LAUSR

Automated vehicle (AV) technologies promise to improve traffic safety and reduce driver workload in the near future. However, most current implementations have limited capabilities and require transition of control between the vehicle and the human driver during automation failures. For this reason, models of driver behavior have been widely studied to assist the design and development of AV technologies. Recent works have shown that driver behavior models grounded in human cognitive information processing achieve better generalization than prior methods. In this work, we applied active inference, a framework of human perception, cognition, and behavior based on the predictive processing theory, to model driver emergency braking responses to automation failures. We estimated the model parameters from experimental data and examined the model parameters using a factor analysis. We verified the model’s braking response prediction capability in counterfactual scenarios. Our results show that the model effectively captured braking reaction times and provided insight on the correspondence between the variations in driver parameters and behavior.