Prediction of Personalized Driving Behaviors via Driver-Adaptive Deep Generative Models

Human drivers have complex and unique driving characteristics, even when driving in common, well-defined scenarios such as lane changes. In this study, we propose using probabilistic, deep generative models to predict personalized driving behavior including velocity, acceleration, and steering angle sequence. Probabilistic approaches are applied to model uncertainty in the driving behavior of individual drivers as a distribution, while surrounding vehicle and driver ID information are considered as given conditions in the distribution. We train individual driver models using real-world driving data, and use them to predict sequences of future driving behavior in dynamic environments, using historical data to take personal driving styles into account. Our results show that the proposed driver behavior modeling method is able to learn from a driver's vehicle operation data and their interactions with surrounding vehicles to reproduce their specific driving style.