Multi-Agent Autonomous Intersection Management Systems
| dc.contributor.advisor | Brennan, Robert | |
| dc.contributor.author | Baradaran Amini, Sama | |
| dc.contributor.committeemember | Behjat, Laleh | |
| dc.contributor.committeemember | Li, Simon | |
| dc.date | 2021-11 | |
| dc.date.accessioned | 2021-06-21T21:42:52Z | |
| dc.date.available | 2021-06-21T21:42:52Z | |
| dc.date.issued | 2021-06-11 | |
| dc.description.abstract | With the advancements in the field of autonomous driving, it is important to plan safe and efficient ways to regulate traffic once self-driving vehicles actually take the road. One of the most important points in traffic regulation is the intersection management system as it is the single point with highest congestion and collision rates. Most of the previous research that has been performed in this field, suggest a single form of autonomous intersection management system, and compare results with the conventional light-signal system. Through these results, it is clear that the light-signal controlled intersections have no place in the world of autonomous driving. Therefore, in this research the effort has been made to evaluate multiple autonomous multi-agent intersection management systems and arrive at the most feasible and efficient model by comparing them to each other especially with a focus on agent communication metrics. This thesis presents three separate models for autonomous multi-agent intersection management, and moves towards evaluating performance of each system, separately, and compared to each other. The two centralized multi-agent systems provide a more secure and reliable method as they can drop collision rates to zero. However, a decentralized system can regulate traffic faster while requiring a more powerful IT infrastructure to handle large number of agent communications in the system. | en_US |
| dc.identifier.citation | Baradaran Amini, S. (2021). Multi-Agent Autonomous Intersection Management Systems (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/38942 | |
| dc.identifier.uri | http://hdl.handle.net/1880/113520 | |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject.classification | Sociology--Transportation | en_US |
| dc.subject.classification | Applied Mechanics | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Engineering--Automotive | en_US |
| dc.subject.classification | Robotics | en_US |
| dc.title | Multi-Agent Autonomous Intersection Management Systems | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Mechanical & Manufacturing | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |