Panoptic Driving Perception (PDP) is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). A PDP model typically integrates multiple tasks, necessitating the simultaneous and robust execution of various perception tasks to facilitate downstream path planning. The fusion of visual and radar sensors is currently acknowledged as a robust and cost-effective approach. However, most existing research has primarily focused on fusing visual and radar features dedicated to object detection or utilizing a shared feature space for multiple tasks, neglecting the individual representation differences between various tasks. To address this gap, we propose a pair of Asymmetric Fair Fusion (AFF) modules with favorable explainability designed to efficiently interact with independent features from both visual and radar modalities, tailored to the specific requirements of object detection and semantic segmentation tasks. The AFF modules treat image and radar maps as irregular point sets and transform these features into a crossed-shared feature space for multitasking, ensuring equitable treatment of vision and radar point cloud features. Leveraging AFF modules, we propose a novel and efficient PDP model, ASY-VRNet, which processes image and radar features based on irregular super-pixel point sets. Additionally, we propose an effective multitask learning method specifically designed for PDP models. Compared to other lightweight models, ASY-VRNet achieves state-of-the-art performance in object detection, semantic segmentation, and drivable-area segmentation on the WaterScenes benchmark. Our project is publicly available at https://github.com/GuanRunwei/ASY-VRNet.

翻译：全景驾驶感知（PDP）对于无人水面艇（USV）的自主导航至关重要。PDP模型通常集成多项任务，需要同时稳健地执行多种感知任务以支持下游路径规划。视觉与雷达传感器的融合目前被公认为一种稳健且经济高效的方法。然而，现有研究大多集中于专用于目标检测的视觉与雷达特征融合，或利用共享特征空间处理多任务，忽视了不同任务间各自表征的差异性。为弥补这一不足，我们提出了一对具有良好可解释性的非对称公平融合（AFF）模块，旨在根据目标检测与语义分割任务的具体需求，高效交互来自视觉和雷达模态的独立特征。AFF模块将图像与雷达图视为不规则点集，并将这些特征转换到一个交叉共享的特征空间中以处理多任务，确保视觉与雷达点云特征得到公平对待。基于AFF模块，我们提出了一种新颖高效的PDP模型——ASY-VRNet，该模型基于不规则超像素点集处理图像与雷达特征。此外，我们还提出了一种专为PDP模型设计的有效多任务学习方法。与其他轻量级模型相比，ASY-VRNet在WaterScenes基准测试中，于目标检测、语义分割及可行驶区域分割任务上均达到了最先进的性能。我们的项目已公开于 https://github.com/GuanRunwei/ASY-VRNet。