Reinforcement Learning for Reducing Congestion in Mixed Autonomy Highways

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  • 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.

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  • Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2020


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