发表机构
Tsinghua University; Beijing Institute of Technology; Guangzhou Automobile Group Co., Ltd.; Jilin University; Chang’an University; Chongqing University; Southwest University; Wuhan University of Technology; Virginia Tech Transportation Institute; Virginia Tech（清华大学; 北京理工大学; 广州汽车集团股份有限公司; 吉林大学; 长安大学; 重庆大学; 西南大学; 武汉理工大学; 弗吉尼亚理工大学交通研究所; 弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶系统在信号交叉口安全验证的瓶颈，介绍SinD 2.0多城市无人机数据集。通过跨域多样、高密度风险交互、分层语义注释及全栈测试工具链，能有效暴露算法性能局限，为ADS安全分析提供有力支持。
AI 中文摘要
信号交叉口的安全验证仍然是自动驾驶系统（ADS）部署的关键瓶颈，因为这些场景涉及密集的异质交通、有争议的路权和长尾安全关键交互，对预期功能安全（SOTIF）构成重大挑战。现有自然驾驶数据集存在地理同质性、安全关键事件稀疏和缺乏语义风险注释等问题，限制了算法泛化性评估和针对性SOTIF验证。本文介绍了SinD 2.0，一个用于跨域ADS安全分析的大规模基于无人机的交叉口数据集。其主要贡献包括跨域多样性，涵盖中国四个城市的六个信号交叉口；高密度风险交互，通过替代安全措施提取32682个安全关键事件；分层语义注释，提供包括交通违规等多维语义标签；全栈测试工具链，支持多种测试方式。基准实验表明SinD 2.0在不同城市间存在显著域转移，语义风险子集能有效暴露ADS算法性能局限。数据集、注释和测试工具链可通过链接获取。
英文摘要
Safety validation at signalized intersections remains a critical bottleneck for the deployment of autonomous driving systems (ADS), as these scenarios involve dense heterogeneous traffic, contested right of way, and long-tail safety-critical interactions, posing significant challenges to the Safety of the Intended Functionality (SOTIF). Existing naturalistic driving datasets often suffer from geographical homogeneity, sparsity of safety-critical events, and lack of semantic risk annotations, which limit the evaluation of algorithmic generalizability and targeted SOTIF verification. To address these gaps, this paper introduces SinD 2.0, a large-scale drone-based intersection dataset dedicated to cross-domain ADS safety analysis. The main contributions of SinD 2.0 are: (1) Cross-domain diversity: It covers six signalized intersections across four Chinese cities, capturing distinct intersection topologies and regional driving behavior characteristics; (2) High-density risk interactions: A total of 32,682 safety-critical events are extracted via surrogate safety measures, significantly enriching the density of boundary test scenarios; (3) Hierarchical semantic annotations: Besides integration with high-definition (HD) maps and Signal Phase and Timing (SPaT) data, it provides multi-dimensional semantic labels including traffic violations, high-risk interactions, visual shielding, and narrow feasible areas; (4) Full-stack testing toolchain: It supports automated scenario extraction, prediction-only evaluation, open-loop replay, reactive closed-loop testing, and photorealistic rendering. Benchmark experiments demonstrate that SinD 2.0 exhibits significant domain shifts across cities, and the semantic risk subsets can effectively expose the performance limitations of ADS algorithms. The dataset, annotations, and testing toolchain are available at https://github.com/SOTIF-AVLab/SinD/tree/main.
