Visual Perception Robustness: Evolution, Challenges, and Frontiers for Image Restoration under Adverse Road Traffic Conditions

Ensuring robust visual perception under adverse weather conditions is a critical prerequisite for the safety and reliability of autonomous driving systems. Camera-based perception, despite providing rich semantic information, suffers significant degradation in rain, fog, snow, and low-light scenarios, thus presenting a key bottleneck to all-weather, all-scenario autonomy. This paper systematically reviews the evolution of image restoration techniques for road traffic environments, tracing the progression from traditional physical-model based approaches to data-driven deep learning paradigms. The domain gap between synthetic training datasets and real-world applications is identified. Misalignment between restoration metrics and downstream perception performance is also highlighted, revealing that improvements in image quality do not necessarily translate into enhanced perception outcomes. The review advocates for a paradigm shift toward perception-oriented, semantically-guided, and unsupervised domain-adaptive joint frameworks, which integrates image restoration and perception in an end-to-end manner, utilizes semantic priors for region-specific enhancement, and employs self-supervised signals to fully exploit unlabeled real-world data. At last, this work concludes with prospects for future research, emphasizing the necessity of bridging the restoration-perception gap, extending solutions to more complex degradations and tasks, and leveraging foundation models as intelligent controllers for robust visual systems in autonomous driving.