Hybrid Control Approaches for Autonomous Transport Systems

Abstract

Autonomous transport systems are poised to revolutionize the future of mobility, offering solutions for reducing traffic congestion, improving safety, and enhancing energy efficiency. One of the critical challenges in the development of autonomous vehicles and transport networks is ensuring reliable, adaptive, and efficient control mechanisms. Hybrid control approaches, combining elements of classical control techniques with modern machine learning and artificial intelligence (AI), have emerged as a promising solution. This article explores the role of hybrid control approaches in autonomous transport systems, examining how they integrate traditional control methodologies with adaptive learning techniques to improve the robustness and performance of autonomous systems. The article also discusses key applications, challenges, and opportunities associated with hybrid control approaches, with a focus on real-time decision-making, path planning, and multi-agent coordination in complex environments.