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- W4285563005 abstract "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." @default.
- W4285563005 created "2022-07-16" @default.
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- W4285563005 date "2021-09-15" @default.
- W4285563005 modified "2023-09-25" @default.
- W4285563005 title "Handling Noise in Search-Based Scenario Generation for Autonomous Driving Systems" @default.
- W4285563005 doi "https://doi.org/10.48550/arxiv.2109.07698" @default.
- W4285563005 hasPublicationYear "2021" @default.
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