Autonomous Vehicles and Systems
A Technological and Societal Perspective
1st Edition
Published on 6. February 2024
464 pages
978-1-003-81070-4 (ISBN)
Description
This book captures multidisciplinary research encompassing various facets of autonomous vehicle systems (AVS) research and developments. The AVS field is rapidly moving towards realization with numerous advances continually reported. The contributions to this field come from widely varying branches of knowledge, making it a truly multidisciplinary area of research and development. The topics covered in the book include:
AI and deep learning for AVS
Autonomous steering through deep neural networks
Adversarial attacks and defenses on autonomous vehicles
Gesture recognition for vehicle control
Multi-sensor fusion in autonomous vehicles
Teleoperation technologies for AVS
Simulation and game theoretic decision making for AVS
Path following control system design for AVS
Hybrid cloud and edge solutions for AVS
Ethics of AVS
AI and deep learning for AVS
Autonomous steering through deep neural networks
Adversarial attacks and defenses on autonomous vehicles
Gesture recognition for vehicle control
Multi-sensor fusion in autonomous vehicles
Teleoperation technologies for AVS
Simulation and game theoretic decision making for AVS
Path following control system design for AVS
Hybrid cloud and edge solutions for AVS
Ethics of AVS
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ebooks
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
Illustrations
39 Tables, black and white; 64 Line drawings, color; 20 Line drawings, black and white; 75 Halftones, color; 3 Halftones, black and white; 139 Illustrations, color; 23 Illustrations, black and white
ISBN-13
978-1-003-81070-4 (9781003810704)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
02/2024
1st Edition
River Publishers
€194.00
Article not available for order
Person
Ishwar K. Sethi is currently a professor in the Department of Computer Science and Engineering at Oakland University in Rochester, Michigan. He has over 45 years of experience in applying neural networks including deep learning, machine learning, pattern recognition, and image, video, and text analytics to a broad range of projects. He has authored or coauthored over 175 journal and conference articles and has graduated 25 doctoral students. He has served on the editorial boards of several prominent journals including IEEE Trans. Pattern Analysis and Machine Intelligence, and IEEE Multimedia. He was elected IEEE Fellow in 2001 for his contributions in artificial neural networks and statistical pattern recognition and achieved the status of Life Fellow in 2012. He most recently organized the 20th International Conference on Machine Learning and Applications (ICMLA) in December 2021.
Content
1. Introduction 2. Deep Learning for Autonomous Vehicles and Systems 3. Towards Autonomous Vehicles and Systems - Potentials and Challenges of Artificial Intelligence 4. Autonomous Vehicle Steering through Convolutional and Recurrent Deep Learning 5. 2D and 3D Pose Estimation for Gesture Recognition in Deep-Learning-Driven Human-Vehicle Leader-Follower Systems 6. Adversarial Attacks and Defenses on Autonomous Vehicle Systems 7. Multi-sensor Fusion for Multi-target Detection and Tracking 8. Teleoperation Technologies for Enhancing Connected and Autonomous Vehicles 9. Hybrid Edge/cloud Solutions for Supporting Autonomous Vehicles 10. Game-Theoretic Decision Making for Autonomous Driving Vehicles 11. State of Modeling and Simulation in Autonomous Vehicles 12. Path Following Control System Design and Analysis for Autonomous Vehicles 13. Public Transport Travel Time Prediction using Sequential Forward Floating Selection Algorithm and Stacked Autoencoder 14. A Look into the Ethics of Autonomous Vehicles Systems (AVS)
