SinD 2.0: A Multi-City UAV Dataset with Semantic Risk Annotations for SOTIF-Oriented Safety Validation at Signalized Intersections

2026-07-18

AI Summary

This work addresses the safety validation challenges faced by autonomous driving systems at signalized intersections—particularly due to high traffic heterogeneity, sparsity of safety-critical events, lack of semantic risk annotations, and geographic homogeneity—by introducing the first large-scale, multi-city intersection dataset captured via drones across six intersections in four Chinese cities. The dataset includes 32,682 densely sampled edge-case scenarios and features a hierarchical semantic risk annotation framework encompassing traffic violations, visual occlusions, and narrow drivable areas. Integrated with SPaT (Signal Phase and Timing) data and high-definition maps, it supports both open- and closed-loop simulation testing. Experimental results reveal significant cross-domain distribution shifts within the dataset, and its semantically annotated risk subsets effectively expose algorithmic vulnerabilities, thereby establishing a high-quality benchmark for SOTIF (Safety of the Intended Functionality) validation.

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.

Problem

• SOTIF
• signalized intersections
• autonomous driving safety validation
• semantic risk annotations
• naturalistic driving dataset

Innovation

• SOTIF
• UAV dataset
• semantic risk annotation
• cross-domain safety validation
• signalized intersections