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WiDriver: Driver Activity Recognition System Based on WiFi CSI

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Abstract

Driver is the most active factor in people–vehicle–road system, so the driver activity monitoring has become increasingly important to support the driver assistant system application. The possibility of using device-free sensing technology for driver activity recognition in a simulated driving environment is investigated in this paper. We present WiDriver, among the first efforts to employ channel state information (CSI) amplitude variation data to intelligently estimate driving actions with commodity WiFi devices. The WiDriver proposes the scheme of screening sensitive input data from original CSI matrix of WiFi signals based on BP neural network algorithm; and the continuous driving activities classification algorithm by introducing the posture sequence, driving context finite automate model. Our experimental driving study in carriage with 5 subjects shows that the sensitive input selection scheme can achieve high accuracy of 96.8% in posture recognition and the continuous action classification algorithm can reach 90.76% maneuver operation detection rate.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (NSFC) Project Nos. 61671056, 61302065, 61304257, 61402033, Beijing Natural Science Foundation Project No. 4152036 and the Fundamental Research Funds for the Central Universities No. FRF-TP-15-026A2.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Shihong Duan, Tianqing Yu & Jie He

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  1. Shihong Duan
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  2. Tianqing Yu
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  3. Jie He
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Correspondence to Jie He.

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Duan, S., Yu, T. & He, J. WiDriver: Driver Activity Recognition System Based on WiFi CSI. Int J Wireless Inf Networks 25, 146–156 (2018). https://doi.org/10.1007/s10776-018-0389-0

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  • DOI: https://doi.org/10.1007/s10776-018-0389-0

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