arXiv AI
Jul 29
Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
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arXiv Computer Vision
Sep 10
Data-Driven Risk Fields for Safer End-to-End Autonomous Driving
arXiv:2609.10377v1 Announce Type: cross Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities...
arXiv Machine Learning
Jul 9
A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
arXiv:2607. 07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety.
arXiv AI
Sep 17
Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
RiskWorld is a risk‑aware world modeling framework that forecasts shared occupancy and selectively replaces planned trajectories in automated driving. It fuses spatial risk fields, temporal actor context, and visual bird’s‑eye‑view features, using flow‑guided evolution to transport occupancy and signed residuals to correct it. In open‑loop planning on nuScenes, RiskWorld achieves the lowest collision rate over a 3‑second horizon and the second‑best average L2 error, running at 11.5 FPS on a single NVIDIA RTX 4090.
Hugging Face Trending Papers
Jul 8
A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety. These scenarios are severely under-represented in naturalistic driving data, and existing trajectory and language-augmented datasets seldom provide high-risk event labels, semantic annotations, and verifiable safety signals.
arXiv AI
Sep 21
Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving
arXiv:2609.21486v1 Announce Type: new Abstract: Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene rep...
arXiv AI
Jun 6
RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation
arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.
arXiv AI
Jun 30
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
arXiv:2606. 28716v1 Announce Type: new Abstract: The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems.
arXiv AI
Aug 6
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
arXiv:2608. 04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems.
arXiv Machine Learning
Jul 20
PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.
arXiv Machine Learning
Sep 3
PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems
PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.
arXiv AI
4d ago
ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving
arXiv:2609.37871v1 Announce Type: cross Abstract: Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDr...