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Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic

MK Bouzidi, Y. Yao, D. Goehring, J. Reichardt – 2023

Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of providing an adequately close initial guess for the current optimal trajectory. This can potentially result in convergence failures and safety issues. Therefore, this paper proposes a framework for learning-aided warmstarts of Model Predictive Control algorithms. Our method leverages a neural network based multimodal predictor to generate multiple trajectory proposals for the autonomous vehicle, which are further refined by a sampling-based technique. This combined approach enables us to identify multiple distinct local minima and provide an improved initial guess. We validate our approach with Monte Carlo simulations of traffic scenarios.

Titel
Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic
Verfasser
MK Bouzidi, Y. Yao, D. Goehring, J. Reichardt
Verlag
Cornell University
Datum
2023-10-04
Erschienen in
Cornell University, Electrical Engineering and Systems Science - Systems and Control, Arxiv 2310.02918.
Größe oder Länge
7 pages