  Autonomous driving relies on deriving understanding of objects and scenes
through images. These images are often captured by sensors in the visible
spectrum. For improved detection capabilities we propose the use of thermal
sensors to augment the vision capabilities of an autonomous vehicle. In this
paper, we present our investigations on the fusion of visible and thermal
spectrum images using a publicly available dataset, and use it to analyze the
performance of object recognition on other known driving datasets. We present
an comparison of object detection in night time imagery and qualitatively
demonstrate that thermal images significantly improve detection accuracy.
