Zobrazeno 1 - 10
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pro vyhledávání: '"Zhao,Dawei"'
Autor:
Min, Chen, Si, Shubin, Wang, Xu, Xue, Hanzhang, Jiang, Weizhong, Liu, Yang, Wang, Juan, Zhu, Qingtian, Zhu, Qi, Luo, Lun, Kong, Fanjie, Miao, Jinyu, Cai, Xudong, An, Shuai, Li, Wei, Mei, Jilin, Sun, Tong, Zhai, Heng, Liu, Qifeng, Zhao, Fangzhou, Chen, Liang, Wang, Shuai, Shang, Erke, Shang, Linzhi, Zhao, Kunlong, Li, Fuyang, Fu, Hao, Jin, Lei, Zhao, Jian, Mao, Fangyuan, Xiao, Zhipeng, Li, Chengyang, Dai, Bin, Zhao, Dawei, Xiao, Liang, Nie, Yiming, Hu, Yu, Li, Xuelong
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-p
Externí odkaz:
http://arxiv.org/abs/2410.07701
Autor:
Min, Chen, Zhao, Dawei, Xiao, Liang, Zhao, Jian, Xu, Xinli, Zhu, Zheng, Jin, Lei, Li, Jianshu, Guo, Yulan, Xing, Junliang, Jing, Liping, Nie, Yiming, Dai, Bin
Vision-centric autonomous driving has recently raised wide attention due to its lower cost. Pre-training is essential for extracting a universal representation. However, current vision-centric pre-training typically relies on either 2D or 3D pre-text
Externí odkaz:
http://arxiv.org/abs/2405.04390
Autor:
Zhu, Zheng, Wang, Xiaofeng, Zhao, Wangbo, Min, Chen, Deng, Nianchen, Dou, Min, Wang, Yuqi, Shi, Botian, Wang, Kai, Zhang, Chi, You, Yang, Zhang, Zhaoxiang, Zhao, Dawei, Xiao, Liang, Zhao, Jian, Lu, Jiwen, Huang, Guan
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems. Recently, the emergence of the
Externí odkaz:
http://arxiv.org/abs/2405.03520
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he introduced the concept of the occupancy grid as World Models for robots. We imbue the robot with a spatial-temporal world model, termed UniWorld, to perceive its
Externí odkaz:
http://arxiv.org/abs/2308.07234
Publikováno v:
Journal of Field Robotics, 2023, 1-25
For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could output stable, complete and accurate terrain models and traversability
Externí odkaz:
http://arxiv.org/abs/2307.02060
Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. The existing multi-camera algorithms primarily rely on monocular 2D pre-training. H
Externí odkaz:
http://arxiv.org/abs/2305.18829
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve the accuracy and robustness. H
Externí odkaz:
http://arxiv.org/abs/2211.06788
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning-based free space detection methods have been proved feasible. However, these efforts
Externí odkaz:
http://arxiv.org/abs/2206.09907
Current perception models in autonomous driving heavily rely on large-scale labelled 3D data, which is both costly and time-consuming to annotate. This work proposes a solution to reduce the dependence on labelled 3D training data by leveraging pre-t
Externí odkaz:
http://arxiv.org/abs/2206.09900