Driving Maneuver Prediction Based on Driver Behavior Observation

With respect to an increasing amount of driver assistance systems and automated driving functions, a higher chance of unappreciated action and intervention of these systems can be registered, which in turn lowers the acceptance by drivers and passengers. A reduction of unnecessary warnings and interventions can be achieved by making them adaptive to driver’s intentions and maneuvers planning. In order to learn which driver behavior indicates certain maneuver intentions, a rater-based method using video recordings is proposed in this paper. Three driving maneuvers, namely turning, changing lane and braking for a pedestrian who intends to cross the road, were chosen for analyzing their predictability due to behavior observation. As a first step, a driving simulator study was conducted in order to collect behavior data of 24 drivers. Subsequently, clearly distinguishable behavior classes for each maneuver were extracted from video data, resulting in five superior behavior categories with 29 behavioral classes. Based on these classes four human observers were trained to detect at the earliest convenience maneuver intentions. Overall in 97 % of all cases the observers could predict the maneuvers. Inter-rater reliabilities showed to be between κ= 0.30 and κ = 1.00.