Affiliations:
Guilin University of Electronic Technology, School of Computer Engineering, Beihai, China
Correspondence:
[*]
Corresponding author. Xiaohai Chen, Guilin University of Electronic Technology, School of Computer Engineering, Beihai 536000, China. E-mail: chenxhai2009@163.com.
Abstract: Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field.
Keywords: Transportation systems, ITS, vehicle type, deep learning, optimization
