The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
<|reference_start|>The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition: 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.<|reference_end|>
@article{kong2024the,
    title={The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition},
    author={Lingdong Kong and Shaoyuan Xie and Hanjiang Hu and Yaru Niu and Wei
  Tsang Ooi and Benoit R. Cottereau and Lai Xing Ng and Yuexin Ma and Wenwei
  Zhang and Liang Pan and Kai Chen and Ziwei Liu and Weichao Qiu and Wei Zhang
  and Xu Cao and Hao Lu and Ying-Cong Chen and Caixin Kang and Xinning Zhou and
  Chengyang Ying and Wentao Shang and Xingxing Wei and Yinpeng Dong and Bo Yang
  and Shengyin Jiang and Zeliang Ma and Dengyi Ji and Haiwen Li and Xingliang
  Huang and Yu Tian and Genghua Kou and Fan Jia and Yingfei Liu and Tiancai
  Wang and Ying Li and Xiaoshuai Hao and Yifan Yang and Hui Zhang and Mengchuan
  Wei and Yi Zhou and Haimei Zhao and Jing Zhang and Jinke Li and Xiao He and
  Xiaoqiang Cheng and Bingyang Zhang and Lirong Zhao and Dianlei Ding and
  Fangsheng Liu and Yixiang Yan and Hongming Wang and Nanfei Ye and Lun Luo and
  Yubo Tian and Yiwei Zuo and Zhe Cao and Yi Ren and Yunfan Li and Wenjie Liu
  and Xun Wu and Yifan Mao and Ming Li and Jian Liu and Jiayang Liu and Zihan
  Qin and Cunxi Chu and Jialei Xu and Wenbo Zhao and Junjun Jiang and Xianming
  Liu and Ziyan Wang and Chiwei Li and Shilong Li and Chendong Yuan and Songyue
  Yang and Wentao Liu and Peng Chen and Bin Zhou and Yubo Wang and Chi Zhang
  and Jianhang Sun and Hai Chen and Xiao Yang and Lizhong Wang and Dongyi Fu
  and Yongchun Lin and Huitong Yang and Haoang Li and Yadan Luo and Xianjing
  Cheng and Yong Xu},
    journal={arXiv preprint arXiv:2405.08816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08816},
    primaryClass={cs.CV cs.RO}
}