Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving

• URL: http://arxiv.org/abs/2407.12491v2
• Date: Thu, 25 Jul 2024 21:55:44 GMT
• Title: Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
• Authors: Yuqi Dai, Jian Sun, Shengbo Eben Li, Qing Xu, Jianqiang Wang, Lei He, Keqiang Li,
• Abstract summary: This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
• Score: 52.808273563372126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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