Modular Framework for Uncertainty Prediction in Autonomous Vehicle Motion Forecasting within Complex Traffic Scenarios

2025-01-27

AI Summary

To address insufficient uncertainty modeling and unreliable validation in autonomous vehicle trajectory prediction under complex traffic scenarios, this paper proposes a modular uncertainty prediction framework. Methodologically: (1) we design an end-to-end differentiable probabilistic heatmap predictor for context-aware occupancy grid modeling; (2) we introduce a decoupled encoder-decoder architecture enabling independent module training and flexible composition; and (3) we establish a high-risk robustness verification system grounded in principled uncertainty metrics. Compared to end-to-end baselines, our framework achieves faster training convergence and enhanced stability. It supports zero-shot cross-scenario transfer without full-model retraining and significantly improves both trajectory prediction reliability and uncertainty quantification accuracy—particularly in challenging interactive driving scenarios.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

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
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

We propose a modular modeling framework designed to enhance the capture and validation of uncertainty in autonomous vehicle (AV) trajectory prediction. Departing from traditional deterministic methods, our approach employs a flexible, end-to-end differentiable probabilistic encoder-decoder architecture. This modular design allows the encoder and decoder to be trained independently, enabling seamless adaptation to diverse traffic scenarios without retraining the entire system. Our key contributions include: (1) a probabilistic heatmap predictor that generates context-aware occupancy grids for dynamic forecasting, (2) a modular training approach that supports independent component training and flexible adaptation, and (3) a structured validation scheme leveraging uncertainty metrics to evaluate robustness under high-risk conditions. To highlight the benefits of our framework, we benchmark it against an end-to-end baseline, demonstrating faster convergence, improved stability, and flexibility. Experimental results validate these advantages, showcasing the capacity of the framework to efficiently handle complex scenarios while ensuring reliable predictions and robust uncertainty representation. This modular design offers significant practical utility and scalability for real-world autonomous driving applications.

Problem

• Autonomous Vehicles
• Action Prediction
• Uncertainty in Complex Traffic

Innovation

• Heatmap Prediction
• Modular Design
• Stability Assessment