Object classification in road traffic using machine learning: state-of-the-art approaches and future directions

Abstract

The rapid growth of urban populations and the increasing complexity of transportation networks have amplified the demand for intelligent traffic management systems. Among the critical components of such systems is the accurate and efficient classification of objects in road traffic environments, including vehicles, pedestrians, cyclists, and various roadside entities. The ability to reliably identify and distinguish these objects is fundamental for applications such as autonomous driving, traffic monitoring, infrastructure planning, and road safety enhancement.