Abstract
Intelligent transportation systems (ITS) are crucial for modern transportation infrastructure. These systems, powered by advanced technologies, are the key to optimizing traffic flow, enhancing safety, and reducing environmental impact. Among the many components of ITS, vehicle classification stands out as a crucial element. By accurately classifying vehicles based on their size, type, and behavior, tailored strategies for traffic control, lane assignment, and toll collection can be developed. With the advent of artificial intelligence (AI), new opportunities have emerged to enhance the performance of vehicle classification systems. This chapter aims to provide an overview of recent advances, current challenges, and future perspectives in vehicle classification technologies within ITS. It delves into implementing AI techniques in vehicle classification systems, analyzing their effectiveness, limitations, and potential applications. The conclusion highlights the need for robust algorithms, data diversity, and secure communication protocols. It also explores the need for explainable AI to enhance trust in classification decisions and integration with other ITS components for a comprehensive transportation ecosystem. Recent developments in deep learning techniques have led to high accuracy in vehicle classification. However, the complexities of traffic environments, diverse weather conditions, and privacy concerns pose significant challenges that need attention. Nevertheless, the future of vehicle classification for ITS is promising, with the potential to build more intelligent and efficient transportation systems, thereby contributing to a safer and more sustainable future.