7 papers
V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Zewei Zhou, Hao Xiang, Zhaoliang Zheng +11
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems. Prior work primarily focuses…
CDA-SimBoost: A Unified Framework Bridging Real Data and Simulation for Infrastructure-Based CDA Systems
Zhaoliang Zheng, Xu Han, Yuxin Bao +5
Cooperative Driving Automation (CDA) has garnered increasing research attention, yet the role of intelligent infrastructure remains insufficiently explored. Existing solutions offe…
CooPre: Cooperative Pretraining for V2X Cooperative Perception
Seth Z. Zhao, Hao Xiang, Chenfeng Xu +3
Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and ann…
InSPE: Rapid Evaluation of Heterogeneous Multi-Modal Infrastructure Sensor Placement
Zhaoliang Zheng, Yun Zhang, Zongling Meng +3
Infrastructure sensing is vital for traffic monitoring at safety hotspots (e.g., intersections) and serves as the backbone of cooperative perception in autonomous driving. While ve…
V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Reality
Hao Xiang, Zhaoliang Zheng, Xin Xia +6
Cooperative perception enabled by Vehicle-to-Everything (V2X) communication holds significant promise for enhancing the perception capabilities of autonomous vehicles, allowing the…
Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models
Xu Han, Zhiwen Wu, Xin Xia +1
This paper introduces and tests a framework integrating traffic regulation compliance into automated driving systems (ADS). The framework enables ADS to follow traffic laws and mak…