Control Strategies in Autonomous Vehicle Path Tracking: A Comprehensive Review

Abstract

Autonomous vehicle path tracking is a critical aspect of the overall control system of a vehicle. This review paper provides a comprehensive examination of the sophisticated control strategies used for autonomous vehicle path tracking. The paper categorizes the control strategies into three main types: model-based, learning-based, and hybrid approaches. Each category is analysed for its strengths, weaknesses, and application contexts. Hybrid strategies prove to be the best approach of the three as they combine the strengths of both model and learning-based strategies, providing a balanced approach that leverages the advantages of each method. The review aims to highlight current research trends, recognise gaps in the existing works, and recommend directions for future study.