  The recent advances in camera-based bird's eye view (BEV) representation
exhibit great potential for in-vehicle 3D perception. Despite the substantial
progress achieved on standard benchmarks, the robustness of BEV algorithms has
not been thoroughly examined, which is critical for safe operations. To bridge
this gap, we introduce RoboBEV, a comprehensive benchmark suite that
encompasses eight distinct corruptions, including Bright, Dark, Fog, Snow,
Motion Blur, Color Quant, Camera Crash, and Frame Lost. Based on it, we
undertake extensive evaluations across a wide range of BEV-based models to
understand their resilience and reliability. Our findings indicate a strong
correlation between absolute performance on in-distribution and
out-of-distribution datasets. Nonetheless, there are considerable variations in
relative performance across different approaches. Our experiments further
demonstrate that pre-training and depth-free BEV transformation has the
potential to enhance out-of-distribution robustness. Additionally, utilizing
long and rich temporal information largely helps with robustness. Our findings
provide valuable insights for designing future BEV models that can achieve both
accuracy and robustness in real-world deployments.
