Internet of Things and Artificial Intelligence in Transportation Revolution

• Autore: Lytras Miltiadis
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (232 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: artificial neural networks; at-risk driving; authentication; automatic license plate recognition; autonomous navigation; autonomous path planning; collision avoidance; connected vehicle; convolutional neural networks; crowdsourcing; data fusion; DDPG; decision-making; deep learning; deep reinforcement learning; deep support vector machine; driver drowsiness; driver stress; end-to-end; histogram of oriented gradients; hybrid dynamic system; indoor localization; Inertial Measurement Unit (IMU); Inertial Measurement Units; intelligent transportation systems; intelligent vehicle access; internet of things; maritime autonomous surface ships; maritime vessel flows; multi-objective genetic algorithm; multiple kernel learning; n/a; road anomalies; road transportation; scene division; security; speed guidance; state transition; time-frequency; traffic signal control; unknown inputs observer; unmanned ships; urban freeway; vehicle arrival time; vehicle density
• Persona (resp. second.): ChuiKwok Tai; LiuRyan Wen; LytrasMiltiadis
• Sommario/riassunto: The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.
• Titolo autorizzato: Internet of Things and Artificial Intelligence in Transportation Revolution
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557134703321
• Lo trovi qui: Univ. Federico II