BEV segmentation models for autonomous driving lack generalizability across datasets

A new study published on arXiv evaluates the performance of Bird's-Eye View (BEV) segmentation models used in autonomous driving. Researchers found that models trained on single datasets, like nuScenes, tend to overfit and perform poorly when applied to different environments or sensor configurations, a phenomenon known as domain shift. The study advocates for cross-dataset validation to improve model generalizability and adaptability, demonstrating that multi-dataset training enhances performance compared to single-dataset approaches. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Highlights the need for more robust BEV segmentation models that generalize across diverse datasets and sensor inputs for autonomous driving.

RANK_REASON The cluster contains an academic paper presenting a new evaluation methodology for existing models.

Read on arXiv cs.CV →

• arXiv
• BEVal
• nuScenes
• autonomous driving
• LiDAR
• camera

• paper
• other