  A key factor to optimal acceptance and comfort of automated vehicle features
is the driving style. Mismatches between the automated and the driver preferred
driving styles can make users take over more frequently or even disable the
automation features. This work proposes identification of user driving style
preference with multimodal signals, so the vehicle could match user preference
in a continuous and automatic way. We conducted a driving simulator study with
36 participants and collected extensive multimodal data including behavioral,
physiological, and situational data. This includes eye gaze, steering grip
force, driving maneuvers, brake and throttle pedal inputs as well as foot
distance from pedals, pupil diameter, galvanic skin response, heart rate, and
situational drive context. Then, we built machine learning models to identify
preferred driving styles, and confirmed that all modalities are important for
the identification of user preference. This work paves the road for implicit
adaptive driving styles on automated vehicles.
