  Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes
expensive Lidar sensors, making it a feasible solution for economical
autonomous driving. However, most existing BEV solutions either suffer from
modest performance or require considerable resources to execute on-vehicle
inference. This paper proposes a simple yet effective framework, termed
Fast-BEV, which is capable of performing real-time BEV perception on the
on-vehicle chips. Towards this goal, we first empirically find that the BEV
representation can be sufficiently powerful without expensive view
transformation or depth representation. Starting from M2BEV baseline, we
further introduce (1) a strong data augmentation strategy for both image and
BEV space to avoid over-fitting (2) a multi-frame feature fusion mechanism to
leverage the temporal information (3) an optimized deployment-friendly view
transformation to speed up the inference. Through experiments, we show Fast-BEV
model family achieves considerable accuracy and efficiency on edge. In
particular, our M1 model (R18@256x704) can run over 50FPS on the Tesla T4
platform, with 47.0% NDS on the nuScenes validation set. Our largest model
(R101@900x1600) establishes a new state-of-the-art 53.5% NDS on the nuScenes
validation set. The code is released at: https://github.com/Sense-GVT/Fast-BEV.
