Situation-adaptive Vehicle Trajectory Prediction

Conference: AmE 2013 - 4. GMM-Fachtagung
02/19/2013 - 02/20/2013 at Dortmund, Deutschland

Proceedings: AmE 2013

Pages: 6Language: englishTyp: PDF

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Authors:
Omerbegovic, Said (Technische Universität Darmstadt, Germany)
Firl, Jonas (Adam Opel AG, Rüsselsheim, Germany)

Abstract:
Advanced driver assistance systems require accurate knowledge on the current and future states of all surrounding vehicles. Current approaches use the information on actual vehicle dynamics and a motion model, leading to short-time pre-dictions. In this work, we developed a framework to determine long-term predictions of the future vehicle trajectory for extra-urban traffic scenarios in dependence on the present situation. Scene analysis is used to yield the probability of occurrence on a set of maneuvers. Trajectory prediction is accomplished by an approach of Case-based reasoning. Based on the current observation, similar trajectories are retrieved from a database, resulting in long-term predictions for a given maneuver. The information of scene analysis is combined with these trajectory predictions to yield the final situation-adaptive long-term vehicle trajectory prediction. The performance of our method is verified with a comparison to conventional approaches in terms of error analysis over the prediction horizon.