Prediction of human driver intentions at a narrow passage in inner city traffic

Abstract

Autonomous vehicles have to be able to predict whether a human driver will wait at an unregulated inner city narrow passage or not to adapt its behaviour accordingly. To this end, a driving simulator study was conducted in which participiants were subjected to different cooperation behaviours during their approach to a narrow passage. They were asked to rate their intention afterwards. From the recorded trajectories, features which are specific to the scenario are derived. Therewith, Random Forest and Conditional Random Field classifiers for both intention and behaviour prediction are trained. The results show that robust prediction of driver intention and behaviour is possible.