  Bird's-eye-view (BEV) semantic segmentation is becoming crucial in autonomous
driving systems. It realizes ego-vehicle surrounding environment perception by
projecting 2D multi-view images into 3D world space. Recently, BEV segmentation
has made notable progress, attributed to better view transformation modules,
larger image encoders, or more temporal information. However, there are still
two issues: 1) a lack of effective understanding and enhancement of BEV space
features, particularly in accurately capturing long-distance environmental
features and 2) recognizing fine details of target objects. To address these
issues, we propose OE-BevSeg, an end-to-end multimodal framework that enhances
BEV segmentation performance through global environment-aware perception and
local target object enhancement. OE-BevSeg employs an environment-aware BEV
compressor. Based on prior knowledge about the main composition of the BEV
surrounding environment varying with the increase of distance intervals,
long-sequence global modeling is utilized to improve the model's understanding
and perception of the environment. From the perspective of enriching target
object information in segmentation results, we introduce the center-informed
object enhancement module, using centerness information to supervise and guide
the segmentation head, thereby enhancing segmentation performance from a local
enhancement perspective. Additionally, we designed a multimodal fusion branch
that integrates multi-view RGB image features with radar/LiDAR features,
achieving significant performance improvements. Extensive experiments show
that, whether in camera-only or multimodal fusion BEV segmentation tasks, our
approach achieves state-of-the-art results by a large margin on the nuScenes
dataset for vehicle segmentation, demonstrating superior applicability in the
field of autonomous driving.
