Object Detection and Tracking for Autonomous Vehicles in Adverse Weather Conditions

Object detection and tracking is a central aspect of perception for autonomous vehicles. While there has been significant development in this field in recent years, many perception algorithms still struggle to provide reliable information in challenging weather conditions which include night-time, direct sunlight, glare, fog, etc. To achieve full autonomy, there is a need for a robust perception system capable of handling such challenging conditions. In this paper, we attempt to bridge this gap by proposing an algorithm that combines the strength of automotive radars and infra-red thermal cameras. We show that these sensors complement each other well and provide reliable data in poor visibility conditions. We demonstrate the advantages of a thermal camera over a visible-range camera in these situations and employ YOLOv3 for object detection. The proposed system utilizes a modified Track-Oriented Multiple Hypothesis Tracking (MHT) algorithm which uses data from these sensors to keep track of the surrounding vehicles. The modifications in the well-known MHT algorithm were introduced in order to curb the exponential growth of possible hypotheses and consequently reduce the computational time without loss of any critical information. To validate the system, we provide a real-time implementation on an urban dataset collected at the Texas A&M University.