  Model predictive control (MPC) is a promising approach for 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 a challenging task. Numerous
tuning parameters as well as conflicting requirements need to be considered.
This contribution formulates the MPC tuning task as a multi-objective
optimization problem. Solving it is challenging for two reasons: First,
MPC-parameterizations are evaluated on an computationally expensive simulation
environment. As a result, the used optimization algorithm needs to be as
sampleefficient as possible. Second, for some poor parameterizations the
simulation cannot be completed and therefore useful objective function values
are not available (learning with crash constraints). In this contribution, 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 and by a method on how to deal with crash
constraints. Results show, that BO works best for a small budget, NSGA-II is
best for medium budgets and for large budgets none of the evaluated optimizers
is superior to random search. Both proposed BO extensions are shown to be
beneficial.
