Zobrazeno 1 - 10
of 331
pro vyhledávání: '"Xu, Runsheng"'
Autor:
Li, Jinlong, Li, Baolu, Tu, Zhengzhong, Liu, Xinyu, Guo, Qing, Juefei-Xu, Felix, Xu, Runsheng, Yu, Hongkai
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems. However, these systems often struggle in low-light condi
Externí odkaz:
http://arxiv.org/abs/2404.04804
Autor:
Xiang, Hao, Zheng, Zhaoliang, Xia, Xin, Xu, Runsheng, Gao, Letian, Zhou, Zewei, Han, Xu, Ji, Xinkai, Li, Mingxi, Meng, Zonglin, Jin, Li, Lei, Mingyue, Ma, Zhaoyang, He, Zihang, Ma, Haoxuan, Yuan, Yunshuang, Zhao, Yingqian, Ma, Jiaqi
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilita
Externí odkaz:
http://arxiv.org/abs/2403.16034
Autor:
Li, Baolu, Li, Jinlong, Liu, Xinyu, Xu, Runsheng, Tu, Zhengzhong, Guo, Jiacheng, Li, Xiaopeng, Yu, Hongkai
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions
Externí odkaz:
http://arxiv.org/abs/2403.11371
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 feature extraction. However, the data source to train the various agents
Externí odkaz:
http://arxiv.org/abs/2402.04273
Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated real-world data, which can always be expensive and hard to acquire. Si
Externí odkaz:
http://arxiv.org/abs/2310.08117
Autor:
Jiang, Wentao, Xiang, Hao, Cai, Xinyu, Xu, Runsheng, Ma, Jiaqi, Li, Yikang, Lee, Gim Hee, Liu, Si
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 placement of roadside LiDARs is a crucial but often overlooked problem.
Externí odkaz:
http://arxiv.org/abs/2310.07247
Autor:
Liu, Si, Gao, Chen, Chen, Yuan, Peng, Xingyu, Kong, Xianghao, Wang, Kun, Xu, Runsheng, Jiang, Wentao, Xiang, Hao, Ma, Jiaqi, Wang, Miao
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limi
Externí odkaz:
http://arxiv.org/abs/2308.16714
Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, this such assumption may fail in different weather conditions.
Externí odkaz:
http://arxiv.org/abs/2307.09676
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 training and validating. However, the perception performance is degrad
Externí odkaz:
http://arxiv.org/abs/2307.07935
Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to see through occlusions, greatly enhancing perception performance. Nevertheless, existing works all focused on homogeneous traffic where vehicles are equipped wit
Externí odkaz:
http://arxiv.org/abs/2304.10628