arXiv:2609.18125v1 Announce Type: new
Abstract: Humans often observe others before interacting and adjust their behavior accordingly. Robot navigation in crowds, however, often represents pedestrians...
By Bo-Han Chen, Hiromu Taketsugu, Norimichi Ukita
arXiv:2607. 07357v1 Announce Type: cross Abstract: Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation.
By Daeun Song, Nhat Le, Jeffrey Chen, Mohammad Nazeri, Amirreza Payandeh, Rohan Chandra, Reuth Mirsky, Ross Mead, Ling Xiao, Xuesu Xiao
arXiv:2609.37476v1 Announce Type: cross
Abstract: Training robust social-navigation policies requires simulators with diverse scene layouts, terrain, and human motion, but constructing such environme...
By Jiaming Wang, Duc Thang Nguyen, Jizhuo Chen, Volodymyr Shcherbyna, Diwen Liu, Zhengcheng Shen, Harold Soh
arXiv:2607. 07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents.
By Yi Yang, Siyuan Liu, Xin Gao, Huamu Sun, Chao Liu, Qing Zhou, Bingbing Nie
arXiv:2609.36852v1 Announce Type: new
Abstract: Trajectory prediction is a key component for understanding human behavior patterns in dynamic scenes. Researchers have devoted substantial efforts to m...
By Ziqian Zou, Conghao Wong, Qinmu Peng, Xinge You
TrajFusionNet+ is a transformer-based model that predicts pedestrian crossing intention by fusing sequential trajectory data, visual trajectory overlays, and graph-based scene context. It extends the earlier TrajFusionNet with three attention modules—Sequence, Visual, and Graph—to capture temporal, visual, and relational cues. The model outperforms state‑of‑the‑art methods on the PIE and JAAD datasets and shows better generalization under a joint‑training, separate‑evaluation protocol.
By Fran\c{c}ois G. Landry, Moulay A. Akhloufi