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

Understand

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.