LAUSR

For vehicles to navigate autonomously, they need to perceive and understand their immediate surroundings. Currently, cameras are the preferred sensors, due to their high performance and relatively low-cost compared with other sensors like LiDARs and Radars. However, their performance is limited by inherent imaging constraints, a standard RGB camera may perform poorly in extreme conditions, including low illumination, high contrast, bad weather (e.g. fog, rain, snow, etc.), glare, etc. Further, when using monocular cameras, it is more challenging to determine spatial distances than when using active range sensors such as LiDARs or Radars. Over the past years, novel image sensors, namely, infrared cameras, range-gated cameras, polarization cameras, and event cameras, have demonstrated strong potential. Some of them could be game-changers for future autonomous vehicles, they are the result of progress in sensor technology and the development of the accompanying perception algorithms. This paper presents in a systematic manner their principles, comparative advantages, data processing algorithms, and related applications. The purpose is to provide practitioners with an in-depth overview of novel sensing technologies that can contribute to the safe deployment of autonomous vehicles.