Cesar Barrios, Yuichi Motai

Predicting Vehicle Trajectory (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 path intersections of more than one vehicle at the same time. The authors evaluate the use of smartphones' built-in sensors to predict a vehicle's trajectory as a possible intermediate solution for a V2V and V2I communication, until all vehicles have all the necessary sensors and communication infrastructure to fully populate this system.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 204
• Erscheinungstermin: 3. März 2017
• Englisch
• ISBN-13: 9781138031623
• Artikelnr.: 47899534

Autorenporträt

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.

Inhaltsangabe

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.