This book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Having an accurate prediction of where a vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. The US DOT will be mandating that all vehicle manufacturers begin implementing V2V and V2I systems, so very soon collision avoidance systems will no longer rely on line of sight sensors, but instead will be able to take into account another vehicle’s spatial movements to determine if the future trajectories of the vehicles will intersect at the same time. Furthermore, the book introduces the reader to some improvements when predicting the future trajectory of a vehicle and presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems. Additionally, it evaluates whether smartphones can be used for trajectory predictions, in an attempt to populate a V2V collision avoidance system faster than a vehicle manufacturer can.
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.
