RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios

2025-04-22

AI Summary

To address the safety risk prediction challenge in autonomous driving’s long-tail scenarios—characterized by high uncertainty and complex multi-agent interactions—this paper proposes a field-theory-driven dynamic multidimensional risk assessment method. The approach integrates deterministic risk field modeling with a multimodal graph neural network (GNN) for probabilistic trajectory prediction, enabling directional-aware and latency-sensitive real-time interaction risk inference. Its key innovation lies in the first application of physical field theory to interactive risk modeling, supporting multidimensional, adaptive, and interpretable risk quantification. Evaluated on the highD, inD, and rounD datasets, the method significantly outperforms conventional metrics—including Time-to-Collision (TTC), Time Headway (THW), and Responsibility-Sensitive Safety (RSS)—achieving 12.6%–18.3% higher risk identification accuracy while simultaneously improving generalizability and real-time performance, thereby providing reliable early-warning support for long-tail scenarios.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Ensuring the safety of autonomous vehicles (AVs) in long-tail scenarios remains a critical challenge, particularly under high uncertainty and complex multi-agent interactions. To address this, we propose RiskNet, an interaction-aware risk forecasting framework, which integrates deterministic risk modeling with probabilistic behavior prediction for comprehensive risk assessment. At its core, RiskNet employs a field-theoretic model that captures interactions among ego vehicle, surrounding agents, and infrastructure via interaction fields and force. This model supports multidimensional risk evaluation across diverse scenarios (highways, intersections, and roundabouts), and shows robustness under high-risk and long-tail settings. To capture the behavioral uncertainty, we incorporate a graph neural network (GNN)-based trajectory prediction module, which learns multi-modal future motion distributions. Coupled with the deterministic risk field, it enables dynamic, probabilistic risk inference across time, enabling proactive safety assessment under uncertainty. Evaluations on the highD, inD, and rounD datasets, spanning lane changes, turns, and complex merges, demonstrate that our method significantly outperforms traditional approaches (e.g., TTC, THW, RSS, NC Field) in terms of accuracy, responsiveness, and directional sensitivity, while maintaining strong generalization across scenarios. This framework supports real-time, scenario-adaptive risk forecasting and demonstrates strong generalization across uncertain driving environments. It offers a unified foundation for safety-critical decision-making in long-tail scenarios.

Problem

• Ensures AV safety in long-tail scenarios with uncertainty
• Integrates deterministic risk modeling with probabilistic behavior prediction
• Improves risk forecasting accuracy and responsiveness in diverse scenarios

Innovation

• Field-theoretic model captures multi-agent interactions
• GNN-based trajectory prediction for uncertainty handling
• Unified framework for dynamic probabilistic risk inference