Development method of driver health monitoring and intelligent driving assistance system based on mobile ecology

Abstract

The invention provides a development method of a driver health monitoring and intelligent driving assistance system based on mobile ecology. The invention belongs to the technical field of new energy automobiles. The core technical problem to be solved is to comprehensively evaluate the health state of the driver to more truly reflect the current and future possible physical state of the driver, so that the decision-making ability and safety of the intelligent driving system are improved. In order to solve the problem, the system ensures real-time transmission of data by using a low-delay communication technology, and processes and analyzes the data by means of the local computing power of the vehicle-mounted terminal system and the big data analysis and machine learning technology of the cloud. And finally, according to the evaluation result of the health state of the driver, the environment in the vehicle is automatically adjusted to provide comfortable driving experience, and an intelligent driving system is activated when necessary to ensure driving safety. The main purpose of the invention is to comprehensively monitor the health state of the driver so as to improve the driving safety and comfort.

Description

Technical Field

[0001] New energy vehicle field. Background Art

[0002] In the new energy vehicle industry, the application and intelligence of the Internet of Vehicles have prompted users' driving habits to develop towards a safer and more comfortable state. In view of the current widespread application of Internet of Vehicles technology and mobile wearable devices for user health status, driver health monitoring and intelligent driving assistance technology based on mobile ecology is proposed. This technology can monitor the driver's health status through mobile devices such as smartphones and smart bracelets, and realize data sharing through interconnection with the vehicle system, thereby improving the comprehensive assessment of the driver's physical condition during driving by the on-board intelligent system, and then optimizing the application strategy of intelligent driving to achieve the purpose of improving driving safety. . Summary of the invention

[0003] The key components of the system architecture include: data collection, data transmission, data processing and analysis, decision-making and control. Among them, the data collection node mainly uses smart wearable devices to collect the driver's physiological data such as heart rate and sleep quality through its built-in biosensors and heart rate monitors, and uses smartphones as data aggregation points to collect health data from various smart wearable devices.

[0004] The data transmission node uses smart mobile devices as intermediate nodes for data transmission, and transmits data from smart wearable devices to the vehicle-mounted system of smart connected vehicles through low-latency communication protocols such as Bluetooth Low Energy (BLE) and Wi-Fi Direct. The vehicle-mounted system receives data from mobile devices and, when necessary, uses long-distance wireless transmission technologies such as 5G and cloud services to further process the data.

[0005] The data processing and analysis node is based on local data processing and analysis. It mainly uses the local computing power and data processing algorithms of the vehicle system to perform preliminary processing and analysis on the collected data, such as data cleaning and status judgment. When necessary, the cloud platform uses big data analysis, machine learning and other technologies to conduct in-depth analysis and mining of the collected data, thereby providing a higher level of decision support.

[0006] The decision-making and control node is where the vehicle-mounted execution system's working strategy is determined based on the vehicle-mounted system's analysis of the data.

[0007] The working process of the system is as follows: first, the smart wearable device monitors the driver's heart rate, sleep quality and other physiological indicators at night or in real time, and records relevant data in real time through built-in sensors. The mobile device can establish a connection with the smart wearable device through Bluetooth and aggregate the data to the mobile phone. When the driver powers on the vehicle for the first time, the mobile device will automatically establish a connection with the vehicle system through Bluetooth, and the health data collected will be shared with the vehicle system.

[0008] After the vehicle system receives data from the mobile device, it will first perform preliminary processing and analysis locally. If it is judged that the data is abnormal, the data will be uploaded to the cloud service platform and the cloud computing platform will be used for deeper data analysis, so as to further evaluate the driver's health status at this time and return the analysis results to the vehicle system.

[0009] After the vehicle system evaluates the driver's condition based on the data, if the result shows that the driver is in a bad condition (such as poor sleep quality), the system will automatically adjust the cabin systems such as seats, fragrance, music, and lighting to a mode suitable for the driver in this state to provide a comfortable driving experience. At the same time, in order to prevent the driver from driving fatigue, the system will provide the driver with health suggestions such as rest through voice.

[0010] When the system determines that the driver is in poor health, ADAS will be set to aggressive mode, which means it will intervene in vehicle control more frequently than in normal conditions and, if necessary, completely take over control of the vehicle to ensure driving safety.

[0011] In order to realize the system functions, the key points applied in system development are: 1) As an important source of data collection, the accuracy and authenticity of the collection of human physiological information by smart wearable devices need to be further improved. Therefore, in order to ensure the high accuracy of the system, it will be key to replace ordinary sensors with high-precision biosensors.

[0012] 2) Based on the timeliness of data sharing between mobile devices and vehicle systems, relevant requirements are put forward for the application of low-latency communication technologies, such as Bluetooth 5.0 and Wi-Fi Direct, which are widely used. At the same time, in order to reduce the delay that may be caused by data transmission, edge computing is used to perform preliminary processing and analysis of data at the device end or gateway to reduce the amount of data transmission and speed up the response speed of the system.

[0013] 3) When it is necessary to deeply analyze and process the data, the cloud computing platform becomes the focus. At this time, the data is analyzed through big data, AI technology, etc. to accurately determine the driver's health status and predict the driver's possible behavior. Through the comprehensive analysis of historical data on the cloud platform, the driver's possible health trend can be predicted. On the one hand, it can provide timely health improvement suggestions for driving, and on the other hand, it can predict the driving behavior pattern in the driving environment, providing basic data guarantee for safe driving.

[0014] Integration with DMS: In current vehicle systems, the Driver Monitoring System (DMS) is mainly used to monitor the real-time status of the driver during driving to ensure driving safety. It uses the in-vehicle camera to capture information such as the driver's facial expressions and eye opening and closing to determine whether the driver is in a state of fatigue or distraction.

[0015] The driver health monitoring and intelligent driving assistance system based on the mobile ecosystem collects the driver's health data through smart wearable devices and mobile devices, especially the driver's health status information after sleeping and waking up (such data includes data for a time period and real-time data, such as sleep data is time period data, and heart rate data is real-time data). The accumulation of this data can be used to evaluate future health trends through historical data analysis, and this data can be shared with the vehicle system to provide a more comprehensive driver status assessment.

[0016] In actual in-vehicle applications, the driver's possible physical condition can be assessed through physiological data such as heart rate and sleep quality from smart wearable devices, and the driver's real-time fatigue state can be analyzed through information such as the driver's facial expressions and eye opening and closing conditions monitored by DMS. By combining visual information and physiological data, a comprehensive assessment of the driver's overall condition can be made.

[0017] According to the comprehensive evaluation results, the intelligent driving assistance system will implement a graded intervention strategy based on the driver's status, such as giving a voice prompt when slightly fatigued, automatically taking over vehicle control when seriously fatigued, etc. At the same time, according to the different health conditions of the driver, the intelligent planning system will provide the driver with the system's safest driving route (which may not be the shortest, but must be the easiest to drive), such as avoiding traffic jams and complex roads (which requires the driver to pay more attention and may aggravate physical discomfort), and in extreme cases (such as sudden illness, unbearable migraine, etc.), the intelligent driving system will completely take over the vehicle to park it safely and contact rescue autonomously.

[0018] System advantages: The driver health monitoring and intelligent driving assistance system based on the mobile ecosystem can use smart products such as wearable devices that users carry with them on a daily basis as data input ports. At the same time, it combines real-time data such as DMS and in-vehicle health systems to conduct a comprehensive assessment of the driver's health status, so as to more realistically reflect their current and possible future physical conditions, thereby improving the decision-making ability and safety of the intelligent driving system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Driver health monitoring and intelligent driving assistance system architecture based on mobile ecology.

[0020] Figure 2 In-vehicle health data features.

Claims

1. A method for developing a driver health monitoring and intelligent driving assistance system based on mobile ecology, characterized in that: By using smart mobile devices as intermediate nodes for data transmission and through low-latency communication protocols, data is transmitted from smart wearable devices to the vehicle-mounted system of smart connected vehicles. The vehicle-mounted system receives data from mobile devices and, when necessary, uses cloud services through long-distance wireless transmission technologies such as 5G to further process the data.

2. A method for developing a driver health monitoring and intelligent driving assistance system based on a mobile ecosystem, characterized in that local data processing and analysis mainly uses the local computing power and data processing algorithms of the vehicle system to process and analyze the collected data, and the data transmitted to the cloud is deeply analyzed and mined through the cloud platform using big data analysis, machine learning and other technologies.

3. A method for developing a driver health monitoring and intelligent driving assistance system based on mobile ecology, characterized in that: After the system evaluates the driver's condition based on the data, if the result shows that the driver is in a bad condition, it will automatically adjust the system in the cockpit to a mode suitable for the driver in this state to provide a comfortable driving experience. At the same time, it provides health advice to the driver through voice, automatically activates the ADAS aggressive mode, and completely takes over the control of the vehicle when necessary to ensure driving safety.

4. A method for developing a driver health monitoring and intelligent driving assistance system based on mobile ecology, characterized in that: The driver health data collected by smart wearable devices and mobile devices are integrated with the data of the driver monitoring system (DMS) to provide a more comprehensive driver status assessment and implement a graded intervention strategy based on the comprehensive assessment results.

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