Adaptive Sensor Fusion Algorithms for Autonomous Systems in Adverse Weather Conditions

Abstract

This paper presents an adaptive sensor fusion algorithm designed to enhance the performance of autonomous systems operating under adverse weather conditions. Traditional sensor fusion methods struggle with data inconsistencies caused by environmental factors such as fog, rain, and snow, which compromise the reliability of LiDAR and radar inputs. To address this challenge, we propose a novel fusion framework integrating machine learning-based adaptive weighting to dynamically adjust sensor contributions based on weather conditions. The proposed algorithm is validated using simulated and real-world datasets, demonstrating superior robustness, accuracy, and computational efficiency compared to state-of-the-art methods. Experimental results show an 18% improvement in obstacle detection accuracy and a 23% reduction in false positives under adverse conditions. These findings suggest significant potential for improving the safety and reliability of autonomous systems in real-world scenarios.