Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
This addresses the need for transparency and safety in automated driving systems, particularly for informing passengers and vulnerable road users, though it appears incremental as it builds on existing qualitative and graph-based methods.
The paper tackles the problem of making automated driving more trustworthy by developing the Qualitative Explainable Graph (QXG), a symbolic and qualitative representation for scene understanding, which enables real-time interpretation of sensor data to provide explanations for vehicle decisions.
We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables the interpretation of an automated vehicle's environment using sensor data and machine learning models. It leverages spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an intelligible scene model. Crucially, QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations and real-time decision-making across various sensor types. Our research showcases the transformative potential of QXG, particularly in the context of automated driving, where it elucidates decision rationales by linking the graph with vehicle actions. These explanations serve diverse purposes, from informing passengers and alerting vulnerable road users (VRUs) to enabling post-analysis of prior behaviours.