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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.17043 |
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| _version_ | 1866914692878827520 |
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| author | Lee, Jonathan W. Wang, Han Jang, Kathy Hayat, Amaury Bunting, Matthew Alanqary, Arwa Barbour, William Fu, Zhe Gong, Xiaoqian Gunter, George Hornstein, Sharon Kreidieh, Abdul Rahman Lichtlé, Nathan Nice, Matthew W. Richardson, William A. Shah, Adit Vinitsky, Eugene Wu, Fangyu Xiang, Shengquan Almatrudi, Sulaiman Althukair, Fahd Bhadani, Rahul Carpio, Joy Chekroun, Raphael Cheng, Eric Chiri, Maria Teresa Chou, Fang-Chieh Delorenzo, Ryan Gibson, Marsalis Gloudemans, Derek Gollakota, Anish Ji, Junyi Keimer, Alexander Khoudari, Nour Mahmood, Malaika Mahmood, Mikail Matin, Hossein Nick Zinat Mcquade, Sean Ramadan, Rabie Urieli, Daniel Wang, Xia Wang, Yanbing Xu, Rita Yao, Mengsha You, Yiling Zachár, Gergely Zhao, Yibo Ameli, Mostafa Baig, Mirza Najamuddin Bhaskaran, Sarah Butts, Kenneth Gowda, Manasi Janssen, Caroline Lee, John Pedersen, Liam Wagner, Riley Zhang, Zimo Zhou, Chang Work, Daniel B. Seibold, Benjamin Sprinkle, Jonathan Piccoli, Benedetto Monache, Maria Laura Delle Bayen, Alexandre M. |
| author_facet | Lee, Jonathan W. Wang, Han Jang, Kathy Hayat, Amaury Bunting, Matthew Alanqary, Arwa Barbour, William Fu, Zhe Gong, Xiaoqian Gunter, George Hornstein, Sharon Kreidieh, Abdul Rahman Lichtlé, Nathan Nice, Matthew W. Richardson, William A. Shah, Adit Vinitsky, Eugene Wu, Fangyu Xiang, Shengquan Almatrudi, Sulaiman Althukair, Fahd Bhadani, Rahul Carpio, Joy Chekroun, Raphael Cheng, Eric Chiri, Maria Teresa Chou, Fang-Chieh Delorenzo, Ryan Gibson, Marsalis Gloudemans, Derek Gollakota, Anish Ji, Junyi Keimer, Alexander Khoudari, Nour Mahmood, Malaika Mahmood, Mikail Matin, Hossein Nick Zinat Mcquade, Sean Ramadan, Rabie Urieli, Daniel Wang, Xia Wang, Yanbing Xu, Rita Yao, Mengsha You, Yiling Zachár, Gergely Zhao, Yibo Ameli, Mostafa Baig, Mirza Najamuddin Bhaskaran, Sarah Butts, Kenneth Gowda, Manasi Janssen, Caroline Lee, John Pedersen, Liam Wagner, Riley Zhang, Zimo Zhou, Chang Work, Daniel B. Seibold, Benjamin Sprinkle, Jonathan Piccoli, Benedetto Monache, Maria Laura Delle Bayen, Alexandre M. |
| contents | The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17043 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs Lee, Jonathan W. Wang, Han Jang, Kathy Hayat, Amaury Bunting, Matthew Alanqary, Arwa Barbour, William Fu, Zhe Gong, Xiaoqian Gunter, George Hornstein, Sharon Kreidieh, Abdul Rahman Lichtlé, Nathan Nice, Matthew W. Richardson, William A. Shah, Adit Vinitsky, Eugene Wu, Fangyu Xiang, Shengquan Almatrudi, Sulaiman Althukair, Fahd Bhadani, Rahul Carpio, Joy Chekroun, Raphael Cheng, Eric Chiri, Maria Teresa Chou, Fang-Chieh Delorenzo, Ryan Gibson, Marsalis Gloudemans, Derek Gollakota, Anish Ji, Junyi Keimer, Alexander Khoudari, Nour Mahmood, Malaika Mahmood, Mikail Matin, Hossein Nick Zinat Mcquade, Sean Ramadan, Rabie Urieli, Daniel Wang, Xia Wang, Yanbing Xu, Rita Yao, Mengsha You, Yiling Zachár, Gergely Zhao, Yibo Ameli, Mostafa Baig, Mirza Najamuddin Bhaskaran, Sarah Butts, Kenneth Gowda, Manasi Janssen, Caroline Lee, John Pedersen, Liam Wagner, Riley Zhang, Zimo Zhou, Chang Work, Daniel B. Seibold, Benjamin Sprinkle, Jonathan Piccoli, Benedetto Monache, Maria Laura Delle Bayen, Alexandre M. Systems and Control The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest. |
| title | Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2402.17043 |