Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

Autor:	Belmecheri, Nassim, Gotlieb, Arnaud, Lazaar, Nadjib, Spieker, Helge
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Transport Research Arena (TRA) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.09668 Zobrazit plný text záznamu View this record from Arxiv