@Article{cmc.2026.086072,
AUTHOR = {Inam Ullah, Zeeshan Ali Haider, Omar Almomani, Karamath Ateeq, Chang Choi},
TITLE = {Recent Advances in Artificial Intelligence for Smart Vehicles and Intelligent Transportation Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {89},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v89n2/68811},
ISSN = {1546-2226},
ABSTRACT = {The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such as the Internet of Things (IoT), edge computing, and cloud computing, in this instance, to create real-time distributed intelligence in transportation networks. Also, the application fields of autonomous driving, intelligent traffic control, Advanced Driver Assistance Systems (ADAS), intelligent parking, and Vehicle-to-Everything (V2X) communication are covered. The survey showed that other key challenges stemming from data heterogeneity, scale, latency, security, privacy, and model interpretability would need to be resolved for stable deployment. Lastly, AI-fueled Smart Cities, 5G/6G-powered transportation, Digital Twins, and Explainable Artificial Intelligence (XAI) are briefly mentioned as future research directions and novelties. This survey offers a comprehensive overview of AI-based transportation systems and will be of interest to researchers in the field.},
DOI = {10.32604/cmc.2026.086072}
}