BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving
This addresses the need for more robust and generalizable BEV segmentation models in autonomous driving, though it is incremental as it focuses on evaluation rather than proposing a new method.
The study tackled the problem of domain shift in bird's-eye view segmentation models for autonomous driving by conducting a cross-dataset evaluation, finding that multi-dataset training improves performance compared to single-dataset training.
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 training and testing datasets and setups, as well as different semantic categories. We investigate the influence of different sensors, such as cameras and LiDAR, on the models' ability to generalize to diverse conditions and scenarios. Additionally, we conduct multi-dataset training experiments that improve models' BEV segmentation performance compared to single-dataset training. Our work addresses the gap in evaluating BEV segmentation models under cross-dataset validation. And our findings underscore the importance of enhancing model generalizability and adaptability to ensure more robust and reliable BEV segmentation approaches for autonomous driving applications. The code for this paper available at https://github.com/manueldiaz96/beval .