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.

本研究旨在进行自动驾驶系统中的关键任务，即驾驶员意图预测和从egocentric图像中识别风险对象，并探讨两项任务的道路场景级别表示方法，通过语义区域表示方法和自动语义区域标注算法进行场景级别表示的学习和预测，取得了驾驶员意图预测和风险对象识别方面的最佳性能表现。

通过语义区域预测学习道路场景级别表示