AI RESEARCH
BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving
arXiv CS.CV
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ArXi:2408.16322v4 Announce Type: replace Current research in semantic bird's-eye view segmentation for autonomous driving focuses solely on optimizing neural network models using a single dataset, typically nuScenes. This practice leads to the development of highly specialized models that may fail when faced with different environments or sensor setups, a problem known as domain shift. In this paper, we conduct a comprehensive cross-dataset evaluation of state-of-the-art BEV segmentation models to assess their performance across different.