{"ID":2842950,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11715","arxiv_id":"2511.11715","title":"CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution","abstract":"As autonomous driving technology advances, the critical challenge evolves beyond collision avoidance to the \\textbf{adjudication of liability} when accidents occur. Existing datasets, focused on detection and localization, lack the annotations required for this legal reasoning. To bridge this gap, we introduce the \\textbf{C}hinese \\textbf{A}ccident \\textbf{D}uty-determination \\textbf{D}ataset (\\textbf{CADD}), the first benchmark for statute-based liability attribution. CADD contains 792 real-world driving recorder videos, each annotated within a novel \\textbf{``Behavior--Liability--Statute''} pipeline. This framework provides \\textbf{granular, symmetric behavior annotations}, clear responsibility assignments, and, uniquely, links each case to the specific \\textbf{Chinese traffic law statute} violated. We demonstrate the utility of CADD through detailed analysis and establish benchmarks for liability prediction and explainable decision-making. By directly connecting perceptual data to legal consequences, CADD provides a foundational resource for developing accountable and legally-grounded autonomous systems.","short_abstract":"As autonomous driving technology advances, the critical challenge evolves beyond collision avoidance to the \\textbf{adjudication of liability} when accidents occur. Existing datasets, focused on detection and localization, lack the annotations required for this legal reasoning. To bridge this gap, we introduce the \\tex...","url_abs":"https://arxiv.org/abs/2511.11715","url_pdf":"https://arxiv.org/pdf/2511.11715v1","authors":"[\"Yunfei Shen\",\"Zhongcheng Wu\"]","published":"2025-11-12T18:24:29Z","proceeding":"cs.CY","tasks":"[\"cs.CY\"]","methods":"[]","has_code":false}