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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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