An Effective and Robust Driving Scenario Identification Framework Utilizing Unsupervised Covariance Clustering

The technology of autonomous driving vehicles has made rapid progress over the last decade, but the challenge of proving these system's safety still exists. Compared to conventional mile-based testing, scenario-based testing (SBT) is a more promising solution since scenarios covering diverse and rare driving conditions in real traffic can be simulated to assess the system's performance in safety-critical scenarios. Furthermore, understanding run-time scenarios is vital to trigger safety mechanisms designed for the Safety Of The Intended Functionality (SOTIF). However, a challenging task is to extract or generate scenarios in the design phase and recognize driving scenarios in the run-time phase due to the complexity and diversity of driving scenarios, especially the interaction with other driving agents. In this study, we proposed a complete framework for offline extraction and online identification of all kinds of interaction scenarios. A covariance-clustering based method was adopted to identify the meta-driving actions, which used the Toeplitz matrix to achieve more interpretable clustering results than distance-based methods. Experiments demonstrated the effectiveness of our method by its robust identification results for cut-in and merge-in scenarios. With a lightweight design and a theoretically valid confidence estimation method, our approach is computationally efficient for reliable online applications.