Internet of Things and Artificial Intelligence in Transportation Revolution
Lytras, Miltiadis
Internet of Things and Artificial Intelligence in Transportation Revolution - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (232 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-0311-0 9783036503103 9783036503110
10.3390/books978-3-0365-0311-0 doi
History of engineering & technology
decision-making autonomous navigation collision avoidance scene division deep reinforcement learning maritime autonomous surface ships internet of things crowdsourcing indoor localization data fusion security authentication Inertial Measurement Units road transportation traffic signal control speed guidance vehicle arrival time connected vehicle unmanned ships DDPG autonomous path planning end-to-end at-risk driving deep support vector machine driver drowsiness driver stress multi-objective genetic algorithm multiple kernel learning urban freeway hybrid dynamic system state transition unknown inputs observer vehicle density maritime vessel flows intelligent transportation systems deep learning automatic license plate recognition intelligent vehicle access histogram of oriented gradients artificial neural networks convolutional neural networks time-frequency Inertial Measurement Unit (IMU) road anomalies n/a
Internet of Things and Artificial Intelligence in Transportation Revolution - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (232 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-0311-0 9783036503103 9783036503110
10.3390/books978-3-0365-0311-0 doi
History of engineering & technology
decision-making autonomous navigation collision avoidance scene division deep reinforcement learning maritime autonomous surface ships internet of things crowdsourcing indoor localization data fusion security authentication Inertial Measurement Units road transportation traffic signal control speed guidance vehicle arrival time connected vehicle unmanned ships DDPG autonomous path planning end-to-end at-risk driving deep support vector machine driver drowsiness driver stress multi-objective genetic algorithm multiple kernel learning urban freeway hybrid dynamic system state transition unknown inputs observer vehicle density maritime vessel flows intelligent transportation systems deep learning automatic license plate recognition intelligent vehicle access histogram of oriented gradients artificial neural networks convolutional neural networks time-frequency Inertial Measurement Unit (IMU) road anomalies n/a