Seq-BEV: Semantic Bird-Eye-View Map Generation in Full View Using Sequential Images for Autonomous Driving

Semantic Bird-Eye-View (BEV) map is a straightforward data representation for environment perception. It can be used for downstream tasks, such as motion planning and trajectory prediction. However, taking as input a front-view image from a single camera, most existing methods can only provide V-shaped semantic BEV maps, which limits the field-of-view for the BEV maps. To provide a solution to this problem, we propose a novel end-to-end network to generate semantic BEV maps in full view by taking as input the equidistant sequential images. Specifically, we design a self-adapted sequence fusion module to fuse the features from different images in a distance sequence. In addition, a road-aware view transformation module is introduced to wrap the front-view feature map into BEV based on an attention mechanism. We also create a dataset with semantic labels in full BEV from the public nuScenes data. The experimental results demonstrate the effectiveness of our design and the superiority over the state-of-the-art methods.