79 citations · 133 across the 13 of their papers we have counts for
14 papers
FFVO: A Feedforward Pose Decoder for Long-Horizon Visual Odometry
Meng-Li Shih, Shih-Yang Su, Yuliang Zou +7
Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate camera motion and 3D structure in…
CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception
Rujia Wang, Xiangbo Gao, Hao Xiang +2
Multi-agent collaborative perception enhances each agent perceptual capabilities by sharing sensing information to cooperatively perform robot perception tasks. This approach has p…
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
Wentao Jiang, Hao Xiang, Xinyu Cai +5
Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the p…
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Si Liu, Chen Gao, Yuan Chen +8
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) a…
HM-ViT: Hetero-modal Vehicle-to-Vehicle Cooperative perception with vision transformer
Hao Xiang, Runsheng Xu, Jiaqi Ma
Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to see through occlusions, greatly enhancing perception performance. Nevertheless, existing wo…
Realizing Immersive Communications in Human Digital Twin by Edge Computing Empowered Tactile Internet: Visions and Case Study
Hao Xiang, Changyan Yi, Kun Wu +5
Human digital twin (HDT) is expected to revolutionize the future human lifestyle and prompts the development of advanced human-centric applications (e.g., Metaverse) by bridging ph…
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
Runsheng Xu, Xin Xia, Jinlong Li +10
Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks th…
The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research
Runsheng Xu, Hao Xiang, Xu Han +4
Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traf…
V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception
Hao Xiang, Runsheng Xu, Xin Xia +3
Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With th…
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers
Runsheng Xu, Zhengzhong Tu, Hao Xiang +3
Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for autonomous driving. Although recent literature has made significant progress on BEV map unde…
TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation
David Paz, Hao Xiang, Andrew Liang +1
We present a framework for dynamic trajectory generation for autonomous navigation, which does not rely on HD maps as the underlying representation. High Definition (HD) maps have…
Model-Agnostic Multi-Agent Perception Framework
Runsheng Xu, Weizhe Chen, Hao Xiang +2
Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different p…
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
Runsheng Xu, Hao Xiang, Zhengzhong Tu +3
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust coopera…
OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication
Runsheng Xu, Hao Xiang, Xin Xia +3
Employing Vehicle-to-Vehicle communication to enhance perception performance in self-driving technology has attracted considerable attention recently; however, the absence of a sui…