Stochastic reachable sets of interacting traffic participants

Knowledge about the future development of a certain road traffic situation is indispensable for safe path planning of autonomous ground vehicles or action selection of intelligent driver assistance systems. Due to a significant uncertainty about the future behavior of traffic participants, the prediction of traffic situations should be computed in a probabilistic setting. Under consideration of the dynamics of traffic participants, their future position is computed probabilistically by Markov chains that are obtained with methods known from hybrid verification. The characteristic feature of the presented approach is that all possible behaviors of traffic participants are considered, allowing to identify any dangerous future situation. The novel contribution of this work is the explicit modeling of the interaction of traffic participants, which leads to a more accurate prediction of their positions. Results are demonstrated for different traffic situations.