In this report, we describe the technical details of our submission to the 2024 RoboDrive Challenge Robust Map Segmentation Track. The Robust Map Segmentation track focuses on the segmentation of complex driving scene elements in BEV maps under varied driving conditions. Semantic map segmentation provides abundant and precise static environmental information crucial for autonomous driving systems' planning and navigation. While current methods excel in ideal circumstances, e.g., clear daytime conditions and fully functional sensors, their resilience to real-world challenges like adverse weather and sensor failures remains unclear, raising concerns about system safety. In this paper, we explored several methods to improve the robustness of the map segmentation task. The details are as follows: 1) Robustness analysis of utilizing temporal information; 2) Robustness analysis of utilizing different backbones; and 3) Data Augmentation to boost corruption robustness. Based on the evaluation results, we draw several important findings including 1) The temporal fusion module is effective in improving the robustness of the map segmentation model; 2) A strong backbone is effective for improving the corruption robustness; and 3) Some data augmentation methods are effective in improving the robustness of map segmentation models. These novel findings allowed us to achieve promising results in the 2024 RoboDrive Challenge-Robust Map Segmentation Track.

本文描述了我们在2024年RoboDrive Challenge Robust Map Segmentation Track中提交的技术细节。我们通过探索几种方法来提高地图分割任务的稳健性，包括利用时间信息、使用不同的骨干网络以及数据增强等。通过评估结果，我们得出了一些重要的发现，包括时间融合模块在改善地图分割模型的稳健性方面是有效的，强大的骨干网络可提高其对干扰的稳定性，以及某些数据增强方法对地图分割模型的稳健性有积极影响。这些创新性的发现使我们在2024年RoboDrive Challenge-Robust Map Segmentation Track取得了令人期待的结果。

三星-罗斯福研究所团队：2024机器人驱动挑战-鲁棒地图分割赛道技术报告