Hyperspectral imaging increases the amount of information incorporated per pixel in comparison to normal color cameras. Conventional hyperspectral sensors as used in satellite imaging utilize spatial or spectral scanning during acquisition which is only suitable for static scenes. In dynamic scenarios, such as in autonomous driving applications, the acquisition of the entire hyperspectral cube at the same time is mandatory. In this work, we investigate the eligibility of novel snapshot hyperspectral cameras in dynamic scenarios such as in autonomous driving applications. These new sensors capture a hyperspectral cube containing 16 or 25 spectra without requiring moving parts or line-scanning. These sensors were mounted on land vehicles and used in several driving scenarios in rough terrain and dynamic scenes. We captured several hundred gigabytes of hyperspectral data which were used for terrain classification. We propose a random-forest classifier based on hyperspectral and spatial features combined with fully connected conditional random fields ensuring local consistency and context aware semantic scene segmentation. The classification is evaluated against a novel hyperspectral ground truth dataset specifically created for this purpose.