Recognition of situation classes at road intersections

The recognition and prediction of situations is 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 seconds ahead. Realistic situations are generated by a pairwise combination of these individual motion trajectories and classified according to nine categories with a polynomial classifier. In the proposed framework, situations are penalised for which the time to collision significantly exceeds the typical human reaction time. The correspondingly favoured situations are combined by a probabilistic framework, resulting in a more reliable situation recognition and collision detection than obtained based on independent motion hypotheses. The proposed method is evaluated on a real-world differential GPS data set acquired during a test drive of 10 km, including three road intersections. Our method is typically able to recognise the situation correctly about 1-2 seconds before the distance to the intersection centre becomes minimal.