Dynamic-Scene-Graph-Supported Visual Understanding of Autonomous Driving Scenarios

Understanding driving scenarios is a critical issue in autonomous driving, as it allows developers to create more effective and reliable autonomous driving systems. However, the complexity and dynamics of driving scenarios present a significant challenge. In this paper, we utilize scene graphs to encode the semantics of driving scenarios and propose a spatio-temporal visual analytics system to support scenario comprehension. The system provides a multi-level visualization of autonomous driving scenarios, including single-frame semantics, time-varying visual summaries, and scenario distribution. With a subgraph matching algorithm, the system supports users in interactively mining and comparing driving scenarios of interest. Users can explore, analyze, compare, summarize, and draw conclusions about various scenarios through our system. We demonstrate the effectiveness of the system through case studies. Our system can help users identify meaningful scenarios and corner cases, thus providing support for autonomous driving simulation and data augmentation.