意味領域予測による道路シーン表現の学習
Learning Road Scene-level Representations via Semantic Region Prediction
自動運転の運転意図予測と危険物体同定のために、意味領域予測タスクと自動ラベリングで道路シーンの表現を学習し、最先端性能を達成した。
著者: Zihao Xiao, Alan Yuille, Yi-Ting Chen
分類: cs.CV, cs.RO
原文アブストラクト
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.
