Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

arXiv:2403.09668v1 Announce Type: new
Abstract: 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 …

abstract arxiv automated constraints cs.ai cs.cv data driving environment extract graph graphs interpretation machine machine learning machine learning models mobility representation semantics sensor temporal through trustworthy type understanding urban