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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.14567 |
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| _version_ | 1866917844723171328 |
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| author | Bhattacharyya, Viranjan Ard, Tyler Wang, Rongyao Vahidi, Ardalan Jia, Yunyi Han, Jihun |
| author_facet | Bhattacharyya, Viranjan Ard, Tyler Wang, Rongyao Vahidi, Ardalan Jia, Yunyi Han, Jihun |
| contents | In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments Bhattacharyya, Viranjan Ard, Tyler Wang, Rongyao Vahidi, Ardalan Jia, Yunyi Han, Jihun Systems and Control In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers. |
| title | Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.14567 |