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- W4302011771 abstract "In this paper, we develop an optimal weight adaptation 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." @default.
- W4302011771 created "2022-10-06" @default.
- W4302011771 creator A5065617320 @default.
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- W4302011771 date "2022-10-02" @default.
- W4302011771 modified "2023-10-16" @default.
- W4302011771 title "Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization" @default.
- W4302011771 doi "https://doi.org/10.48550/arxiv.2210.00700" @default.
- W4302011771 hasPublicationYear "2022" @default.
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