Sparse U-PDP: A Unified Multi-Task Framework for Panoptic Driving Perception

Abstract

This paper proposes a multi-task unified decoding framework for the panoptic driving perception, Sparse U-PDP. This framework’s primary objective is to combine vehicle object detection, lane detection and drivable area segmentation tasks. This paper mainly builds a multi-task unified decoder and further explores whether the potential connection between multi-tasks can improve the robustness of the model. Experiments demonstrate that the proposed Sparse U-PDP outperforms the present state-of-the-art multi-task model in terms of accuracy. The primary contributions of this work are as follows: First, we present the approach to unified multi-task representation. We abstract the multi-task into the “dynamic convolution kernels” representation form to build a highly unified multi-task decoder. Second, we use the proposed dynamic interaction module to establish different feature sampling pipelines for various task features. Lastly, our model is verified on the BDD100K dataset, where we achieve an AP50 of 84.1 in the “vehicle” category, 32.0 IoU in the lane detection, and 93.0 mIoU in the drivable area segmentation with the helper of CSP-Darknet. That is to say; Sparse U-PDP verifies that a more unified task representation form can implicitly increase the mutual help between different task branches.