Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
This addresses the need for transparent decision-making in automated vehicles, though it appears incremental as it builds on existing graph-based and qualitative methods.
The paper tackles the problem of trustworthy automated driving by introducing the Qualitative Explainable Graph (QXG), a unified symbolic and qualitative representation for scene understanding, which enables interpretable explanations of vehicle decisions in real-time.
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility. The QXG enables interpreting an automated vehicle's environment using sensor data and machine learning models. It utilizes spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an interpretable scene model. A QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations across various sensor types. Our research showcases the potential of QXG, particularly in the context of automated driving, where it can rationalize decisions by linking the graph with observed actions. These explanations can serve diverse purposes, from informing passengers and alerting vulnerable road users to enabling post-hoc analysis of prior behaviors.