Learning interaction-aware probabilistic driver behavior models from urban scenarios

Human drivers have complex and individual behavior characteristics which describe how they act in a specific situation. Accurate behavior models are essential for many applications in the field of autonomous driving, ranging from microscopic traffic simulation, intention estimation and trajectory prediction, to interactive and cooperative motion planning. Designing such models by hand is cumbersome and inaccurate, especially in urban environments, with their high variety of situations and the corresponding diversity in human behavior. Learning how humans act from recorded scenarios is a promising way to overcome these problems. However, predicting complete trajectories at once is challenging, as one needs to account for multiple hypotheses and long-term interactions between multiple agents. In contrast, we propose to learn Markovian action models with deep neural networks that are conditioned on a driver's route intention (such as turning left or right) and the situational context. Step-wise forward simulation of these models for the different possible routes of all agents allows for multi-modal and interaction-aware scene predictions at arbitrary road layouts. Learning to predict only one time step ahead given a specific route reduces learning complexity, such that simpler and faster models are obtained. This enables the integration into particle-based algorithms such as Monte Carlo tree search or particle filtering. We evaluate the learned model both on its own and integrated into our previously presented dynamic Bayesian network for intention estimation and show that it outperforms our previous hand-tuned rule-based model.