A probabilistic maneuver prediction framework for self-learning vehicles with application to intersections

Abstract

This contribution proposes a novel algorithm for predicting maneuvers at intersections. With applicability to driver assistance systems and autonomous driving, the presented methodology estimates a maneuver probability for every possible direction at an intersection. For this purpose, a generic intersection-feature, space-based representation is defined which combines static and dynamic intersection information with the dynamic properties of the observed vehicle, provided by a tracking module. A statistical behavior model is learned from previously recorded patterns by approximating the resulting feature space. Because the feature space consists of different types of features (mixed-feature space), a Bernoulli-Gaussian Mixture Model is applied as approximating function. Further, an online learning extension is proposed to adapt the model to the characteristics of different intersections.