Sensor blockage in autonomous vehicles: AI-driven detection and mitigation strategies

This article presents a comprehensive analysis of sensor blockage in autonomous vehicles, addressing a critical challenge to reliable perception systems across diverse environmental conditions. We examine the multifaceted nature of sensor contamination—from environmental factors like precipitation and dust to seasonal challenges such as ice formation—and their differential impact across LiDAR, camera, radar, and ultrasonic sensing modalities. Through systematic investigation, we demonstrate that AI-driven approaches significantly outperform traditional methods in detection accuracy, response time, and adaptability to complex blockage scenarios. Our research introduces novel methodologies for real-time blockage identification using deep learning architectures, automated cleaning systems optimized for resource efficiency, and adaptive sensor fusion strategies that maintain operational integrity during degraded conditions. Experimental validation across both simulation environments and extensive field trials reveals substantial improvements in perception reliability, with implemented systems reducing blockage-related failures by over 80% compared to unprotected baselines. We identify remaining challenges in extreme weather operation, mixed contamination scenarios, and resource limitations during extended adverse conditions, while outlining promising research directions in emerging sensor technologies, advanced AI architectures, and integrated health monitoring systems. These findings provide critical insights for enhancing the all-weather capability of autonomous vehicles, representing an essential step toward safe, reliable autonomous transportation.