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Vehicle re-identification (ReID) endeavors to associate vehicle imagescollected from a distributed network of cameras spanning diverse trafficenvironments. This task assumes paramount importance within the spectrum ofvehicle-centric technologies, playing a pivotal role in deploying IntelligentTransportation Systems (ITS) and advancing smart city initiatives. Rapidadvancements in deep learning have significantly propelled the evolution ofvehicle ReID technologies in recent years. Consequently, undertaking acomprehensive survey of methodologies centered on deep learning for vehiclere-identification has become imperative and inescapable. This paper extensivelyexplores deep learning techniques applied to vehicle ReID. It outlines thecategorization of these methods, encompassing supervised and unsupervisedapproaches, delves into existing research within these categories, introducesdatasets and evaluation criteria, and delineates forthcoming challenges andpotential research directions. This comprehensive assessment examines thelandscape of deep learning in vehicle ReID and establishes a foundation andstarting point for future works. It aims to serve as a complete reference byhighlighting challenges and emerging trends, fostering advancements andapplications in vehicle ReID utilizing deep learning models.

A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges