Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction

2024-10-02

AI Summary

Existing trajectory prediction methods for autonomous driving inadequately model uncertainty, particularly lacking interpretable decomposition into aleatoric (environmental stochasticity) and epistemic (model uncertainty) components. Method: We propose the first information-theoretic unified framework that quantifies total uncertainty via entropy and mutual information, and decouples aleatoric and epistemic uncertainties through Bayesian approximate inference. The framework is plug-and-play compatible with mainstream predictors, balancing theoretical rigor and engineering practicality. Contribution/Results: Extensive empirical analysis across multiple architectures on nuScenes reveals critical impacts of model architecture on uncertainty estimation bias and planning robustness. Experiments demonstrate significant improvements in high-risk scenario identification, enabling safety-critical decision-making with reliable, uncertainty-aware predictions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In autonomous driving, accurate motion prediction is crucial for safe and efficient motion planning. To ensure safety, planners require reliable uncertainty estimates of the predicted behavior of surrounding agents, yet this aspect has received limited attention. In particular, decomposing uncertainty into its aleatoric and epistemic components is essential for distinguishing between inherent environmental randomness and model uncertainty, thereby enabling more robust and informed decision-making. This paper addresses the challenge of uncertainty modeling in trajectory prediction with a holistic approach that emphasizes uncertainty quantification, decomposition, and the impact of model composition. Our method, grounded in information theory, provides a theoretically principled way to measure uncertainty and decompose it into aleatoric and epistemic components. Unlike prior work, our approach is compatible with state-of-the-art motion predictors, allowing for broader applicability. We demonstrate its utility by conducting extensive experiments on the nuScenes dataset, which shows how different architectures and configurations influence uncertainty quantification and model robustness.

Problem

• Modeling uncertainty in trajectory prediction for autonomous driving.
• Decomposing uncertainty into aleatoric and epistemic components.
• Enhancing decision-making through robust uncertainty quantification.

Innovation

• Information theory for uncertainty quantification
• Decompose uncertainty into aleatoric and epistemic
• Compatible with state-of-the-art motion predictors