K-Risk curates 31,398 high-risk driving events from 20 trajectory datasets with multi-layered semantic and LLM-generated annotations validated via closed-loop simulation.
InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving
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abstract
The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present InterHub, a dense interaction dataset derived by mining interaction events from extensive naturalistic driving records. We employ formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions. Additionally, we introduce a user-friendly toolkit enabling the expansion of InterHub with both public and private data. By unifying, categorizing, and analyzing diverse interaction events, InterHub facilitates cross-comparative studies and large-scale research, thereby advancing the evaluation and development of autonomous driving technologies.
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cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
K-Risk curates 31,398 high-risk driving events from 20 trajectory datasets with multi-layered semantic and LLM-generated annotations validated via closed-loop simulation.