High-Risk Test Scenario Generation for Autonomous Vehicles at Roundabouts Using Naturalistic Driving Data

While autonomous vehicles have the potential to mitigate risks associated with dangerous driving behaviors, the safety and stability of autonomous driving technology in real-world applications still require comprehensive tests. Scenario-based virtual simulation testing has emerged as a crucial approach for testing autonomous vehicles, especially in critical scenarios under complex environments, such as merging scenarios at roundabouts with unique traffic features. However, the lack of high-risk scenarios in the real-world traffic domain presents challenges for simulation tests. To address these challenges, this study proposes a scenario generation framework for complex roundabout environments, focusing on merging areas, which is driven by trajectory data and employs generative deep learning techniques to create adversarial hazardous scenarios. Specifically, leveraging real trajectory data from roundabouts, the framework utilizes a time series generative adversarial network (TimeGAN) to generate realistic safety-critical driving trajectories. By creating specific hazardous scenarios, this strategy broadens the library of test scenarios and speeds up the testing process for autonomous vehicles. The significance of the scenarios produced is proven using Simulation of Urban Mobility (SUMO) and CARLA simulation, confirming their necessity in autonomous driving testing. The TimeGAN model effectively captures the spatial–temporal features of merging scenarios, generating high-quality data that enhance the testing scenario library. Findings of this study contribute to solving the problem of scarcity of critical scenarios in virtual testing and accelerate the testing procedure for self-driving automobiles.