CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution

TL;DR

CADD is a novel Chinese traffic accident dataset that links real-world driving videos with legal liability annotations based on traffic statutes, facilitating research on legally-grounded autonomous driving decision-making.

Contribution

This paper introduces CADD, the first dataset linking accident videos with liability annotations and legal statutes, enabling legal reasoning in autonomous driving systems.

Findings

Established benchmarks for liability prediction

Demonstrated utility in legal reasoning tasks

Provided detailed analysis of 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.