Boundary-Aware Semantic Bird-Eye-View Map Generation Based on Conditional Diffusion Models

Semantic bird-eye-view (BEV) map is an efficient data representation for environment perception in autonomous driving. In real driving scenarios, the collected sensory data usually exhibit class imbalance. For example, road layouts are often the majority classes and road objects are the minority. Such imbalanced data could lead to inferior performance in BEV map generation, particularly for minority objects due to insufficient learning samples. This work attempts to mitigate this issue from the perspective of network and loss function design. To this end, a diffusion-guided semantic BEV map generation network with a boundary-aware loss is proposed. The network learns the underlying distribution of the data, including the relationship between majority and minority classes. The boundary-aware loss increases weighting for minority classes during training, making the network focus on these classes. Experimental results on a public dataset demonstrate our superiority over the state-of-the-art methods, and our effectiveness in addressing the class imbalance issue.