ConVeX is introduced, an extensive multi-agent synthetic dataset for collaborative perception (CP) that reproduces different realistic driving scenarios (urban, rural, highway), road layouts, and weather and lighting conditions and ground-truth annotations for object detection.
Experimental results show that ROSE achieves competitive detection accuracy while providing improved robustness and cross-modal consistency across adverse weather scenarios, demonstrating strong generalization potential for roadside multi-modal perception under challenging environmental conditions.
Guo-Yu Zhang, Peng Hang, Xin Xia et al.· Communications in Transporta...· 0 citations
This work presents a conceptual framework for Collaborative Joint Perception and Prediction (Co-P&P) that improves motion prediction of surrounding road users, thereby enhancing situational awareness in complex and dynamic traffic environments.
Lei Wan, Hannan Ejaz Keen, Alexey V. Vinel· 0 citations
Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing valuable support for end-to-end autonomous driving. However, existing cooperative driving methods mainly exploit roadside information to enhance the representation of th...
Jun-Wei You, Wei-Zhe Tang, Can Wang et al.· 0 citations
Vehicle-to-Vehicle (V2V) cooperative perception enhances autonomous driving by enabling vehicles to share information beyond their direct line of sight. However, existing V2V datasets are limited by a small number of participating agents, static collaborator selection strategies, and a significant domain gap between si...
Yu-Lu Wu, Chao Wei, Ju-Jun Cheng et al.· 0 citations
Which2comm, a novel multi-agent 3D object detection framework leveraging object-level sparse features, consistently outperformed other state-of-the-art methods on both detection performance and communication cost, exhibiting superior robustness to real-world latency.
Duanrui Yu, Anqi Qu, Jing You et al.· Communications in Transporta...· 0 citations
Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to a new environment zero-shot, because of domain shifts in traffic regulations, road layout and driving behaviors. Therefore, adapting a traj...
Weijiang Xiong, Lang Feng, Alexandre Alahi et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
