
Predicting Vehicle Trajectory
Description
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Reviews / Votes
"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
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Persons
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.
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