Improvement of driver- state estimation algorithm using multi-modal information

This paper aims to improve the performance of drivers´ state estimation algorithm using multi-modal information. While driving, drivers´ abnormal states may increase the likelihood of an accident. Thus, detection of the precise states of drivers is a key factor of preventing car accidents. Driver drowsiness, distraction, and workload was assessed through human-in-the-loop experimental data including vehicle, video, voice and physiological information. Dynamic Bayesian Network (DBN) was applied to assess the drivers´ state and integrate sensor data, whereas Hybrid Bayesian Network (HBN) was used previously. This paper shows the improved performance - DBS estimates drowsiness with 67.3% correct detection (n=4), visual distraction with 82.8% correct detection (n=16), cognitive distraction with 80.6% correct detection (n=16), and high workload with 86.6% correct detection (n=16). This research showed improved performance compared to our previous algorithm shown in Ryu et al. (2015).