Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case
It addresses practical issues for autonomous driving systems, but is incremental as it reviews existing methods.
The paper tackles the challenges of applying hyperspectral imaging to autonomous driving, such as variable lighting and real-time constraints, by analyzing techniques using the HSI-Drive dataset.
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.