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.
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.
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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.
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 Machine Learning
Jul 14
Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment
arXiv:2607. 11128v1 Announce Type: cross Abstract: Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur.
arXiv AI
Jun 30
When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
arXiv:2606. 29115v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk condition (MRC) behaviors such as slowing or stopping when uncertainty arises.
arXiv AI
Jul 21
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
arXiv:2511. 14592v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns.
arXiv AI
Jul 28
From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety
arXiv:2510. 03314v2 Announce Type: replace-cross Abstract: Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical challenge, as conventional infrastructure-based measures are often insufficient in dynamic urban environments.
arXiv AI
Jun 2
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.
arXiv AI
2d ago
Risk-Aware Decision-Making for Autonomous Overtaking: A World Model-Based Mixture-of-Experts Framework
The paper introduces WM‑RMoE, a World Model‑based Risk‑aware Mixture‑of‑Experts framework for autonomous overtaking. It uses a learned latent dynamics model to perform multi‑step rollouts, evaluating cumulative risk at the trajectory level, and employs a hierarchical gating mechanism to coordinate long‑, short‑horizon, and rule‑based safety experts. A Gaussian Mixture Model preserves multimodal maneuver branches, improving robustness and preventing behavioral averaging, leading to better safety compliance, decision stability, and generalization in experiments.
arXiv AI
1d ago
CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation
CrashDiffuser is a closed-loop VLM‑guided diffusion framework designed for fine‑grained safety‑critical traffic scenario generation. It separates semantic collision reasoning from trajectory synthesis via a hierarchical collision‑intent interface that specifies target contact regions (head, rear, or side). The system uses a vision‑language model to extract scene context and predict structured action tuples, which condition a diffusion model to produce executable adversarial trajectories, achieving high target‑collision and contact‑region control rates on WOMD‑derived scenarios.
arXiv AI
Jul 3
DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
arXiv:2603. 18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required for safe driving and make unsafe exploration unavoidable in real-world settings.
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.