Robust multitask learning framework for panoptic autonomous driving perception

Abstract

This study puts forward a resilient cross-task modeling architecture to overcome the precision-versus-latency dilemma in comprehensive traffic scene understanding. The backbone employs a C2f module to enhance gradient flow and small-object detection, while structural re-parameterization via repVGG blocks enables multi-branch training and fast single-path inference. In the segmentation decoder, the CARAFE content-aware upsampling operator replaces nearest-neighbor interpolation to preserve fine edge details, significantly improving lane line continuity. When assessed on the BDD100K corpus, our model's empirical results indicate it surpasses YOLOP as well as HybridNets in detecting objects, segmenting drivable zones, and delineating lane markings. Removal experiments verify the synergistic contributions of each module. Operating at 186 frames per second and utilizing 16.89 million parameters, the network achieves an advantageous tradeoff between precision and computational efficiency for perception tasks in autonomous vehicles.