The global vehicle fleet has grown rapidly over the past decade, impacting the way traffic is to be managed. Vehicle traffic management and control through technology is a well-known and widely studied problem that continues to present challenges and opportunities for action, mainly due to the growing demand, the mentioned increase in the vehicle fleet, and inefficiency of current systems, generally based on fixed-time traffic lights. Solutions have been presented for this scenario, and among them, Artificial Intelligence (AI) and Machine Learning (ML) techniques have stood out. The AI/ML field, however, is vast and varied. This article proposes a survey of the most used AI/ML techniques in the management of vehicular traffic lights, and it does so through a Systematic Mapping Review (SMR), pointing out models that receive greater focus, research trends and gaps.


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