  In this work, we tackle two vital tasks in automated driving systems, i.e.,
driver intent prediction and risk object identification from egocentric images.
Mainly, we investigate the question: what would be good road scene-level
representations for these two tasks? We contend that a scene-level
representation must capture higher-level semantic and geometric representations
of traffic scenes around ego-vehicle while performing actions to their
destinations. To this end, we introduce the representation of semantic regions,
which are areas where ego-vehicles visit while taking an afforded action (e.g.,
left-turn at 4-way intersections). We propose to learn scene-level
representations via a novel semantic region prediction task and an automatic
semantic region labeling algorithm. Extensive evaluations are conducted on the
HDD and nuScenes datasets, and the learned representations lead to
state-of-the-art performance for driver intention prediction and risk object
identification.
