Multi-Sensor Data Fusion and Management Strategies for Robust Perception in Autonomous Vehicles

Abstract

Multi-sensor data fusion has emerged as a critical enabler for robust perception in autonomous vehicles, where the reliability and accuracy of environmental understanding directly impact operational safety and efficiency. This paper presents a comprehensive investigation of cutting-edge data fusion approaches and management strategies that address the challenges of sensor heterogeneity, dynamic driving conditions, and computational constraints. We examine various sensor modalities, including LiDAR, radar, and camera systems, and discuss the advantages of combining their complementary strengths to enhance perception and situational awareness. By exploring state-of-the-art algorithms that integrate machine learning models with probabilistic filtering techniques, we illustrate how high-fidelity maps and real-time sensing can be synchronized to form a unified representation of the environment. Detailed mathematical formulations highlight the role of complex transformations and linear algebraic structures in the data alignment and calibration process. Furthermore, we analyze methods for mitigating sensor uncertainties and propose strategies to handle data overload and synchronization issues under real-time constraints. We present approaches for robust machine learning model design, where domain adaptation and multi-task learning methods enable flexible perception pipelines that generalize to diverse traffic and weather conditions. Ultimately, we identify open research directions and highlight the significance of scalable, secure, and adaptive data management in propelling autonomous vehicle perception forward.