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Understanding complex scenarios from in-vehicle cameras is essential for safely operating autonomous driving systems in densely populated areas. Among these, intersection areas are one of the most critical as they concentrate a considerable number of traffic accidents and fatalities. Detecting and understanding the scene configuration of these usually crowded areas is then of extreme importance for both autonomous vehicles and modern Advanced Driver Assistance Systems (ADAS), aimed at preventing road crashes and increasing the safety of Vulnerable Road Users (VRU). This work investigates how to classify intersection areas from RGB images using well-consolidated neural network approaches along with a method to enhance the results based on the teacher/student training paradigm. An extensive experimental activity aimed at identifying the best input configuration and evaluating different network parameters on both the well-known KITTI dataset and the new KITTI-360 sequences shows that our method outperforms current state-of-the-art intersection classification approaches on per-frame basis, proving the effectiveness of the proposed learning scheme.

Model Guided Road Intersection Classification