Artificial intelligence (AI) is a concept in which entities and systems have the ability to learning and decision-making by imitating biological processes. In this chapter, we first introduce the evolution of AI to give the reader a good grasp of artificial intelligence (AI) and then introduce the existing machine learning (ML) with three typical classifications: unsupervised learning, supervised learning, and reinforcement learning. Supervised learning makes decision based on the output labels provided in training. Unsupervised learning works based on pattern discovery without having the pre-knowledge of output labels. The third machine learning paradigm is reinforcement learning (RL), which takes sequential actions rooted in Markov Decision Process (MDP) with a rewarding or penalizing criterion. Some other kinds of ML algorithms such as federated learning and transfer learning are also introduced in the first subsection. The future transportation network aims to develop a highly dynamic and intelligent system, which enables the networks to change the environment to satisfy various requirements and service types. Cellular-V2X, vehicular edge network, and unmanned aerial vehicle (UAV) are recently attacking network architecture to enable the future transportation. In the second section, we introduce how to integrate AI into cellular-V2X, vehicular edge network, and UAV. Leveraging AI into transportation helps the sector increase passenger safety, reduce traffic congestion and accidents, lessen carbon emissions, and also minimize the overall financial expenses. Finally, we review some existing research that uses AI to enable autonomous driving, traffic control and prediction, and path planning.
Artificial Intelligence: Need, Evolution, and Applications for Transportation Systems
Internet of Things: Tech., Communicat., Computing
Intelligent Cyber-Physical Systems for Autonomous Transportation ; Kapitel : 5 ; 71-88
2021-12-15
18 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Machine learning , Artificial intelligence , Unmanned aerial vehicle , Reinforcement learning , Supervised learning , Semi-supervised learning , Meta-learning , Unsupervised learning , Markov decision process , Vehicular edge network , Future transportation network , Federated learning , Transfer learning , Vehicular ad hoc networks , Vehicles to anything Engineering , Cyber-physical systems, IoT , Communications Engineering, Networks , Automotive Engineering , Computer Science
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