3D Object Visibility Prediction in Autonomous Driving
Chuanyu Luo Nuo Cheng Ren Zhong Haipeng Jiang Wenyu Chen Aoli Wang Pu Li
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.