時間的文脈を活用した車両・インフラ協調知覚の強化
Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception
路側センサと車載センサの協調による3D物体検出において、時間的文脈を活用したクエリ伝送と融合、通信途絶時の復元機構を提案し、限られた通信帯域下でも高精度な知覚を実現した。
著者: Jiaru Zhong, Haibao Yu, Tianyi Zhu, Jiahui Xu, Wenxian Yang, Zaiqing Nie, Chao Sun
分類: cs.RO
原文アブストラクト
Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions. Integrating both infrastructure and ego-vehicle data through V2X communication, known as vehicle-infrastructure cooperation, has shown considerable advantages in enhancing perception capabilities and addressing corner cases encountered in single-vehicle autonomous driving. However, cooperative perception still faces numerous challenges, including limited communication bandwidth and practical communication interruptions. In this paper, we propose CTCE, a novel framework for cooperative 3D object detection. This framework transmits queries with temporal contexts enhancement, effectively balancing transmission efficiency and performance to accommodate real-world communication conditions. Additionally, we propose a temporal-guided fusion module to further improve performance. The roadside temporal enhancement and vehicle-side spatial-temporal fusion together constitute a multi-level temporal contexts integration mechanism, fully leveraging temporal information to enhance performance. Furthermore, a motion-aware reconstruction module is introduced to recover lost roadside queries due to communication interruptions. Experimental results on V2X-Seq and V2X-Sim datasets demonstrate that CTCE outperforms the baseline QUEST, achieving improvements of 3.8% and 1.3% in mAP, respectively. Experiments under communication interruption conditions validate CTCE's robustness to communication interruptions.
