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

arXiv: https://arxiv.org/abs/2504.15541 · HTML: https://mlsanity.com/paper/2504.15541/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving](https://mlsanity.com/paper/1909.07707/) (2019)
- [Dynamically Conservative Self-Driving Planner for Long-Tail Cases](https://mlsanity.com/paper/2305.07497/) (2023)
- [Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Prediction](https://mlsanity.com/paper/2312.15881/) (2023)
- [Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving](https://mlsanity.com/paper/2302.07109/) (2023)

## Built on by (most cited first, 4 of 4)

- [SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling](https://mlsanity.com/paper/2506.21041/) (2025)
- [Risk-Controllable Multi-View Diffusion for Driving Scenario Generation](https://mlsanity.com/paper/2603.11534/) (2026)
- [Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles](https://mlsanity.com/paper/2604.07378/) (2026)
- [Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment](https://mlsanity.com/paper/2607.11128/) (2026)

## Abstract (arXiv)

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.