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Advancing driving perception technologies under challenging real-world conditions

Published on:

14 May 2024

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Lingdong Kong,

Shaoyuan Xie,

Hanjiang Hu,

Yaru Niu,

Wei Tsang Ooi,

Benoit R. Cottereau,

Lai Xing Ng,

Yuexin Ma,

Wenwei Zhang,

Liang Pan,

Kai Chen,

Ziwei Liu,

Weichao Qiu,

Wei Zhang,

Xu Cao,

Hao Lu,

Ying-Cong Chen,

Caixin Kang,

Xinning Zhou,

Chengyang Ying,

Wentao Shang,

Xingxing Wei,

Yinpeng Dong,

Bo Yang,

Shengyin Jiang,

Zeliang Ma,

Dengyi Ji,

Haiwen Li,

Xingliang Huang,

Yu Tian,

Genghua Kou,

Fan Jia,

Yingfei Liu,

Tiancai Wang,

Ying Li,

Xiaoshuai Hao,

Yifan Yang,

Hui Zhang,

Mengchuan Wei,

Yi Zhou,

Haimei Zhao,

Jing Zhang,

Jinke Li,

Xiao He,

Xiaoqiang Cheng,

Bingyang Zhang,

Lirong Zhao,

Dianlei Ding,

Fangsheng Liu,

Yixiang Yan,

Hongming Wang,

Nanfei Ye,

Lun Luo,

Yubo Tian,

Yiwei Zuo,

Zhe Cao,

Yi Ren,

Yunfan Li,

Wenjie Liu,

Xun Wu,

Yifan Mao,

Ming Li,

Jian Liu,

Jiayang Liu,

Zihan Qin,

Cunxi Chu,

Jialei Xu,

Wenbo Zhao,

Junjun Jiang,

Xianming Liu,

Ziyan Wang,

Chiwei Li,

Shilong Li,

Chendong Yuan,

Songyue Yang,

Wentao Liu,

Peng Chen,

Bin Zhou,

Yubo Wang,

Chi Zhang,

Jianhang Sun,

Hai Chen,

Xiao Yang,

Lizhong Wang,

Dongyi Fu,

Yongchun Lin,

Huitong Yang,

Haoang Li,

Yadan Luo,

Xianjing Cheng,

Yong Xu

Bullets

Key Details

140 teams from 93 institutes across 11 countries participated, with ~1000 submissions

Innovations in data augmentation, sensor fusion, self-supervised learning, new algorithms

Significantly advanced state-of-the-art in handling sensor failures and environment changes

Highlighted key trends and strategies for improving robustness of driving perception

AI generated summary

Advancing driving perception technologies under challenging real-world conditions

The 2024 RoboDrive Challenge focused on innovating robust perception systems for autonomous vehicles that can withstand diverse disturbances like weather changes and sensor failures. 140 international teams participated, pushing boundaries. Key innovations emerged in data augmentation, sensor fusion, self-supervised learning, and new algorithms that handle sensor inconsistencies and environment variability more effectively. Extensive analysis of solutions provided insights to guide future research towards safer, more reliable autonomous systems.

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