Obstacle-sensitive Semantic Bird-Eye-View Map Generation with Boundary-aware Loss for Autonomous driving

Detection of road obstacles is important for autonomous driving. However, road obstacles, like pedestrians, usually account for quite a small portion compared with other semantics, such as road layouts. This leads to the class-imbalance problem in real-world driving datasets and hinders environment perception for autonomous driving. In this paper, we propose an obstacle-sensitive network to improve the semantic Bird-Eye-View (BEV) map generation performance for minority classes. To this end, a context-depth attention module and a boundary-aware loss are introduced. We conduct ablation studies to verify the effectiveness of the proposed network. We also compare our network with other semantic BEV map generation methods. The results demonstrate that our network achieves better performance in terms of semantic BEV map generation, especially for minority classes.