  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 ADAS aimed at
preventing road crashes and increasing the safety of vulnerable road users.
This work investigates inter-section classification from RGB images using
well-consolidate 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 approaches on a per-frame basis and prove the effectiveness of
the proposed learning scheme.
