Description: 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
Review Quotes:
"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