Stochastic Future Prediction in Real World Driving Scenarios

Abstract

Uncertainty plays a key role in future prediction. The future is uncertain. That means there might be many possible futures. A future prediction method should cover the whole possibilities to be robust. In autonomous driving, covering multiple modes in the prediction part is crucially important to make safety-critical decisions. Although computer vision systems have advanced tremendously in recent years, future prediction remains difficult today. Several examples are uncertainty of the future, the requirement of full scene understanding, and the noisy outputs space. In this thesis, we propose solutions to these challenges by modeling the motion explicitly in a stochastic way and learning the temporal dynamics in a latent space.