Accident active defense system based on AI

Abstract

The invention discloses an AI-based accident active defense system, which comprises a vehicle-mounted terminal, an advanced driving assistance module, an interactive display screen and a monitoring platform, and is characterized in that the vehicle-mounted terminal is a terminal device with a networking function, can realize data transmission with the monitoring platform, is mounted in a vehicle, and is connected with the advanced driving assistance module; according to the invention, communication and data exchange are carried out through the communication service module and the remote control module, the monitoring platform transmits early warning information back to the communication service module and the vehicle-mounted terminal through the remote control module, and the vehicle-mounted terminal sends an instruction to the active security defense module through the AI intelligent processing module. On one hand, an alarm module of the vehicle-mounted terminal is used for warning a driver, reminding the driver of abnormal external vehicle conditions and making an avoidance response in time, and on the other hand, the active safety defense module is used for carrying out some forced operations on the vehicle and forcibly reducing the vehicle speed until the vehicle is stopped, so that the vehicle and personal safety are protected; and the occurrence probability of traffic accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the field of vehicle safe driving technology, and in particular to an AI-based active accident prevention system. Background Art

[0002] With the development of society, cars have become very familiar to people. As the number of cars increases, the probability of people encountering safety problems while driving is also increasing. Accidents occur frequently due to emergencies or the driver's own negligence, seriously threatening the lives of drivers and related personnel.

[0003] Therefore, it is necessary to provide an AI-based active accident prevention system to solve the above technical problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an AI-based active accident prevention system to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an AI-based active accident prevention system, comprising an on-board terminal, an advanced driving assistance module, an interactive display screen and a monitoring platform, wherein the on-board terminal is a terminal device with networking function, which can realize data transmission with the monitoring platform, and the on-board terminal is installed in the vehicle, and the on-board terminal has a built-in processor and an alarm module, and the processor is an AI intelligent processing module, and the AI ​​intelligent processing module includes a driver status detection module, a vehicle body status detection module and an active safety defense module, and the monitoring platform includes a communication service module, a cloud storage module, an image analysis module, an early warning module, a big data comprehensive analysis module and a remote control module, the communication service module is connected to the on-board terminal, the cloud storage module, the image analysis module, the big data comprehensive analysis module and the remote control module are connected to the communication service module, and the early warning module is connected to the big data comprehensive analysis module.

[0006] As a preferred technical solution of the present invention, the AI ​​intelligent processing module has multiple simulation scenarios built in in advance. The AI ​​intelligent processing module processes the data after receiving it, thereby making judgments on the monitored information. The AI ​​intelligent processing module can monitor the driver status and vehicle body status in real time.

[0007] As a preferred technical solution of the present invention, the processor also includes a photo-taking module, which includes an internal camera and an external camera. The internal camera takes real-time photos of the driver and transmits them to the driver status detection module. The driver's real-time status is analyzed by the driver status detection module, and the analyzed data is transmitted to the vehicle terminal. The alarm module includes a sound alarm and a light alarm.

[0008] As a preferred technical solution of the present invention, the driver status detection module identifies the driver's status, including whether the seat belt is worn, whether both hands are holding the steering wheel, fatigue driving, etc., and also includes whether the driver plays with mobile phones, makes phone calls, smokes, and other behaviors that affect driving safety. When it is detected that the above situations are not met, the on-board terminal will send signals that affect driving safety to the alarm module through the AI ​​intelligent processing module.

[0009] As a preferred technical solution of the present invention, the external camera is arranged outside the vehicle, and can take pictures of the scene outside the vehicle body in real time to obtain road conditions, and output the data to the active safety defense module. The active safety defense module obtains whether the traffic road conditions are abnormal. The active safety defense module transmits the abnormal signal to the AI ​​intelligent processing module, and the AI ​​intelligent processing module transmits the abnormal signal to the monitoring platform for analysis and processing.

[0010] As a preferred technical solution of the present invention, the abnormal signal detected by the active safety defense module is transmitted to the communication service module of the monitoring platform via the AI ​​intelligent processing module and the vehicle-mounted terminal. The communication service module transmits the abnormal signal to the big data comprehensive analysis module, and the abnormal signal is analyzed and processed via the big data comprehensive analysis module.

[0011] As a preferred technical solution of the present invention, the image analysis module analyzes and compares the image. When the abnormal signal transmitted by the vehicle terminal matches the data in the cloud storage module, the big data integration module sends a signal to the early warning module, and the early warning module promptly issues a front collision warning, a vehicle distance reminder, a lane departure warning, and a pedestrian distance reminder.

[0012] As an optimal technical solution of the present invention, the big data comprehensive analysis module has multiple abnormal scenarios preset in it, and various abnormal road condition scenarios are stored in the cloud storage module to form a database. The big data comprehensive analysis module calls the abnormal road condition scenarios in the cloud storage module and compares them with the signals transmitted from the vehicle terminal.

[0013] As a preferred technical solution of the present invention, the communication service module communicates and exchanges data with the remote control module. The communication service module can transmit the information of the vehicle-mounted terminal to the remote control module through a wireless network, thereby realizing real-time monitoring of the vehicle, and then realizing indirect communication between the remote detection personnel and the vehicle driver. The monitoring platform transmits the early warning information back to the communication service module and the vehicle-mounted terminal through the remote control module, and the vehicle-mounted terminal issues instructions to the active safety defense module through the AI ​​intelligent processing module.

[0014] As a preferred technical solution of the present invention, the advanced driving assistance module is arranged on the front side of the vehicle, which uses a combination of anti-collision radar and photography to obtain vehicle front information, realize front side collision warning, vehicle distance warning, lane departure warning and pedestrian distance warning, anti-collision radar can obtain the distance of the front vehicle, the distance between the front pedestrian and the vehicle, and detect the road conditions ahead by photography.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The AI-based active accident prevention system described in the present invention pre-installs multiple simulation scenarios within an AI intelligent processing module. The AI ​​intelligent processing module processes the received data and makes judgments based on the monitored information. The AI ​​intelligent processing module can monitor the driver's status and vehicle body status in real time to facilitate timely response. The internal camera takes a real-time photo of the driver and transmits it to the driver status detection module. The driver status detection module analyzes the driver's real-time status and transmits the analyzed data to the vehicle terminal. If an abnormal driver status is detected, the vehicle terminal sends a signal affecting driving safety to the alarm module through the AI ​​intelligent processing module. The alarm module uses sound and light to stimulate the driver to wake up and prevent accidents.

[0017] 2. The present invention describes an AI-based active accident prevention system. The present invention communicates and exchanges data with a remote control module through a communication service module. The communication service module can transmit information from the vehicle terminal to the remote control module through a wireless network, thereby realizing real-time monitoring of the vehicle, and further realizing indirect communication between remote detection personnel and the vehicle driver. The monitoring platform transmits the warning information back to the communication service module and the vehicle terminal through the remote control module. The vehicle terminal issues instructions to the active safety defense module through the AI ​​intelligent processing module. On the one hand, the driver is warned through the alarm module of the vehicle terminal to remind the driver that the external vehicle condition is abnormal and to make an evasive response in time. On the other hand, some forced operations are performed on the vehicle through the active safety defense module. When it is detected that the driver has not responded, the active safety defense module takes forced speed reduction until the vehicle stops, thereby protecting the vehicle and personal safety and reducing the probability of traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below with reference to the accompanying drawings and examples.

[0019] Figure 1 This is a schematic diagram of an AI-based active accident prevention system. DETAILED DESCRIPTION

[0020] The embodiments of the present invention are described below with reference to the accompanying drawings. In the process, to ensure clarity and convenience of the description, we may exaggerate the width of the lines or the size of the components in the drawings.

[0021] In addition, the following terms are defined based on the functions of the present invention and may vary depending on the intentions or practices of the user or operator. Therefore, these terms are defined based on the entire content of this specification.

[0022] like Figure 1 As shown, an AI-based active accident prevention system includes a vehicle-mounted terminal, an advanced driving assistance module, an interactive display screen and a monitoring platform. The vehicle-mounted terminal is a terminal device with networking function, which can realize data transmission with the monitoring platform. The vehicle-mounted terminal is installed in the vehicle. The vehicle-mounted terminal has a built-in processor and an alarm module. The processor is an AI intelligent processing module. The AI ​​intelligent processing module includes a driver status detection module, a vehicle body status detection module and an active safety defense module.

[0023] The AI ​​intelligent processing module has multiple simulation scenarios built in in advance. After receiving the data information, the AI ​​intelligent processing module processes it to make judgments on the monitored information. The AI ​​intelligent processing module is an existing technology with strong learning ability. The AI ​​intelligent processing module can monitor the driver status and vehicle body status in real time so as to make timely responses.

[0024] The processor also includes a photo-taking module, which includes an internal camera and an external camera. The internal camera is used to take real-time photos of the driver and transmits them to the driver status detection module. The driver status detection module analyzes the driver's real-time status and transmits the analyzed data to the vehicle terminal. The driver status detection module identifies the driver's status, including whether the seat belt is worn, whether both hands are holding the steering wheel, fatigue driving, etc. It also includes whether the driver plays with the mobile phone, makes calls, smokes, and other behaviors that affect driving safety. When it is detected that the above situations are not met, the vehicle terminal will send signals that affect driving safety to the alarm module through the AI ​​intelligent processing module. The alarm module includes sound alarms and light alarms, which stimulate the driver to wake up through sound and light to prevent accidents.

[0025] The external camera is set outside the vehicle and can take pictures of the scene outside the vehicle in real time to obtain road conditions, and output the data to the active safety defense module. The active safety defense module obtains whether there is any abnormality in the traffic road conditions. The active safety defense module transmits the abnormal signal to the AI ​​intelligent processing module, and the AI ​​intelligent processing module transmits the abnormal signal to the monitoring platform for analysis and processing.

[0026] The monitoring platform includes a communication service module, a cloud storage module, an image analysis module, an early warning module, a big data comprehensive analysis module and a remote control module. The communication service module is connected to the vehicle terminal, the cloud storage module, the image analysis module, the big data comprehensive analysis module and the remote control module are connected to the communication service module, the early warning module is connected to the big data comprehensive analysis module, the abnormal signal detected by the active safety defense module is transmitted to the communication service module of the monitoring platform via the AI ​​intelligent processing module and the vehicle terminal, the communication service module transmits the abnormal signal to the big data comprehensive analysis module, and the abnormal signal is analyzed and processed by the big data comprehensive analysis module. There are multiple abnormal scenarios preset in the big data comprehensive analysis module, and various abnormal road conditions are stored in the cloud storage module to form a database. The big data comprehensive analysis module calls the abnormal road condition scenario in the cloud storage module for comparison with the signal transmitted from the vehicle terminal. At the same time, the image analysis module analyzes and compares the image. When the abnormal signal transmitted from the vehicle terminal matches the data in the cloud storage module, it means that the external camera has captured the abnormal signal. If an abnormal road condition occurs, the big data integration module sends a signal to the warning module, which promptly issues front collision warnings, vehicle-to-vehicle distance reminders, lane departure warnings, and pedestrian distance reminders. The communication service module communicates and exchanges data with the remote control module. The communication service module can transmit the information of the on-board terminal to the remote control module through a wireless network, thereby realizing real-time monitoring of the vehicle and indirect communication between the remote detection personnel and the vehicle driver. The monitoring platform transmits the warning information back to the communication service module and the on-board terminal through the remote control module. The on-board terminal issues instructions to the active safety defense module through the AI ​​intelligent processing module. On the one hand, the driver is warned through the alarm module of the on-board terminal, reminding the driver of abnormal external vehicle conditions and making timely evasive responses. On the other hand, some forced operations are performed on the vehicle through the active safety defense module. When it is detected that the driver does not respond, the active safety defense module takes forced speed reduction until the vehicle stops, thereby protecting the vehicle and personal safety and reducing the probability of traffic accidents.

[0027] The advanced driving assistance module is arranged on the front side of the vehicle. It uses a combination of anti-collision radar and photography to obtain vehicle front information, realize front collision warning, vehicle distance warning, lane departure warning and pedestrian distance warning. The anti-collision radar can obtain the distance to the vehicle in front and the distance between the pedestrian in front and the vehicle. At the same time, it detects the road conditions in front through photography to assist safe driving. When bad signals and road conditions are detected, the advanced driving assistance module transmits the signal to the on-board terminal to warn the driver, reminding the driver to make preparations in advance, avoid bad road conditions to deal with emergencies, and thus reduce the occurrence of traffic accidents.

[0028] The specific implementation method is as follows: by pre-installing multiple simulation scenarios in the AI ​​intelligent processing module, the AI ​​intelligent processing module processes the data after receiving it, and thus makes a judgment on the monitored information. The AI ​​intelligent processing module can monitor the driver's status and vehicle body status in real time so as to make timely responses. The driver's real-time status is photographed by the internal camera and transmitted to the driver status detection module. The driver's real-time status is analyzed by the driver status detection module and the analyzed data is transmitted to the vehicle terminal. When the driver's status is detected to be abnormal, the vehicle terminal will send a signal affecting driving safety to the alarm module through the AI ​​intelligent processing module. The alarm module stimulates the driver to wake up through sound and light to prevent accidents. The scene outside the vehicle body can be photographed in real time through the external camera to obtain road conditions and output the data to the active safety defense module. The active safety defense module obtains whether the traffic road conditions are abnormal. The active safety defense module transmits the abnormal signal to the AI ​​intelligent processing module. The AI ​​intelligent processing module transmits the abnormal signal to the monitoring platform for analysis and processing. The communication service module of the monitoring platform transmits the abnormal signal to the big data comprehensive analysis module. The big data comprehensive analysis module analyzes and processes the abnormal signal. The big data comprehensive analysis module calls the abnormal signal in the cloud storage module. The road scene is compared with the signal transmitted from the vehicle terminal. At the same time, the image analysis module analyzes and compares the image. When the abnormal signal transmitted from the vehicle terminal matches the data in the cloud storage module, it means that the road scene captured by the external camera is indeed abnormal. The big data integration module sends a signal to the warning module, and the warning module promptly issues front collision warnings, vehicle distance reminders, lane departure warnings, and pedestrian distance reminders. The communication service module communicates and exchanges data with the remote control module. The communication service module can transmit the information of the vehicle terminal to the remote control module through the wireless network, thereby realizing real-time monitoring of the vehicle, and then realizing Currently, remote inspection personnel communicate indirectly with vehicle drivers. The monitoring platform transmits warning information back to the communication service module and the on-board terminal through the remote control module. The on-board terminal issues instructions to the active safety defense module through the AI ​​intelligent processing module. On the one hand, the driver is warned through the alarm module of the on-board terminal to remind the driver of abnormal external vehicle conditions and to make evasive responses in time. On the other hand, some forced operations are performed on the vehicle through the active safety defense module. When it is detected that the driver has not responded, the active safety defense module takes forced speed reduction until the vehicle stops, thereby protecting the vehicle and personal safety and reducing the probability of traffic accidents.

[0029] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. An AI-based active accident prevention system, characterized by: It includes a vehicle-mounted terminal, an advanced driving assistance module, an interactive display screen and a monitoring platform. The vehicle-mounted terminal is a terminal device with networking function, which can realize data transmission with the monitoring platform. The vehicle-mounted terminal is installed in the vehicle. The vehicle-mounted terminal has a built-in processor and an alarm module. The processor is an AI intelligent processing module. The AI ​​intelligent processing module includes a driver status detection module, a vehicle body status detection module and an active safety defense module. The monitoring platform includes a communication service module, a cloud storage module, an image analysis module, an early warning module, a big data comprehensive analysis module and a remote control module. The communication service module is connected to the vehicle-mounted terminal, the cloud storage module, the image analysis module, the big data comprehensive analysis module and the remote control module are connected to the communication service module, and the early warning module is connected to the big data comprehensive analysis module.

2. The AI-based active accident prevention system according to claim 1, characterized in that: The AI ​​intelligent processing module has multiple simulation scenarios built in in advance. After receiving the data, the AI ​​intelligent processing module processes it and makes judgments on the monitored information. The AI ​​intelligent processing module can monitor the driver's status and the vehicle body status in real time.

3. The AI-based active accident prevention system according to claim 1, characterized in that: The processor also includes a photo-taking module, which includes an internal camera and an external camera. The internal camera takes real-time photos of the driver and transmits them to the driver status detection module. The driver status detection module analyzes the driver's real-time status and transmits the analyzed data to the vehicle terminal. The alarm module includes a sound alarm and a light alarm.

4. The AI-based active accident prevention system according to claim 1, characterized in that: The driver status detection module identifies the driver's status, including whether the seat belt is worn, whether both hands are holding the steering wheel, fatigue driving, etc. It also includes whether the driver plays with the mobile phone, makes phone calls, smokes, and other behaviors that affect driving safety. When it is detected that the above situations are not met, the on-board terminal will send signals that affect driving safety to the alarm module through the AI ​​intelligent processing module.

5. The AI-based active accident prevention system according to claim 3, characterized in that: The external camera is set outside the vehicle and can take pictures of the scene outside the vehicle in real time to obtain road conditions, and output the data to the active safety defense module. The active safety defense module obtains whether there is any abnormality in the traffic road conditions. The active safety defense module transmits the abnormal signal to the AI ​​intelligent processing module, and the AI ​​intelligent processing module transmits the abnormal signal to the monitoring platform for analysis and processing.

6. The AI-based active accident prevention system according to claim 1, characterized in that: The abnormal signal detected by the active safety defense module is transmitted to the communication service module of the monitoring platform via the AI ​​intelligent processing module and the vehicle-mounted terminal. The communication service module transmits the abnormal signal to the big data comprehensive analysis module, and the abnormal signal is analyzed and processed by the big data comprehensive analysis module.

7. The AI-based active accident prevention system according to claim 1, characterized in that: The image analysis module analyzes and compares the image. When the abnormal signal transmitted by the vehicle terminal matches the data in the cloud storage module, the big data integration module sends a signal to the warning module, which promptly issues a front collision warning, a vehicle distance reminder, a lane departure warning, and a pedestrian distance reminder.

8. The AI-based active accident prevention system according to claim 1, characterized in that: The big data comprehensive analysis module has multiple abnormal scenarios preset in it, and various abnormal road condition scenarios are stored in the cloud storage module to form a database. The big data comprehensive analysis module calls the abnormal road condition scenarios in the cloud storage module and compares them with the signals transmitted from the vehicle terminal.

9. The AI-based active accident prevention system according to claim 7, characterized in that: The communication service module communicates and exchanges data with the remote control module. The communication service module can transmit the information of the vehicle terminal to the remote control module through a wireless network, thereby realizing real-time monitoring of the vehicle, and then realizing indirect communication between remote detection personnel and vehicle drivers. The monitoring platform transmits the early warning information back to the communication service module and the vehicle terminal through the remote control module, and the vehicle terminal issues instructions to the active safety defense module through the AI ​​intelligent processing module.

10. The AI-based active accident prevention system according to claim 1, characterized in that: The advanced driver assistance module is set on the front side of the vehicle. It uses a combination of anti-collision radar and photography to obtain vehicle front information, and realizes front collision warning, vehicle distance warning, lane departure warning and pedestrian distance warning. The anti-collision radar can obtain the distance to the vehicle in front and the distance between the pedestrian in front and the vehicle, and detect the road conditions ahead through photography.

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