The current de-facto Automotive Driver Assist System (ADAS) sensor suite typically comprises mutually dependent visible-light cameras and radar, but when one of these sensors becomes ineffective, so too does the entire sensor suite. This scenario happens often especially when it comes to pedestrians, cyclists, and animals at night or in inclement weather. Studies by the Insurance Institute for Highway Safety (IIHS) have shown that systems now installed on automobiles intended to protect pedestrians, fail to work at night – when more than 76% of the annual 700,000 fatalities occur. Consequently, the U.S. National Highway Traffic Safety Administration (NHTSA) has joined EU NCAP in mandating pedestrian safety regulations through Automatic Emergency Braking (PAEB) for all new vehicles. The NHTSA mandate now requires a vehicle to be able to avoid hitting pedestrians even in total darkness. We will discuss how thermal imaging dramatically improves pedestrian safety to reduce accidents and save lives using HD thermal imaging and innovative AI/ML based computer vision algorithms. Operating in the thermal IR spectrum (8000 to 14000 nm) these algorithms exploit angular, temporal and intensity data to produce ultra-dense 3D point clouds (up to 150x that of LIDAR) along with highly refined object classification and fusion.