DPGP：安全な自動運転のための2D-3Dデュアルパス潜在ゴーストプローブゾーン予測フレームワーク
DPGP: A Hybrid 2D-3D Dual Path Potential Ghost Probe Zone Prediction Framework for Safe Autonomous Driving
単眼カメラのみを用いて、歩行者などが突然飛び出す「ゴーストプローブ」ゾーンを予測する2D-3D融合フレームワークを提案し、12K画像のデータセットで有効性を示した。
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著者: Weiming Qu, Jiawei Du, Shenghai Yuan, Jia Wang, Yang Sun, Shengyi Liu, Yuanhao Zhu, Jianfeng Yu, Song Cao, Rui Xia, Xiaoyu Tang, Xihong Wu, Dingsheng Luo
分類: cs.RO
原文アブストラクト
Modern robots must coexist with humans in dense urban environments. A key challenge is the ghost probe problem, where pedestrians or objects unexpectedly rush into traffic paths. This issue affects both autonomous vehicles and human drivers. Existing works propose vehicle-to-everything (V2X) strategies and non-line-of-sight (NLOS) imaging for ghost probe zone detection. However, most require high computational power or specialized hardware, limiting real-world feasibility. Additionally, many methods do not explicitly address this issue. To tackle this, we propose DPGP, a hybrid 2D-3D fusion framework for ghost probe zone prediction using only a monocular camera during training and inference. With unsupervised depth prediction, we observe ghost probe zones align with depth discontinuities, but different depth representations offer varying robustness. To exploit this, we fuse multiple feature embeddings to improve prediction. To validate our approach, we created a 12K-image dataset annotated with ghost probe zones, carefully sourced and cross-checked for accuracy. Experimental results show our framework outperforms existing methods while remaining cost-effective. To our knowledge, this is the first work extending ghost probe zone prediction beyond vehicles, addressing diverse non-vehicle objects. We will open-source our code and dataset for community benefit.
