Risk estimation for driving support and behavior planning in intelligent vehicles

Vehicles will be equipped with sensors and functions for highly automated driving in the foreseeable future. A big topic of research on the way to this goal is how to convey to these vehicles an understanding of the driving situations that is comparable to that of humans. For safe driving, this requires predicting how a scene will evolve and anticipating how dangerous it will potentially be. Risk estimation is a central ingredient in this process. In this paper, we describe how risk modeling frameworks help in managing the complexity of the driving task. We approach risk from the perspective of rare probabilistic events in environments where predictions might be inherently uncertain, and explain how this leads to a survival-based formulation which allows to model different types of risks encountered in driving situations within a single unified concept. In addition, we show how the framework can be used for driving behavior evaluation and risk-avoiding trajectory planning.