  Perception is essential for autonomous driving system. Recent approaches
based on Bird's-eye-view (BEV) and deep learning have made significant
progress. However, there exists challenging issues including lengthy
development cycles, poor reusability, and complex sensor setups in perception
algorithm development process. To tackle the above challenges, this paper
proposes a novel hierarchical BEV perception paradigm, aiming to provide a
library of fundamental perception modules and user-friendly graphical
interface, enabling swift construction of customized models. We conduct the
Pretrain-Finetune strategy to effectively utilize large scale public datasets
and streamline development processes. Moreover, we present a Multi-Module
Learning (MML) approach, enhancing performance through synergistic and
iterative training of multiple models. Extensive experimental results on the
Nuscenes dataset demonstrate that our approach renders significant improvement
over the traditional training scheme.
