Trajectory tracking control for autonomous vehicles: A systematic PRISMA review of models and strategies

Autonomous vehicles (AVs) are poised to redefine future mobility by offering enhanced safety, energy efficiency, and intelligent adaptability. A fundamental component enabling this transformation is trajectory tracking control, which ensures precise path-following despite environmental uncertainties, dynamic road conditions, and sensor noise. This systematic review follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to analyze state-of-the-art trajectory tracking control strategies, categorizing them into traditional, adaptive, and learning-based methods. The study provides a comprehensive assessment of trajectory tracking models, highlighting their strengths, limitations, and applicability in real-world scenarios. Additionally, the review discusses key challenges, such as scalability, real-time adaptability, and the integration of multi-sensor data. By bridging theoretical advancements with practical implementations, this review contributes to the development of more robust, adaptive, and efficient trajectory tracking systems for autonomous mobility. • Systematic PRISMA review of trajectory tracking control in autonomous vehicles. • Trajectory control methods categorized into rule-based, adaptive, and learning-based. • Detailed analysis of vehicle models for controller design and real-world use. • Hybrid and ML-based approaches improve adaptability in dynamic environments. • Key challenges include real-time learning, sensor fusion, and scalability.