Recognition and Prediction of Situations in Urban Traffic Scenarios

Abstract

The recognition and prediction of intersection situations and an accompanying threat assessment are an indispensable skill of future driver assistance systems. This study focuses on the recognition of situations involving two vehicles at intersections. For each vehicle, a set of possible future motion trajectories is estimated and rated based on a motion database for a time interval of 2-4 s ahead. Possible situations involving two vehicles are generated by a pairwise combination of these individual motion trajectories. An interaction model based on the mutual visibility of the vehicles and the assumption that a driver will attempt to avoid a collision is used to rate possible situations. The correspondingly favoured situations are classified with a probabilistic framework. The proposed method is evaluated on a real-world differential GPS data set acquired during a test drive of about 10 km, including three road intersections. Our method is typically able to recognise the situation correctly about 1.5-3 s before the last vehicle has passed its minimum distance to the centre of the intersection.