Term of Award
Spring 2023
Degree Name
Master of Science, Computer Science (M.S.C.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Computer Science
Committee Chair
Ryan Florin
Committee Member 1
Ryan Florin
Committee Member 2
Andrew Allen
Committee Member 3
Lixin Li
Committee Member 3 Email
lli@georgiasouthern.edu
Abstract
As vehicles become more modern, a large majority of vehicles on the road will have the required sensors to smoothly interact with other vehicles and infrastructure on the road. There will be many benefits of this new connectivity between vehicles on the road but one of the most profound improvements will be in the area of road accident prevention. Vehicles will be able to share information vital to road safety to oncoming vehicles and vehicles that are occluded so they do not have a direct line of sight to see a pedestrian or another vehicle on the road.
Another advantage of these modern connected vehicles is that different traffic parameters can be more easily estimated using the onboard sensors and technologies in the vehicles. For many decades traffic engineers have been able to estimate different traffic parameters like traffic flow, density, and velocity based on how many vehicles the primary vehicle passes and how many vehicles pass the primary vehicles. For much of the time that traffic engineers have been working on traffic estimation, it has been done using more manual and tedious methods. In this paper, a more novel approach of determining these traffic parameters is used.
Also, one of the problems with traffic parameter estimation is that sometimes the results are not accurate because of vehicles that might not have been counted because of occlusion. In this paper, a proposal is put forward on how this can be remedied utilizing the connected vehicle's framework.
Recommended Citation
Jegede, Temitope D., "A Camera-Only Based Approach to Traffic Parameter Estimation Using Mobile Observer Methods" (2023). Electronic Theses and Dissertations. 2570.
https://digitalcommons.georgiasouthern.edu/etd/2570
Research Data and Supplementary Material
No
