Joint Driver Intention Classification and Tracking of Vehicles

Abstract

In this paper we present and validate a new modelling frame-work for joint driver intention classification and tracking of vehicles, a framework derived for automotive active safety systems. Such systems require reliable predictions of the traffic situation to act in time when a dangerous situation occur. Our proposal has two main benefits. First, it incorporates the intention of the driver into the vehicle motion model and thereby improves the prediction capability. The result is a multiple motion model where each model corresponds to a specific driver intent. Second, the connection between different driver plans and corresponding motion model enables a formal classification of the most likely driver intention. To validate our concept, we apply the motion model on real data using a particle filter implementation. Initial studies indicate promising performance.