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This paper introduces K-Risk, a novel dataset designed to enhance the safety of autonomous driving by providing a comprehensive collection of high-risk driving scenarios annotated by large language models. By integrating 20 diverse trajectory datasets from multiple regions and curating 31,398 high-risk events, including a focused subset of near-collision cases, K-Risk offers structured scenario descriptions and actionable insights validated through simulation. The dataset's combination of multi-dimensional risk annotations and interpretable language supervision establishes a standardized framework for developing and assessing risk-aware autonomous driving systems.
K-Risk reveals that a knowledge-augmented dataset can significantly improve the understanding and management of high-risk driving scenarios in autonomous vehicles.
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. Here we present K-Risk, a knowledge-augmented dataset that combines structured driving trajectories with large language model generated semantic annotations for safety-critical driving scenarios. K-Risk integrates 20 human-driven and autonomous-vehicle trajectory datasets from Europe, China, and the United States, covering highways, urban freeways, intersections, and roundabouts. Using a unified risk-centric extraction pipeline, K-Risk curates 31,398 high-risk events, together with a 1,036-event extreme subset of near-collision cases. Each event is released as a synchronized trajectory, metadata, and language triplet containing structured scenario descriptions, abnormal-behavior notifications, and, for a representative subset, causal risk analyses and action recommendations validated through a closed-loop simulator with iterative reflection. By combining multi-dimensional risk annotations, interpretable language supervision, and verifiable decisions, K-Risk bridges structured traffic trajectories, semantic reasoning, and decision supervision, providing a standardized foundation for developing and evaluating next-generation risk-aware autonomous driving agents.
