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Distributed autonomous intersection management with neuro-evolution
The sudden surge in computational power available to computer research in industry and academia has led to developments in AI automation. More and more tasks are able to be automated and replaced with machine learning systems. One such task that promises to be highly beneficial is that of driving, c...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2022
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| _version_ | 1867613227165155328 |
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| access_status_str | Open Access |
| author | Cherry, Matthew P |
| author2 | Nitschke, Geoff |
| author_browse | Cherry, Matthew P Nitschke, Geoff |
| author_facet | Nitschke, Geoff Cherry, Matthew P |
| author_sort | Cherry, Matthew P |
| collection | Thesis |
| description | The sudden surge in computational power available to computer research in industry and academia has led to developments in AI automation. More and more tasks are able to be automated and replaced with machine learning systems. One such task that promises to be highly beneficial is that of driving, clearly indicated by the amount of resources being spent by companies such as Uber, Google and Tesla. Neuro-Evolution has shown promise in the field of controller development, due to its ability to develop complex behaviour without a need for any labelled training data. It has been applied previously in car controller generation, across many fields. This thesis aims to apply Neuro-Evolution specifically to the field of intersection management, in order to study which methods are the most effective for this particular task. In particular we investigate three key hyper-parameters: Neuro-Evolution algorithm, task difficulty and problem exposure. A traffic simulator was developed and the hyper-parameters were used to evolve car controllers, which where then tested on unseen tasks. We show that certain key combinations of hyper-parameters yield exceptional results, but that direct correlations between individual parameters and performance are unclear, indicating that these methods are highly sensitive to hyper-parameter selection. We further identify some areas in which to optimize the evolution method, by looking at hyper-parameters which have a computational cost but which did not produce better performance. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/35637 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:47.627Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/35637 Distributed autonomous intersection management with neuro-evolution Cherry, Matthew P Nitschke, Geoff Computer Science The sudden surge in computational power available to computer research in industry and academia has led to developments in AI automation. More and more tasks are able to be automated and replaced with machine learning systems. One such task that promises to be highly beneficial is that of driving, clearly indicated by the amount of resources being spent by companies such as Uber, Google and Tesla. Neuro-Evolution has shown promise in the field of controller development, due to its ability to develop complex behaviour without a need for any labelled training data. It has been applied previously in car controller generation, across many fields. This thesis aims to apply Neuro-Evolution specifically to the field of intersection management, in order to study which methods are the most effective for this particular task. In particular we investigate three key hyper-parameters: Neuro-Evolution algorithm, task difficulty and problem exposure. A traffic simulator was developed and the hyper-parameters were used to evolve car controllers, which where then tested on unseen tasks. We show that certain key combinations of hyper-parameters yield exceptional results, but that direct correlations between individual parameters and performance are unclear, indicating that these methods are highly sensitive to hyper-parameter selection. We further identify some areas in which to optimize the evolution method, by looking at hyper-parameters which have a computational cost but which did not produce better performance. 2022-02-03T08:40:39Z 2022-02-03T08:40:39Z 2021 2022-02-01T09:37:35Z Master Thesis Masters MSc http://hdl.handle.net/11427/35637 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Computer Science Cherry, Matthew P Distributed autonomous intersection management with neuro-evolution |
| thesis_degree_str | Master's |
| title | Distributed autonomous intersection management with neuro-evolution |
| title_full | Distributed autonomous intersection management with neuro-evolution |
| title_fullStr | Distributed autonomous intersection management with neuro-evolution |
| title_full_unstemmed | Distributed autonomous intersection management with neuro-evolution |
| title_short | Distributed autonomous intersection management with neuro-evolution |
| title_sort | distributed autonomous intersection management with neuro evolution |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/35637 |
| work_keys_str_mv | AT cherrymatthewp distributedautonomousintersectionmanagementwithneuroevolution |