Predicting Vehicle Trajectory
Autor Cesar Barrios, Yuichi Motaien Limba Engleză Paperback – 30 sep 2020
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Specificații
ISBN-13: 9780367656348
ISBN-10: 0367656345
Pagini: 204
Dimensiuni: 156 x 234 x 13 mm
Greutate: 0.38 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
ISBN-10: 0367656345
Pagini: 204
Dimensiuni: 156 x 234 x 13 mm
Greutate: 0.38 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
Public țintă
Professional Practice & DevelopmentCuprins
Preface. Improving Estimation of Vehicle’s Trajectory Using Latest Global Positioning System with Kalman Filtering. Intelligent Forecasting Using Dead Reckoning with Dynamic Errors. Trajectory Estimations Using Smartphones. Summary of Vehicle Trajectories’ Prediction Methods Evaluated. Conclusions. Appendix.
Notă biografică
Cesar Barrios received a B.S. (1999) and an M.S. (2001) in electrical engineering from the New Jersey Institute of Technology, and a Ph.D. degree (2014) in electrical engineering from the University of Vermont. He worked for IBM after graduating with his B.S. degree in 1999, and since 2015 he has been working for GLOBALFOUNDRIES. He began in the Information Technology field and has since moved into Semiconductor Research and Development.
Yuichi Motai received his B.Eng. degree in instrumentation engineering from Keio University, Tokyo, Japan, in 1991, his M.Eng. degree in applied systems science from Kyoto University, Kyoto, Japan, in 1993, and his Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, IN, U.S.A., in 2002. He is currently an Associate Professor of Electrical and Computer Engineering at Virginia Commonwealth University, Richmond, VA, USA. His research interests include the broad area of sensory intelligence (particularly in intelligent vehicle), pattern recognition, computer vision, and sensory-based robotics.
Recenzii
"I found specifically very important in this book the research conducted by the authors to properly handle error accumulation from missing data from offline sensors, and running the system at the fastest rate possible greatly reducing the prediction errors in non-straight paths (which are the harder task to predict). Moreover, I found very interesting the idea of using every-day equipment (smartphones) as a temporary (yet effective) solution to enable older vehicles to V2V and V2I technologies. It is also very important that the evaluation revealed that, in some cases, the smartphone prediction errors are similar to more expensive sensors in V2I. This book addresses solutions specifically for improved trajectory prediction in traffic networks. There is no question that this short book may be a valuable handbook for engineers, especially those who work on the specific problem trying to engage as much as possible users/vehicles in the V2V or V2I ecosystem."
—IEEE Intelligent Transportation Systems Magazine, Fall 2017
—IEEE Intelligent Transportation Systems Magazine, Fall 2017
Descriere
Current vehicular systems are mostly based on line of sight sensors used to prevent a collision. The book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths, by having an accurate prediction of where the vehicle is heading. This is crucial for the system to reliably determine possible pa