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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.11715 |
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| _version_ | 1866917080792563712 |
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| author | Shen, Yunfei Wu, Zhongcheng |
| author_facet | Shen, Yunfei Wu, Zhongcheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution Shen, Yunfei Wu, Zhongcheng Computers and Society 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. |
| title | CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2511.11715 |