  This paper presents the first evaluation of k-nearest neighbours-Averaging
(kNN-Avg) on a real-world case study. kNN-Avg is a novel technique that tackles
the challenges of noisy multi-objective optimisation (MOO). Existing studies
suggest the use of repetition to overcome noise. In contrast, kNN-Avg
approximates these repetitions and exploits previous executions, thereby
avoiding the cost of re-running. We use kNN-Avg for the scenario generation of
a real-world autonomous driving system (ADS) and show that it is better than
the noisy baseline. Furthermore, we compare it to the repetition-method and
outline indicators as to which approach to choose in which situations.
