Adaptive Driving Agent: From Driving a Machine to Riding with a Friend

The successful integration of automation in systems that affect human experiences requires the user acceptance of those automated functionalities. For example, the human comfort felt during a ride is affected by the automated control behavior of the vehicle. The challenge presented in this paper is how to develop an intelligent agent that learns its users? driving preferences and adjusts the vehicle control in real time, accordingly, minimizing the number of otherwise required manual interventions. This is a hard problem since users? preferences can be complex, context dependent and do not necessarily translate to the language of machines in a simple and straightforward manner. Our solution includes (1) a simulation test bed, (2) an adaptive intelligent interface and (3) an adaptive agent that learns to predict user's driving discomfort and it also learns to compute corrective actions that maximize user acceptance of automated driving. Overall, we conducted three user studies with 94 subjects in simulated driving scenarios. Our results show that our intelligent agent learned to successfully predict how to adjust the automated driving style to increase user? acceptance by decreasing the number of user manual interventions.