A Roadside Unit for Infrastructure Assisted Intersection Control of Autonomous Vehicles
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910856277655552 |
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| author | Evans, Michael Machado, Marcial Johnson, Rickey Vadella, Anna Escamilla, Luis Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Jostes, Milan Butani, Devson Kaddis, Ryan Chung, Chan-Jin Siegel, Joshua |
| author_facet | Evans, Michael Machado, Marcial Johnson, Rickey Vadella, Anna Escamilla, Luis Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Jostes, Milan Butani, Devson Kaddis, Ryan Chung, Chan-Jin Siegel, Joshua |
| contents | Recent advances in autonomous vehicle technologies and cellular network speeds motivate developments in vehicle-to-everything (V2X) communications. Enhanced road safety features and improved fuel efficiency are some of the motivations behind V2X for future transportation systems. Adaptive intersection control systems have considerable potential to achieve these goals by minimizing idle times and predicting short-term future traffic conditions. Integrating V2X into traffic management systems introduces the infrastructure necessary to make roads safer for all users and initiates the shift towards more intelligent and connected cities. To demonstrate our control algorithm, we implement both a simulated and real-world representation of a 4-way intersection and crosswalk scenario with 2 self-driving electric vehicles, a roadside unit (RSU), and a traffic light. Our architecture reduces acceleration and braking through intersections by up to 75.35%, which has been shown to minimize fuel consumption in gas vehicles. We propose a cost-effective solution to intelligent and connected intersection control to serve as a proof-of-concept model suitable as the basis for continued research and development. Code for this project is available at https://github.com/MMachado05/REU-2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Roadside Unit for Infrastructure Assisted Intersection Control of Autonomous Vehicles Evans, Michael Machado, Marcial Johnson, Rickey Vadella, Anna Escamilla, Luis Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Jostes, Milan Butani, Devson Kaddis, Ryan Chung, Chan-Jin Siegel, Joshua Robotics Recent advances in autonomous vehicle technologies and cellular network speeds motivate developments in vehicle-to-everything (V2X) communications. Enhanced road safety features and improved fuel efficiency are some of the motivations behind V2X for future transportation systems. Adaptive intersection control systems have considerable potential to achieve these goals by minimizing idle times and predicting short-term future traffic conditions. Integrating V2X into traffic management systems introduces the infrastructure necessary to make roads safer for all users and initiates the shift towards more intelligent and connected cities. To demonstrate our control algorithm, we implement both a simulated and real-world representation of a 4-way intersection and crosswalk scenario with 2 self-driving electric vehicles, a roadside unit (RSU), and a traffic light. Our architecture reduces acceleration and braking through intersections by up to 75.35%, which has been shown to minimize fuel consumption in gas vehicles. We propose a cost-effective solution to intelligent and connected intersection control to serve as a proof-of-concept model suitable as the basis for continued research and development. Code for this project is available at https://github.com/MMachado05/REU-2024. |
| title | A Roadside Unit for Infrastructure Assisted Intersection Control of Autonomous Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.00866 |