Copertina

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kong, Lingdong, Xie, Shaoyuan, Hu, Hanjiang, Niu, Yaru, Ooi, Wei Tsang, Cottereau, Benoit R., Ng, Lai Xing, Ma, Yuexin, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei, Qiu, Weichao, Zhang, Wei, Cao, Xu, Lu, Hao, Chen, Ying-Cong, Kang, Caixin, Zhou, Xinning, Ying, Chengyang, Shang, Wentao, Wei, Xingxing, Dong, Yinpeng, Yang, Bo, Jiang, Shengyin, Ma, Zeliang, Ji, Dengyi, Li, Haiwen, Huang, Xingliang, Tian, Yu, Kou, Genghua, Jia, Fan, Liu, Yingfei, Wang, Tiancai, Li, Ying, Hao, Xiaoshuai, Yang, Yifan, Zhang, Hui, Wei, Mengchuan, Zhou, Yi, Zhao, Haimei, Zhang, Jing, Li, Jinke, He, Xiao, Cheng, Xiaoqiang, Zhang, Bingyang, Zhao, Lirong, Ding, Dianlei, Liu, Fangsheng, Yan, Yixiang, Wang, Hongming, Ye, Nanfei, Luo, Lun, Tian, Yubo, Zuo, Yiwei, Cao, Zhe, Ren, Yi, Li, Yunfan, Liu, Wenjie, Wu, Xun, Mao, Yifan, Li, Ming, Liu, Jian, Liu, Jiayang, Qin, Zihan, Chu, Cunxi, Xu, Jialei, Zhao, Wenbo, Jiang, Junjun, Liu, Xianming, Wang, Ziyan, Li, Chiwei, Li, Shilong, Yuan, Chendong, Yang, Songyue, Liu, Wentao, Chen, Peng, Zhou, Bin, Wang, Yubo, Zhang, Chi, Sun, Jianhang, Chen, Hai, Yang, Xiao, Wang, Lizhong, Fu, Dongyi, Lin, Yongchun, Yang, Huitong, Li, Haoang, Luo, Yadan, Cheng, Xianjing, Xu, Yong
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Robotics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

_version_	1866916265627484160
author	Kong, Lingdong Xie, Shaoyuan Hu, Hanjiang Niu, Yaru Ooi, Wei Tsang Cottereau, Benoit R. Ng, Lai Xing Ma, Yuexin Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei Qiu, Weichao Zhang, Wei Cao, Xu Lu, Hao Chen, Ying-Cong Kang, Caixin Zhou, Xinning Ying, Chengyang Shang, Wentao Wei, Xingxing Dong, Yinpeng Yang, Bo Jiang, Shengyin Ma, Zeliang Ji, Dengyi Li, Haiwen Huang, Xingliang Tian, Yu Kou, Genghua Jia, Fan Liu, Yingfei Wang, Tiancai Li, Ying Hao, Xiaoshuai Yang, Yifan Zhang, Hui Wei, Mengchuan Zhou, Yi Zhao, Haimei Zhang, Jing Li, Jinke He, Xiao Cheng, Xiaoqiang Zhang, Bingyang Zhao, Lirong Ding, Dianlei Liu, Fangsheng Yan, Yixiang Wang, Hongming Ye, Nanfei Luo, Lun Tian, Yubo Zuo, Yiwei Cao, Zhe Ren, Yi Li, Yunfan Liu, Wenjie Wu, Xun Mao, Yifan Li, Ming Liu, Jian Liu, Jiayang Qin, Zihan Chu, Cunxi Xu, Jialei Zhao, Wenbo Jiang, Junjun Liu, Xianming Wang, Ziyan Li, Chiwei Li, Shilong Yuan, Chendong Yang, Songyue Liu, Wentao Chen, Peng Zhou, Bin Wang, Yubo Zhang, Chi Sun, Jianhang Chen, Hai Yang, Xiao Wang, Lizhong Fu, Dongyi Lin, Yongchun Yang, Huitong Li, Haoang Luo, Yadan Cheng, Xianjing Xu, Yong
author_facet	Kong, Lingdong Xie, Shaoyuan Hu, Hanjiang Niu, Yaru Ooi, Wei Tsang Cottereau, Benoit R. Ng, Lai Xing Ma, Yuexin Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei Qiu, Weichao Zhang, Wei Cao, Xu Lu, Hao Chen, Ying-Cong Kang, Caixin Zhou, Xinning Ying, Chengyang Shang, Wentao Wei, Xingxing Dong, Yinpeng Yang, Bo Jiang, Shengyin Ma, Zeliang Ji, Dengyi Li, Haiwen Huang, Xingliang Tian, Yu Kou, Genghua Jia, Fan Liu, Yingfei Wang, Tiancai Li, Ying Hao, Xiaoshuai Yang, Yifan Zhang, Hui Wei, Mengchuan Zhou, Yi Zhao, Haimei Zhang, Jing Li, Jinke He, Xiao Cheng, Xiaoqiang Zhang, Bingyang Zhao, Lirong Ding, Dianlei Liu, Fangsheng Yan, Yixiang Wang, Hongming Ye, Nanfei Luo, Lun Tian, Yubo Zuo, Yiwei Cao, Zhe Ren, Yi Li, Yunfan Liu, Wenjie Wu, Xun Mao, Yifan Li, Ming Liu, Jian Liu, Jiayang Qin, Zihan Chu, Cunxi Xu, Jialei Zhao, Wenbo Jiang, Junjun Liu, Xianming Wang, Ziyan Li, Chiwei Li, Shilong Yuan, Chendong Yang, Songyue Liu, Wentao Chen, Peng Zhou, Bin Wang, Yubo Zhang, Chi Sun, Jianhang Chen, Hai Yang, Xiao Wang, Lizhong Fu, Dongyi Lin, Yongchun Yang, Huitong Li, Haoang Luo, Yadan Cheng, Xianjing Xu, Yong
contents	In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.
format	Preprint
id	arxiv_https___arxiv_org_abs_2405_08816
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition Kong, Lingdong Xie, Shaoyuan Hu, Hanjiang Niu, Yaru Ooi, Wei Tsang Cottereau, Benoit R. Ng, Lai Xing Ma, Yuexin Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei Qiu, Weichao Zhang, Wei Cao, Xu Lu, Hao Chen, Ying-Cong Kang, Caixin Zhou, Xinning Ying, Chengyang Shang, Wentao Wei, Xingxing Dong, Yinpeng Yang, Bo Jiang, Shengyin Ma, Zeliang Ji, Dengyi Li, Haiwen Huang, Xingliang Tian, Yu Kou, Genghua Jia, Fan Liu, Yingfei Wang, Tiancai Li, Ying Hao, Xiaoshuai Yang, Yifan Zhang, Hui Wei, Mengchuan Zhou, Yi Zhao, Haimei Zhang, Jing Li, Jinke He, Xiao Cheng, Xiaoqiang Zhang, Bingyang Zhao, Lirong Ding, Dianlei Liu, Fangsheng Yan, Yixiang Wang, Hongming Ye, Nanfei Luo, Lun Tian, Yubo Zuo, Yiwei Cao, Zhe Ren, Yi Li, Yunfan Liu, Wenjie Wu, Xun Mao, Yifan Li, Ming Liu, Jian Liu, Jiayang Qin, Zihan Chu, Cunxi Xu, Jialei Zhao, Wenbo Jiang, Junjun Liu, Xianming Wang, Ziyan Li, Chiwei Li, Shilong Yuan, Chendong Yang, Songyue Liu, Wentao Chen, Peng Zhou, Bin Wang, Yubo Zhang, Chi Sun, Jianhang Chen, Hai Yang, Xiao Wang, Lizhong Fu, Dongyi Lin, Yongchun Yang, Huitong Li, Haoang Luo, Yadan Cheng, Xianjing Xu, Yong Computer Vision and Pattern Recognition Robotics In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field.
title	The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
topic	Computer Vision and Pattern Recognition Robotics
url	https://arxiv.org/abs/2405.08816