Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data
dc.contributor.author | Breg, Alexander Noel | |
dc.contributor.advisor | Murphey, Yi Lu | |
dc.date.accessioned | 2019-01-15T14:11:18Z | |
dc.date.available | NO_RESTRICTION | en_US |
dc.date.available | 2019-01-15T14:11:18Z | |
dc.date.issued | 2018-12-15 | |
dc.date.submitted | 2018-08-30 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146791 | |
dc.description.abstract | This paper presents a neural network developed for predicting the path of a remote vehicle using post facto created vehicle-to-vehicle (V2V) data and uses that prediction to determine whether it is safe for the host vehicle to change lanes. The data was collected in a 2013 experiment involving various drivers traveling on public roads in Ann Arbor, MI. The trips were on suburban roads, city roads and divided highways over a two-day period. The vehicular satellite global positioning system (GPS) data from movement over this period was gathered and post-processed to find vehicle paths within 10 meters of one another. The path traces of the two vehicles were combined to simulate what a V2V network would have provided to properly equipped vehicles if such a network and vehicles existed on real road networks demonstrating natural driving behavior. This research harnesses this data to determine the increased effectiveness of a neural network predicting the future path of remote vehicles and lane change safety when a V2V network is available. The most studied maneuver is overtaking. To a lesser extent, this paper also provides a view into how a neural network predicts remote vehicle behaviors using a host vehicle equipped with only perceptive hardware and no given information from the remote vehicle. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Autonomous vehicle | en_US |
dc.subject | Path planning | en_US |
dc.subject | Path prediction | en_US |
dc.subject | Neural network | en_US |
dc.subject | Vehicle-to-vehicle | en_US |
dc.subject.other | Electrical engineering | en_US |
dc.title | Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data | en_US |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | Master of Science in Engineering (MSE) | en_US |
dc.description.thesisdegreediscipline | Electrical Engineering, College of Engineering & Computer Science | en_US |
dc.description.thesisdegreegrantor | University of Michigan-Dearborn | en_US |
dc.contributor.committeemember | Watta, Paul | |
dc.identifier.uniqname | 7519-6570 | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146791/1/49698122_breg_thesis_embedded (1).pdf | |
dc.identifier.orcid | 0000-0002-2152-8117 | en_US |
dc.identifier.name-orcid | Breg, Alexander; 0000-0002-2152-8117 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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