Kavli Affiliate: Yi Zhou
| First 5 Authors: Xiaoshuai Hao, Yifan Yang, Hui Zhang, Mengchuan Wei, Yi Zhou
| Summary:
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.
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