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.

翻译：实时处理在自动驾驶系统中至关重要，因为系统必须即时决策并快速响应。在现实场景中，自动驾驶车辆需持续解读周围环境、分析复杂的传感器数据，并在毫秒级时间内做出决策，通过多项计算机视觉任务确保安全。本文提出一种新型实时多任务网络，该网络擅长三项关键自动驾驶任务：单目3D物体检测、语义分割与密集深度估计。为应对多任务学习中普遍存在的负迁移挑战，我们引入一种任务自适应注意力生成器。该生成器能自动判别三项任务之间的相互关联，并规划任务共享模式，同时充分利用硬参数共享方法的效率优势。据我们所知，所提出的模型是首个能够同时处理包含3D物体检测在内的多项任务且保持实时处理速度的模型。在Cityscapes-3D数据集上的测试表明，经过严格优化的网络持续优于多种基线模型。此外，深入的消融研究证实了我们框架中集成方法的有效性。