RFID-Based Driving Fatigue Detection

Chao Yang, Xuyu Wang, Shiwen Mao

Abstract: With the growth of the number of vehicles and car accidents, driving safety is becoming increasingly important. There is a compelling need for an effective, low-cost driving fatigue detection system. In this paper, we propose an RFID based system, termed NodTrack, to detect the nodding movements of drivers, which is a key indicator of fatigue and one of the most dangerous motions during drowsy driving. The NodTrack system utilizes the phase difference between two RFID tags mounted on the back of a hat worn by the driver, to extract nodding features. We propose an effective technique to mitigate the cumulative error caused by frequency hopping in most FCC-compliant RFID systems, as well as a long short-term memory (LSTM) autoencoder model to learn the nodding features from calibrated data. The highly accurate detection performance of the proposed system is validated by our experimental study.