剖析高光谱成像在自动驾驶中的挑战与解决方案
Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case
- 基于HSI-Drive数据集分析高光谱视觉算法设计思路
- 验证复杂光照与动态场景下高光谱信息的可用性
- 适合研究自动驾驶感知与多模态传感器融合的学者
高光谱成像（HSI）在自动驾驶（AD）中虽具前景，但仍面临非受控光照、宽景深范围、快速移动物体等动态场景挑战，同时需满足嵌入式平台实时运行与计算资源受限的要求。这些因素共同决定了适宜的HSI技术选型及定制化视觉算法的开发。本文以最新版HSI-Drive数据集实验结果为例，分析多种基于HSI的视觉系统技术，探讨其在自动驾驶应用中的可行性与优化方向。
原文摘要 · Abstract (English)
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.
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