  Modern automated driving solutions utilize trajectory planning and control
components with numerous parameters that need to be tuned for different driving
situations and vehicle types to achieve optimal performance. This paper
proposes a method to automatically tune such parameters to resemble expert
demonstrations. We utilize a cost function which captures deviations of the
closed-loop operation of the controller from the recorded desired driving
behavior. Parameter tuning is then accomplished by using local optimization
techniques. Three optimization alternatives are compared in a case study, where
a trajectory planner is tuned for lane following in a real-world driving
scenario. The results suggest that the proposed approach improves manually
tuned initial parameters significantly even with respect to noisy demonstration
data.
