A semantic map of the road scene, covering fundamental road elements, is an
essential ingredient in autonomous driving systems. It provides important
perception foundations for positioning and planning when rendered in the
Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can
guide the learning of translating front perspective views into BEV directly
with the help of calibration parameters. However, it suffers from geometric
distortions in the representation of distant objects. In addition, another
stream of methods without prior knowledge can learn the transformation between
front perspective views and BEV implicitly with a global view. Considering that
the fusion of different learning methods may bring surprising beneficial
effects, we propose a Bi-Mapper framework for top-down road-scene semantic
understanding, which incorporates a global view and local prior knowledge. To
enhance reliable interaction between them, an asynchronous mutual learning
strategy is proposed. At the same time, an Across-Space Loss (ASL) is designed
to mitigate the negative impact of geometric distortions. Extensive results on
nuScenes and Cam2BEV datasets verify the consistent effectiveness of each
module in the proposed Bi-Mapper framework. Compared with exiting road mapping
networks, the proposed Bi-Mapper achieves 5.0 higher IoU on the nuScenes
dataset. Moreover, we verify the generalization performance of Bi-Mapper in a
real-world driving scenario. Code will be available at
this https URL

提出了一个全局视角和局部先验知识相结合的双映射框架 (Bi-Mapper)，包括异步相互学习策略和横跨空间损失函数 (ASL)，旨在提高自动驾驶系统中道路场景语义理解的可靠性，并在 nuScenes 和 Cam2BEV 数据集上进行了验证。