Scenario-aware Uncertainty Quantification for Trajectory Prediction with Statistical Guarantees

2025-12-05

AI Summary

To address unreliable uncertainty quantification and poor adaptability to heterogeneous real-world scenarios in autonomous driving trajectory prediction, this paper proposes a scene-aware uncertainty quantification framework. First, trajectories are projected into the Frenet coordinate system to decouple longitudinal and lateral motion dynamics. Second, we introduce CopulaCPTS—a novel copula-based conformal calibration method—that enables scene-adaptive generation of multivariate time-series prediction intervals for the first time. Third, we design a Trajectory Reliability Discriminator (TRD) coupled with a joint longitudinal-lateral risk model to support fine-grained, segment-level reliability assessment. Evaluated on the nuPlan dataset, the framework significantly improves calibration fidelity and reliability discrimination accuracy across diverse driving scenarios. It delivers well-calibrated, interpretable uncertainty estimates—critical for downstream risk-aware motion planning.

Technology Category

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

Application Category

• 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
• Security and Privacy: Large-scale security measurements

Abstract

Reliable uncertainty quantification in trajectory prediction is crucial for safety-critical autonomous driving systems, yet existing deep learning predictors lack uncertainty-aware frameworks adaptable to heterogeneous real-world scenarios. To bridge this gap, we propose a novel scenario-aware uncertainty quantification framework to provide the predicted trajectories with prediction intervals and reliability assessment. To begin with, predicted trajectories from the trained predictor and their ground truth are projected onto the map-derived reference routes within the Frenet coordinate system. We then employ CopulaCPTS as the conformal calibration method to generate temporal prediction intervals for distinct scenarios as the uncertainty measure. Building upon this, within the proposed trajectory reliability discriminator (TRD), mean error and calibrated confidence intervals are synergistically analyzed to establish reliability models for different scenarios. Subsequently, the risk-aware discriminator leverages a joint risk model that integrates longitudinal and lateral prediction intervals within the Frenet coordinate to identify critical points. This enables segmentation of trajectories into reliable and unreliable segments, holding the advantage of informing downstream planning modules with actionable reliability results. We evaluated our framework using the real-world nuPlan dataset, demonstrating its effectiveness in scenario-aware uncertainty quantification and reliability assessment across diverse driving contexts.

Problem

• Develops scenario-aware uncertainty quantification for trajectory prediction
• Provides prediction intervals with reliability assessment for autonomous driving
• Segments trajectories into reliable and unreliable parts for planning modules

Innovation

• Scenario-aware uncertainty quantification framework for trajectory prediction
• CopulaCPTS conformal calibration for temporal prediction intervals
• Trajectory reliability discriminator with joint risk model