158 citations · 616 across the 31 of their papers we have counts for
31 papers
V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Hao Xiang, Zhaoliang Zheng, Xin Xia +15
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the percep…
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
Jinlong Li, Baolu Li, Xinyu Liu +3
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for fea…
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
Xianghao Kong, Wentao Jiang, Jinrang Jia +3
Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated…
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…
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
Jinlong Li, Runsheng Xu, Xinyu Liu +5
Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testin…
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality
Jinlong Li, Runsheng Xu, Xinyu Liu +4
Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for…
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…
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
Rui Song, Runsheng Xu, Andreas Festag +2
Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly pr…
Collaboration Helps Camera Overtake LiDAR in 3D Detection
Yue Hu, Yifan Lu, Runsheng Xu +3
Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major…
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…
Street-View Image Generation from a Bird's-Eye View Layout
Alexander Swerdlow, Runsheng Xu, Bolei Zhou
Bird's-Eye View (BEV) Perception has received increasing attention in recent years as it provides a concise and unified spatial representation across views and benefits a diverse s…
Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication
Jinlong Li, Runsheng Xu, Xinyu Liu +4
Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the d…
Automated Driving Systems Data Acquisition and Processing Platform
Xin Xia, Zonglin Meng, Xu Han +5
This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected…
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Xinyu Cai, Wentao Jiang, Runsheng Xu +4
Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to…
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
Jinlong Li, Runsheng Xu, Jin Ma +3
Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers…
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…
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song +6
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation,…
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…
Pik-Fix: Restoring and Colorizing Old Photos
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +6
Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer…
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…
ROMNet: Renovate the Old Memories
Runsheng Xu, Zhengzhong Tu, Yuanqi Du +5
Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cr…