TY  - JOUR
PY  - 2023//
TI  - Using multilevel hidden Markov models to understand driver hazard avoidance during the takeover process in conditionally automated vehicles
JO  - Proceedings of the Human Factors and Ergonomic Society annual meeting
A1  - Wang, Manhua
A1  - Parikh, Ravi
A1  - Jeon, Myounghoon
SP  - 698
EP  - 704
VL  - 67
IS  - 1
N2  - Ensuring a safe transition between the automation system and human operators is critical in conditionally automated vehicles. During the automation-to-human transition process, hazard avoidance plays an important role after human drivers regain the vehicle control. This study applies the multilevel Hidden Markov Model to understand the hazard avoidance processes in response to static road hazards as continuous processes. The three-state model--Approaching, Negotiating, and Recovering--had the best model fitness, compared to the four-state and five-state models. The trained model reaches an average of 66% accuracy rate on predicting hazard avoidance states on the testing data. The prediction performance reveals the possibility to use the hazard avoidance pattern to recognize driving behaviors. We further propose several improvements at the end to generalize our models into other scenarios, including the potential to model hazard avoidance as a basic driving skill across different levels of automation conditions.<p />  <p>Language: en</p>
LA  - en
SN  - 2169-5067
UR  - http://dx.doi.org/10.1177/21695067231192612
ID  - ref1
ER  -