SinD 2.0: A Multi-City UAV Dataset with Semantic Risk Annotations for SOTIF-Oriented Safety Validation at Signalized Intersections
Robotics
Summary
The authors created SinD 2.0, a big dataset of drone videos from six busy intersections in four Chinese cities to help test how safe self-driving cars are at traffic lights. They focused on capturing lots of risky events and added detailed labels showing different kinds of traffic problems and tricky situations. Their dataset also comes with tools that let researchers run various tests, including realistic simulations. They found that driving behaviors vary a lot between cities and these detailed labels help find weaknesses in self-driving car algorithms.
Authors
Yunwei Li, Shengjie Fu, Chunrong Chen, Chengxiang Zhao, Yuchen Fan, Mingyu Zhu, Yanchao Xu, Yuxin Zhang, Lan Yang, Chuzhao Li, Jie Ji, Yi He, Abhijit Sarkar, Akash Sonth, Hong Wang, Jun Li
Abstract
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.