Managing the complexity of inner-city scenes: An efficient situation hypotheses selection scheme

Due to the large number and the high variability of possible traffic situations, intersections are among the most accident-prone spots in inner-city traffic. To reliably assist the driving tasks elaborated risk assessment systems are needed. Current approaches are mainly based on the prediction of possible future trajectories of the involved traffic participants. However, considering the variability and combinatorics of intersection-related traffic situations, this becomes unfeasible for limited computational resources. Here, we present a general framework for an efficient situation hypotheses selection system. The selection process is based on reasoning about whether a particular situation results in a threat for the ego vehicle's behavior. Our approach combines the results of a probabilistic situation recognition and a fast risk assessment using state-of-the-art regression methods. We show that the proposed system is able to effectively reduce the number of unnecessarily considered situation hypotheses on average by over 80%.