A review of signalized intersection control technologies targeted at autonomous driving within intelligent transportation systems

This article presents a systematic review of signalized intersection control technologies in the context of autonomous driving within intelligent transportation systems. As the prevalence of connected and autonomous vehicles (CAVs) increases, traditional traffic signal systems are undergoing a paradigm shift toward more intelligent, cooperative, and adaptive control architectures. To better contextualize this transformation, the review adopts a conceptual framework that views signal control not merely as phase timing optimization, but as an integrated system involving control logic, bidirectional information exchange and vehicle-side behavioral adaptation. This study employs bibliometric methods to analyze control models for signalized intersections within autonomous driving contexts, with particular emphasis on strategies applicable to real-world mixed traffic conditions, including scenarios with partial penetration of autonomous vehicles. The analysis is grounded in literature retrieved from Web of Science, Scopus, and PubMed, and is complemented by co-word visualization using VOSviewer and CiteSpace. The review synthesizes insights across four major domains: classical phase-based strategies, communication technology, platooning control, and artificial intelligence. In addition to summarizing the existing research progress, this review underscores several enduring challenges in the field, including data sparsity, insufficient scalability, and communication latency. It further outlines promising future research directions covering lightweight artificial intelligence models, cloud-edge collaborative architectures, and human-centered multimodal interaction modeling. These findings provide a system-level reference for developing safer, more efficient, and more sustainable intelligent transportation systems.