Long-Term On-Board Prediction of Pedestrians in Traffic Scenes

Progress towards advanced systems for assisted or even autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in particular in bringing reliable driving to inner cities. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and pedestrian motion over such large time horizons. Our experimental results show that it is indeed possible to predict pedestrian movements at the desired time horizons. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.

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