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Vision-based real-time traffic control using artificial neural network on general-purpose embedded hardware

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dc.contributor.advisor Munasinghe R Zoysa HKG 2020 2020 2020
dc.description.abstract In urban cities, tra c management of intersections is a substantially challenging prob- lem. In appropriate tra c control leads to waste of fuel, time, and productivity of nations. Though the tra c signals are used to control tra c, it often causes problems due to the pre-programmed timing being not appropriate for the actual tra c intensity at the intersection. Tra c intensity determination based on statistical methods only gives the average intensities expected at any given time. However, to control tra c e ectively, the knowledge of real-time tra c intensity is a must-have. In this project, vision-based technology and arti cial intelligence (AI) are used to estimate tra c in real-time and control the tra c in order to reduce the tra c congestion. General -purpose electronic hardware has been used for in-situ image processing based on edge- detection methods. A Neural Network (NN) was trained to infer tra c intensity in each image in real-time using a scale of 1(very low) to 5 (very high). A Trained AI unit, which takes approximately 4 seconds to process each image and estimate tra c inten- sity was tested on the road where it recorded a 90% acceptance rate. In order to control the tra c, a ratio-based method and a reinforcement learning (RL)-based method was used. The performance of these methods are compared with a pre-programmed tra c controller. en_US
dc.language.iso en en_US
dc.subject ELECTRONICS AND AUTOMATION-Dissertations en_US
dc.subject ARTIFICIAL NEURAL NETWORK - Traffic Control en_US
dc.subject TRAFFIC SENSING en_US
dc.subject NEURAL NETWORK en_US
dc.title Vision-based real-time traffic control using artificial neural network on general-purpose embedded hardware en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US MSc in Electronics and Automation en_US
dc.identifier.department Department of Electrionic and Telecommunication Engineering en_US 2020
dc.identifier.accno TH4431 en_US

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