Image Dehazing Techniques in Autonomous Driving Systems

Abstract

With self-driving technology, safety under intricate conditions for vehicles is indispensable. Image dehazing as a key element of visual sensing for self-driving vehicles is indispensable under poor environmental conditions. In complex traffic scenarios, visual perception systems must accurately recognize obstacles, traffic signs, and lane structures to ensure driving safety. However, under adverse weather conditions such as fog, haze, or sandstorms, image quality can severely degrade, leading to diminished detection performance and potential safety risks. The paper reviews the progression of image dehazing for self-driving vehicles with classical methods, deep learning-based methods, and unsupervised learning-based methods. The classical methods show efficiency but cannot cope with instability under intricate conditions. Deep learning-based methods, particularly CNNs and Transformers, have registered remarkable progress with issues of computational costs and data adaptability. Unsupervised learning-based methods reduce reliance on labeled data with issues of training instability and incomplete reconstruction. The paper also explores image dehazing as an integration with object detection and multi-sensor fusion, with future directions on lightweight design, data generalization, and multi-modal fusion.