Real-time processing is crucial in autonomous driving systems due to the
imperative of instantaneous decision-making and rapid response. In real-world
scenarios, autonomous vehicles are continuously tasked with interpreting their
surroundings, analyzing intricate sensor data, and making decisions within
split seconds to ensure safety through numerous computer vision tasks. In this
paper, we present a new real-time multi-task network adept at three vital
autonomous driving tasks: monocular 3D object detection, semantic segmentation,
and dense depth estimation. To counter the challenge of negative transfer,
which is the prevalent issue in multi-task learning, we introduce a
task-adaptive attention generator. This generator is designed to automatically
discern interrelations across the three tasks and arrange the task-sharing
pattern, all while leveraging the efficiency of the hard-parameter sharing
approach. To the best of our knowledge, the proposed model is pioneering in its
capability to concurrently handle multiple tasks, notably 3D object detection,
while maintaining real-time processing speeds. Our rigorously optimized
network, when tested on the Cityscapes-3D datasets, consistently outperforms
various baseline models. Moreover, an in-depth ablation study substantiates the
efficacy of the methodologies integrated into our framework.

本文介绍了一种新的实时多任务网络，包括单目三维物体检测、语义分割和密集深度估计，通过引入任务自适应注意力生成器来解决多任务学习中普遍存在的负迁移问题，并利用硬参数共享方法提高效率，能够同时处理多个任务，尤其是三维物体检测，并保持实时处理速度。经过在 Cityscapes-3D 数据集上的严格优化和深入剖析研究，我们的网络始终优于各种基准模型。