Temperature-based Collision Detection in Extreme Low Light Condition with Bio-inspired LGMD Neural Network

• Published in: Journal of physics. Conference series Vol. 2224; no. 1; pp. 12004 - 12013
• Main Authors: Zhang, Yicheng, Hu, Cheng, Liu, Mei, Luan, Hao, Lei, Fang, Cuayahuitl, Heriberto, Yue, Shigang
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.04.2022
• Subjects: Algorithms; Collision avoidance; Darkness; Insects; Intelligent vehicles; Light sources; Neural networks; Night; Physics; Temperature gradients
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2224/1/012004

Abstract It is an enormous challenge for intelligent vehicles to avoid collision accidents at night because of the extremely poor light conditions. Thermal cameras can capture temperature map at night, even with no light sources and are ideal for collision detection in darkness. However, how to extract collision cues efficiently and effectively from the captured temperature map with limited computing resources is still a key issue to be solved. Recently, a bio-inspired neural network LGMD has been proposed for collision detection successfully, but for daytime and visible light. Whether it can be used for temperature-based collision detection or not remains unknown. In this study, we proposed an improved LGMD-based visual neural network for temperature-based collision detection at extreme light conditions. We show in this study that the insect inspired visual neural network can pick up the expanding temperature differences of approaching objects as long as the temperature difference against its background can be captured by a thermal sensor. Our results demonstrated that the proposed LGMD neural network can detect collisions swiftly based on the thermal modality in darkness; therefore, it can be a critical collision detection algorithm for autonomous vehicles driving at night to avoid fatal collisions with humans, animals, or other vehicles.