The use of hyperspectral imaging (HSI) in autonomous driving (AD), while promising, faces many challenges related to the specifics and requirements of this application domain. On the one hand, non-controlled and variable lighting conditions, the wide depth-of-field ranges, and dynamic scenes with fast-moving objects. On the other hand, the requirements for real-time operation and the limited computational resources of embedded platforms. The combination of these factors determines both the criteria for selecting appropriate HSI technologies and the development of custom vision algorithms that leverage the spectral and spatial information obtained from the sensors. In this article, we analyse several techniques explored in the research of HSI-based vision systems with application to AD, using as an example results obtained from experiments using data from the most recent version of the HSI-Drive dataset.

翻译：高光谱成像（HSI）在自主驾驶（AD）中的应用虽前景广阔，但面临诸多与该应用领域特性及要求相关的挑战。一方面，存在非受控与多变光照条件、大景深范围以及包含快速移动物体的动态场景；另一方面，实时运行需求与嵌入式平台有限的计算资源相互制约。这些因素的结合，既决定了选择合适高光谱成像技术的标准，也推动着利用传感器获取的光谱与空间信息开发定制化视觉算法的进程。本文以基于最新版HSI-Drive数据集实验所获结果为例，系统分析了面向自主驾驶的高光谱成像视觉系统中多项被探索的技术方案。