The complexity of realistic autonomous system scenarios with many parameters results in a curse of dimensionality which makes finding bugs and predicting extents of bug profiles an arduous task through formal methods, and a time consuming process in simulation-based approaches. This paper introduces the Eagle Strategy with Local Search, a nature inspired approach based on eagle strategy to discovering bug profile limits for scenario-based validation of autonomous systems. The approach is modular and extendable in terms of the used strategies, and also scalable to n-dimensions of scenario parameters. The approach is integrated with methods of bug classification and bug profile visualization in high dimensionality. The performance of the approach is demonstrated by extensive simulations of an autonomous vehicle validation scenario.


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    Title :

    Eagle Strategy with Local Search for Scenario Based Validation of Autonomous Vehicles


    Contributors:


    Publication date :

    2022-03-07


    Size :

    1835222 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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