Information Bottleneck-Inspired Spatial Attention for Robust Semantic Segmentation in Autonomous Driving

Semantic segmentation is critical for autonomous driving systems that rely on accurate scene understanding to ensure safety and reliability. However, domain shifts between training and testing datasets, as well as challenges in nighttime conditions, significantly hinder the performance of semantic segmentation models when deployed in real-world driving scenarios. This paper presents an approach for addressing these challenges in semantic segmentation by leveraging an Information Bottleneck (IB)-inspired spatial attention mechanism. By integrating IB principles into the spatial attention framework, the model could capture essential features while filtering out irrelevant information, thereby improving its generalization capability across diverse domains and enhancing performance in nighttime segmentation. Extensive experiments demonstrate that IB-inspired attention consistently enhances domain generation and nighttime performance, showing robust semantic segmentation for autonomous driving.