Shengnian Ye
ABSTRACT
The purpose of this study is to explore the key points of dangerous driving behavior and driver health monitoring. For this purpose, a smart steering wheel capable of monitoring dangerous driving behavior and a steering wheel cover capable of monitoring physical health have been designed. The real-time face image acquisition mainly adopts MTCNN model. This paper studies driver safety monitoring from three perspectives: cockpit environment parameters, driver body information and driver behavior state, and puts forward a design scheme of driver safety monitoring system combining artificial intelligence and Internet of Things technology. Image recognition is used to detect the driver's behavior state. The fusion algorithm based on LSTM integrates PPG and electrocardiogram signals to collect the relevant data information obtained by various sensors. Through testing and analysis, the data is accurate and effective, and the system realizes effective monitoring and alarm of the dangerous factors existing in the driver's driving process. To a certain extent, it can reduce the road traffic accidents caused by the driver's own problems, and has certain application value. The system has the ability of real-time monitoring of driving behavior and health management at the same time.
- Lin Mei, Xu Jiafu, Wang Yuchen 2023. Research on Vehicle Driver Mental State Monitoring System based on Artificial Intelligence. Science and Technology Innovation and Application, 13(25):27-32.Google Scholar
- ZHANG Xinrong, F., XU Quanning, S., GONG Xinle, T., LI Xueyun. 2022. HUANG Jin: Integrated control strategy for path tracking of human machine co-driving vehicle. Journal of Automotive Safety and Energy 41(09), 130-139.Google Scholar
- Ernst T, Stefan S. 2023. Naturalistic driving study on the impact of an aftermarket blind spot monitoring system on the driver's behaviour of heavy goods vehicles and buses on reducing conflicts with pedestrians and cyclists. Accident Analysis and Prevention, 192.Google Scholar
- Araballi A, Shinde S. 2023. Real Time Implementation of Driver Drowsiness Monitoring System Using SVM Classifier [J]. International Journal of Engineering and Manufacturing (IJEM),13(3).Google Scholar
- Reza H A, Zoheir K, Ali M. 2023. In-vehicle wireless driver breath alcohol detection system using a microheater integrated gas sensor based on Sn-doped CuO nanostructures [J]. Scientific Reports, 13(1).Google Scholar
- Yongkai L, Shuai Z, Gancheng Z, 2023. A CNN-Based Wearable System for Driver Drowsiness Detection. [J]. Sensors (Basel, Switzerland), 23(7).Google Scholar
- Singh J B, Naveen K, Kumar R K, et a.l. 2023. System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures [J]. Sensors, 23(3).Google Scholar
- Qaiser A,Muhammad H, Daisuke S, 2022. Driver awareness collision/proximity detection system for heavy vehicles based on deep neural network [J]. Journal of Physics: Conference Series, 2330(1).Google Scholar
- PAN Zhi-Geng, F., LIU Rong-Fei, S., ZHNAG Ming-Min, T. 2019. Research on Fatigue Driving Detection Algorithm Based on Fuzzy Comprehensive Evaluation. Journal of Software 30(10), 2954-2963.Google Scholar
- Huang Zhichao, F., Zhao Hongmei, S., Chen Yixun, T., Tang Yaosen. 2021. Driver Fatigue and Dangerous Driving Detection System Based on Facial Recognition. Mechanical & Electrical Engineering Technology 50(12), 143-146.Google Scholar
- YANG Shi-qiang, F., YANG Jiang-tao, S., LI Zhuo, T., WANG Jin-hua, LI De-xin. 2021. Human action recognition based on LSTM neural network. Journal of Graphics 42(2), 174-181.Google Scholar
- MA Zhe, F. 2021. Research on Driver Hand Detection and Driving Behavior Analysis. Shandong: University of Jinan.Google Scholar
- HUANG Zhong, F., REN Fuji, S., HU Min, T., LIU Juan. 2019. A Real-time Expression Mimicking Method for Humanoid Robot Based on Dual LSTM Fusion. Robot 41(02), 137-146.Google Scholar
Index Terms
- Design and analysis of driver sign safety monitoring system based on multi-algorithm combination
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