GDMNet: A Unified Multi-Task Network for Panoptic Driving Perception

To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation Network (FMN). Lastly, the Minimum Point Distance IoU (MPDIoU) loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes, facilitating model training adjustments. Experimental results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union (mIoU) of 92.2%, lane detection accuracy and intersection over union (IoU) of 75.3% and 26.4%, respectively, and traffic object detection recall and mAP of 89.7% and 78.2%, respectively. The detection performance surpasses that of other single-task or multi-task algorithm models.