  Vehicle re-identification (ReID) endeavors to associate vehicle images
collected from a distributed network of cameras spanning diverse traffic
environments. This task assumes paramount importance within the spectrum of
vehicle-centric technologies, playing a pivotal role in deploying Intelligent
Transportation Systems (ITS) and advancing smart city initiatives. Rapid
advancements in deep learning have significantly propelled the evolution of
vehicle ReID technologies in recent years. Consequently, undertaking a
comprehensive survey of methodologies centered on deep learning for vehicle
re-identification has become imperative and inescapable. This paper extensively
explores deep learning techniques applied to vehicle ReID. It outlines the
categorization of these methods, encompassing supervised and unsupervised
approaches, delves into existing research within these categories, introduces
datasets and evaluation criteria, and delineates forthcoming challenges and
potential research directions. This comprehensive assessment examines the
landscape of deep learning in vehicle ReID and establishes a foundation and
starting point for future works. It aims to serve as a complete reference by
highlighting challenges and emerging trends, fostering advancements and
applications in vehicle ReID utilizing deep learning models.
