mlsanityData to 7 Oct 2026

Stochastic Future Prediction in Real World Driving Scenarios

arXiv 2209.10693 ↗First posted 21 Sep 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 100 references that are in the map. First posted on arXiv 21 Sep 2022.

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44 of 100 references.

References · 44

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32 more
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