A Vehicle-collision Learning System Using Driving Patterns on the Road

Description:

Demand of automobiles are significantly growing despite various factors, steadily increasing the average number of vehicles on the road. Increase in the number of vehicles, subsequently increases the risk of collisions, characterized by the driving behavior. Driving behavior is influenced by factors like class of vehicle, road condition and vehicle maneuvering by the driver. Rapidly growing mobile technology and use of smartphones embedded with in-built sensors, provides scope of constant development of assistance systems considering the safety of the driver by integrating with the information obtained from the vehicle on-board sensors. Our research aims at learning a vehicle system comprising of vehicle, human and road by employing driving patterns obtained from the sensor data to develop better systems of safety and alerts altogether. The thesis focusses on utilizing together various data recorded by the in-built embedded sensors in a smartphone to understand the vehicle motion and dynamics, followed by studying various impacts of collision events, types and signatures which can potentially be integrated in a prototype framework to detect variations, alert drivers and emergency responders in an event of collision.

Creator(s): Urs, Chaitra Vijaygopalraj
Creation Date: August 2013
Partner(s):
UNT Libraries
Collection(s):
UNT Theses and Dissertations
Usage:
Total Uses: 61
Past 30 days: 11
Yesterday: 0
Creator (Author):
Publisher Info:
Publisher Name: University of North Texas
Publisher Info: www.unt.edu
Place of Publication: Denton, Texas
Date(s):
  • Creation: August 2013
Description:

Demand of automobiles are significantly growing despite various factors, steadily increasing the average number of vehicles on the road. Increase in the number of vehicles, subsequently increases the risk of collisions, characterized by the driving behavior. Driving behavior is influenced by factors like class of vehicle, road condition and vehicle maneuvering by the driver. Rapidly growing mobile technology and use of smartphones embedded with in-built sensors, provides scope of constant development of assistance systems considering the safety of the driver by integrating with the information obtained from the vehicle on-board sensors. Our research aims at learning a vehicle system comprising of vehicle, human and road by employing driving patterns obtained from the sensor data to develop better systems of safety and alerts altogether. The thesis focusses on utilizing together various data recorded by the in-built embedded sensors in a smartphone to understand the vehicle motion and dynamics, followed by studying various impacts of collision events, types and signatures which can potentially be integrated in a prototype framework to detect variations, alert drivers and emergency responders in an event of collision.

Degree:
Level: Master's
PublicationType: Thesi
Language(s):
Subject(s):
Keyword(s): Learning system | sensors | collisions | signatures | maneuvers
Contributor(s):
Partner:
UNT Libraries
Collection:
UNT Theses and Dissertations
Identifier:
  • ARK: ark:/67531/metadc283807
Resource Type: Thesis or Dissertation
Format: Text
Rights:
Access: Public
Holder: Urs, Chaitra Vijaygopalraj
License: Copyright
Statement: Copyright is held by the author, unless otherwise noted. All rights Reserved.