The use of hyperspectral imaging (HSI) in autonomous driving (AD), while promising, faces many challenges related to the specifics and requirements of thisapplication domain. On the one hand, non-controlled and variable lighting conditions, the wide depth-of-field ranges, and dynamic scenes with fastmoving objects. On the other hand, the requirements for realtime operation and the and the limited computational capabilities of embedded platforms. The combination of these factors determines both the criteria for selecting appropriate HSI technologies and the development of customized 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.