Set-Based Prediction of Traffic Participants on Arbitrary Road Networks

Safety is of paramount importance in automated driving. One of the main challenges ensuring safety is the unknown future behavior of surrounding traffic participants. Previous works ignore this uncertainty or often address it by computing probability distributions of other traffic participants over time. Probabilistic approaches make it possible to predict the collision probability with other traffic participants, but cannot formally guarantee (i.e., cannot mathematically prove for given assumptions) whether a planned maneuver is collision-free. Our approach addresses exactly this problem: instead of computing probability distributions, we compute an over-approximation of all possible occupancies of surrounding traffic participants over time. This makes it possible to prove whether an automated vehicle can possibly collide with other traffic participants. The presented algorithm for occupancy prediction works on arbitrary road networks and produces results within a fraction of the prediction horizon. Experiments based on real-world data validate our approach and show that we could not find a behavior of a traffic participant that is not enclosed in our prediction.