Reinforcement Learning for Reducing Congestion in Mixed Autonomy Highways

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Maheshwari, Aditya




This thesis focuses on assessing the impact of AVs on improving traffic flow in mixed autonomy collision-free highways. Roads were simulated using micro-traffic models, with adjustments designed to simulate a future world in which Automatic Braking Systems will be good enough to completely prevent rear-end and lane-change collisions on highways. We combined Deep Neural Networks with Reinforcement Learning (Deep Q Networks) to model the AVs, and this approach was tested on single and double lane highways. Among many other salient issues, our results show that replacing only 5% of human-drivers with AVs can, remarkably, improve average road speeds by up to 10% on single lane roads, up to 24% on double lane roads, and also significantly reduce the percentage of fully-stopped cars on average, for most situations. Furthermore, these trends, for the most part, continue to amplify as higher percentages of the human drivers are replaced with AVs.


Computer Science
Artificial Intelligence




Carleton University

Thesis Degree Name: 

Master of Computer Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).