TrajFlow: Learning the Distribution over Trajectories
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Predicting the future behaviour of people remains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty which is inherent to human behaviour. This paper studies an approach for probabilistic motion forecasting with improved accuracy in the predicted sample likelihoods. We are able to learn multi-modal distributions over the motions of an agent solely from data, while also being able to provide predictions in real-time. Our approach achieves state-of-the-art results on the inD dataset when evaluated with the standard metrics employed for motion forecasting. Furthermore, our approach also achieves state-of-the-art results when evaluated with respect to the likelihoods it assigns to its generated trajectories. Evaluations on artificial datasets indicate that the distributions learned by our model closely correspond to the true distributions observed in data and are not as prone towards being over-confident in a single outcome in the face of uncertainty.
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