Academic Journal of Engineering and Technology Science, 2023, 6(7); doi: 10.25236/AJETS.2023.060705.
Luoluo Wang, Hao Zhang, Junliang Chen, Jiexin Zhou, Xiyu Luo
Zhuhai City Polytechnic, Zhuhai, Guangdong, China
This article studies a multimodal fusion based vehicle fatigue driving detection system. This system comprehensively utilizes multiple signals such as EEG, EMG, and facial features for fatigue driving detection, and uses recognition algorithms for signal processing and feature extraction. With the support of hardware circuits and IoT technology, the system has higher accuracy and practical value. The experimental results indicate that the system can achieve good performance in accuracy and stability, and can play an important role in practical applications. Therefore, the multi-mode fusion vehicle fatigue driving detection system has broad application prospects and promotion value, which can provide drivers with a safer, more convenient and comfortable driving experience, while also reducing the incidence of traffic accidents.
Information fusion; Internet of Things; Fatigue driving
Luoluo Wang, Hao Zhang, Junliang Chen, Jiexin Zhou, Xiyu Luo. Analysis and Exploration of Automotive Fatigue Driving Detection Technology Based on Multimode Fusion. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 7: 24-29. https://doi.org/10.25236/AJETS.2023.060705.
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