Mazhr, Ahmed (German University in Cairo), El Mougy, Amr (American University in Cairo)

Autonomous Vehicle Perception Using Monocular Thermal Imaging Cameras

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception IV" (ThBT5), Thursday, September 26, 2024, 16:10−16:30, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

This paper investigates the integration of monocular thermal imaging cameras into autonomous vehicles to address challenges faced by conventional sensors in adverse conditions. This study explores the performance of thermal cameras in low-light, darkness, and adverse weather, aiming to enhance safety and efficiency. To achieve that, the research proposes thedevelopment of machine learning algorithms tailored for interpreting thermal image. These algorithms would facilitate accurate object detection and road segmentation, overcoming limitations of conventional algorithms. The goal is to create a system that generates a bird’s-eye view spatial map, highlighting drivable regions and detected objects. Comparative analysis shows that the proposed thermal camera architecture competes favorably, particularly in adverse conditions, compared to commonly used imaging sensors and architectures.