What Is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?

David Stenger, Robert Ritschel, Felix Krabbes, Rick Voßwinkel and Hendrik Richter ()
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David Stenger: Institute of Automatic Control (IRT), RWTH Aachen University, D-52074 Aachen, Germany
Robert Ritschel: Department Automated Driving Functions, IAV GmbH, D-09120 Chemnitz, Germany
Felix Krabbes: Faculty of Automotive Engineering, Zwickau University of Applied Sciences, D-08056 Zwickau, Germany
Rick Voßwinkel: Faculty of Automotive Engineering, Zwickau University of Applied Sciences, D-08056 Zwickau, Germany
Hendrik Richter: Faculty of Engineering, HTWK Leipzig University of Applied Sciences, D-04277 Leipzig, Germany

Mathematics, 2023, vol. 11, issue 2, 1-19

Abstract: Model predictive control (MPC) is a promising approach to the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort, as well as lateral and longitudinal tracking, is challenging. Numerous tuning parameters and conflicting requirements need to be considered. In this paper, we formulate the MPC tuning task as a multi-objective optimization problem. Its solution is demanding for two reasons: First, MPC-parameterizations are evaluated in a computationally expensive simulation environment. As a result, the optimization algorithm needs to be as sample-efficient as possible. Second, for some poor parameterizations, the simulation cannot be completed; therefore, useful objective function values are not available (for instance, learning with crash constraints). In this work, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II), and multiple versions of Bayesian optimization (BO). We extend BO by introducing an adaptive batch size to limit the computational overhead. In addition, we devise a method to deal with crash constraints. The results show that BO works best for a small budget, NSGA-II is best for medium budgets, and none of the evaluated optimizers are superior to random search for large budgets. Both proposed BO extensions are, therefore, shown to be beneficial.

Keywords: Bayesian optimization; metaheuristics; model predictive control; multi-objective optimization; controller tuning; vehicle guidance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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