Embedded System Architectures for Autonomous Vehicle Navigation and Control

Abstract

Autonomous vehicles are revolutionizing transportation, relying on specialized embedded system architectures to achieve precise navigation and control. This article provides a detailed exploration of embedded system designs tailored for autonomous vehicle applications. It examines the integration of sensors, processing units, and control mechanisms crucial for making real-time decisions and navigating complex environments effectively. The discussion covers various navigation algorithms and their implementation on embedded platforms to ensure accurate positioning and efficient path planning. Additionally, the article explores control systems that manage vehicle dynamics and interactions with the surroundings, emphasizing their responsiveness and real-time processing capabilities. It also addresses the integration and fusion of sensor data from multiple sources like cameras, LiDAR, radar, and IMUs within embedded architectures, aiming to enhance reliability and robustness in autonomous operations. The review concludes with insights into challenges such as computational constraints, energy efficiency, and safety considerations, and discusses future trends including AI-driven navigation advancements and regulatory frameworks influencing autonomous vehicle deployment.