Bicycle Crash Detection: Using a Voice-Assistant for More Accurate Reporting

Simple item page
dc.contributor.advisorFickas, Stephen
dc.contributor.authorWilliams, Brian
dc.date.accessioned2018-09-06T21:57:44Z
dc.date.available2018-09-06T21:57:44Z
dc.date.issued2018-09-06
dc.description.abstractIt is estimated that over half of bicycle crashes are not reported. There are various reasons for this, such as no property damage or physical injuries sustained. In order to improve the likelihood that bicycle riders will report a crash, I have developed Urban Bike Buddy, a smartphone application which uses the internal sensors of the device to detect a crash. The application interacts with Alexa to help guide the user through the crash reporting process. The innovative features of my work are the ability to initiate communication with Alexa without user interaction. In addition, there is an intersection controller that has been connected to extra hardware that allows bicycle riders to request a crossing signal during their approach based on the speed that they are riding. These features add value to bicycle riders, and will help contribute to a safer environment for bicycle riders, automobiles, and pedestrians as well.en_US
dc.identifier.urihttps://hdl.handle.net/1794/23760
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsAll Rights Reserved.
dc.subjectbicycleen_US
dc.subjectcrash detectionen_US
dc.subjectcrash reportingen_US
dc.subjectvoice assistantsen_US
dc.titleBicycle Crash Detection: Using a Voice-Assistant for More Accurate Reporting
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Williams_oregon_0171N_12185.pdf
Size:
5.19 MB
Format:
Adobe Portable Document Format
Download

Collections

Theses and Dissertations
Computer Science Theses and Dissertations