify whether the driver is driving fatigued; the direction of the head in multiple consecutive images is used to determine whether the driver is not looking straight ahead, and there is a bowing behavior; Step 2.4: For the identified dangerous driving behavior, through the voice playback module and the screen display module, voice broadcast and light flashing reminders; Step 2.5: Further record the dangerous driving behavior through the wireless communication module, connect to the IoT gateway, and upload it to the cloud system for storage. 8. The driving management method of a vehicle-mounted robot system according to claim 5, wherein the driving safety management comprises the following steps: Step 3.1: During the driving of the vehicle, a high-definition video camera that collects the road conditions during the driving of the vehicle in the video collection terminal collects the image information in front of the vehicle in advance, and transmits the image information to the image processing module for processing; Step 3.2: the image processing module preprocesses the image and performs image enhancement to reduce the noise of the image in the image; the image processing module transmits the processed image to the driving behavior recognition module for driving behavior recognition; Step 3.3: The driving behavior recognition module recognizes the objects in the image, and judges the driving safety of the vehicle through the built-in model, which includes: processing the image of the lane in front of the vehicle to obtain the number of lanes and lane edge information, and further processing to obtain the information of the lane in the lane. The current car position of the vehicle can be determined by judging whether the vehicle is pressed against the sideline of the road lane within a predetermined period of time to determine whether the car deviates from the lane; the driving image in front of the vehicle is processed, and obstacles such as vehicles and pedestrians in front are identified. , to identify whether the vehicle has a potential collision hazard; Step 3.4: For the recognized driving safety reminder, the voice broadcast module and the screen display module are used for voice broadcast and light flashing reminder; Step 3.5: Further record the driving safety reminder through the wireless communication module, connect to the IoT gateway, and upload it to the cloud system for storage.

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