With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth. The prevailing framework for multi-sensor autonomous driving encompasses sensor installation, perception, path planning, decision-making, and motion control. At the perception phase, a common approach involves utilizing neural networks to infer 3D bounding box (Bbox) attributes from raw sensor data, including classification, size, and orientation. In this paper, we present a novel attribute and its corresponding algorithm: 3D object visibility. By incorporating multi-task learning, the introduction of this attribute, visibility, negligibly affects the model's effectiveness and efficiency. Our proposal of this attribute and its computational strategy aims to expand the capabilities for downstream tasks, thereby enhancing the safety and reliability of real-time autonomous driving in real-world scenarios.

翻译：随着硬件与软件技术的快速发展，自动驾驶研究取得了显著进展。当前多传感器自动驾驶的主流框架涵盖传感器安装、感知、路径规划、决策制定与运动控制等环节。在感知阶段，常见方法是通过神经网络从原始传感器数据中推断3D边界框属性，包括分类、尺寸与朝向。本文提出了一种新颖属性及其对应算法：3D物体可见性。通过引入多任务学习，该"可见性"属性的添加对模型效果与效率的影响可忽略不计。我们提出该属性及其计算策略的目标是拓展下游任务的能力边界，从而提升现实场景中实时自动驾驶系统的安全性与可靠性。