Autonomous Vehicles Get a Boost to Seeing in the Dark

To overcome this problem, Tian and his student Yang Lu turned to a theory that attempts to explain why humans are able to discern colors relatively well under low-light conditions. The Retinex theory suggests that our visual system is able to discern light in two different ways—namely, the reflectance and illumination components of the light. Even in low-light conditions, our eyes and brain are able to compensate for changes in the illumination of the light enough to discern colors.

Tian’s team applied this concept to their autonomous-car-navigation system, which processes the reflective and luminescence qualities of polarized light separately. One algorithm—trained using real-world data of the same images in light and dark conditions—works like our own visual system to compensate for changes in brightness. A second algorithm processes the reflective properties of incoming light, removing background noise.

Not sure if I fully grasp how this works, but it sounds cool!