Abstract
Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing
challenges in bringing reliable driving to inner cities, as
those are composed of highly dynamic scenes observed from
a moving platform at considerable speeds. 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 people trajectories over
such large time horizons. We pay particular attention to
modeling the uncertainty of our estimates arising from the
non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict
people trajectories at the desired time horizons and that our
uncertainty estimates are informative of the prediction error.
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