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Optimal Weight Adaptation for Model Predictive Control of Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization
In this paper, we develop an optimal weight adap- tation strategy of model predictive control (MPC) for connected and automated vehicles (CAVs) in mixed traffic. We model the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game and formulate a game-theoretic MPC problem to find a Nash equilibrium of the game. In the MPC problem, the weights in the HDV’s objective function can be learned online using moving horizon inverse reinforcement learning. Using Bayesian optimization, we propose a strategy to optimally adapt the weights in the CAV’s objective function so that the expected true cost when using MPC in simulations can be minimized. We validate the effectiveness of the optimal strategy by numerical simulations of a vehicle crossing example at an unsignalized intersection.
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- NSF-PAR ID:
- 10421260
- Date Published:
- Journal Name:
- 2023 American Control Conference
- Page Range / eLocation ID:
- 1183-1188
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
