6 papers
Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving
Nuoran Li, Zhang Zhang, Yueran Zhao +2
Vehicle-to-everything-aided autonomous driving (V2X-AD) significantly enhances driving performance through information sharing. However, existing collaborative perception methods o…
HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction
Yueran Zhao, Zhang Zhang, Chao Sun +3
Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission. However, as more agents participate, existing frameworks fac…
RoadMamba: A Dual Branch Visual State Space Model for Road Surface Classification
Tianze Wang, Zhang Zhang, Chao Yue +2
Acquiring the road surface conditions in advance based on visual technologies provides effective information for the planning and control system of autonomous vehicles, thus improv…
RoadFormer : Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving
Tianze Wang, Zhang Zhang, Chao Sun
The classification of the type of road surface (RSC) aims to utilize pavement features to identify the roughness, wet and dry conditions, and material information of the road surfa…
PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model
Zhang Zhang, Chao Sun, Chao Yue +3
Serving the Intelligent Transport System (ITS) and Vehicle-to-Everything (V2X) tasks, roadside perception has received increasing attention in recent years, as it can extend the pe…
HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer
Zhang Zhang, Chao Sun, Chao Yue +4
Roadside vision centric 3D object detection has received increasing attention in recent years. It expands the perception range of autonomous vehicles, enhances the road safety. Pre…