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.

自动驾驶中，强大的感知能力在面对各种未知条件时对车辆的安全部署至关重要。以四个关键任务为中心的 RoboDrive Challenge 比赛通过推动驾驶感知技术的发展来适应现实世界中的各种变化。通过对 140 个注册团队的近千份解决方案进行评估分析，比赛取得了显著的成果，特别是在处理传感器不一致性和环境变异性方面。这个挑战为该领域设立了新的基准，为未来的研究提供了丰富的技术参考。