Reproduce “Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction” on OpenResearch

About this paper

Aron Distelzweig, Andreas Look, Eitan Kosman, Faris Janjoš, Jörg Wagner, Abhinav Valada

Researchers from Bosch Center for Artificial Intelligence and the University of Freiburg introduce an information-theoretic framework to quantify and decompose uncertainty in trajectory prediction, showing that epistemic uncertainty is a reliable indicator for out-of-distribution scenarios and that diverse model ensembles enhance prediction robustness for autonomous driving systems.

Key insights

• Both aleatoric and epistemic uncertainties provide complementary information, and their combination (total uncertainty) correlates more strongly with prediction error than either component alone.
• Epistemic uncertainty serves as a particularly reliable indicator for detecting out-of-distribution scenarios, which is critical for robust autonomous driving.
• Model diversity, especially in mixed deep ensembles combining different architectures, significantly enhances prediction robustness and uncertainty quantification, particularly under challenging out-of-distribution conditions.

Summary by alphaXiv.