Stochastic Future Prediction in Real World Driving Scenarios

TL;DR

This paper addresses the challenge of predicting multiple possible future scenarios in autonomous driving by modeling motion stochastically and learning temporal dynamics in a latent space to improve robustness and safety.

Contribution

It introduces a novel approach that explicitly models motion stochastically and learns temporal dynamics in a latent space for better future prediction in driving scenarios.

Findings

Improved coverage of multiple future modes.

Enhanced robustness in uncertain driving environments.

Better scene understanding through stochastic modeling.

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.