Advancement in testing and verification methodologies is one of the key requirements for the commercialization and standardization of autonomous driving. Even though great progress has been made, the main challenges encountered during testing of autonomous vehicles, e.g., high number of test scenarios, huge parameter space and long simulation runs, still remain. In order to reduce current testing efforts, we propose an innovative method based on surrogate models in combination with stochastic optimization. The approach presents an iterative zooming-in algorithm aiming to minimize a given cost function and to identify faulty behavior regions within the parameter space. The surrogate model is updated in each iteration and is further used for intensive evaluation tasks, such as exploration and optimization.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Testing of autonomous vehicles using surrogate models and stochastic optimization


    Contributors:


    Publication date :

    2017-10-01


    Size :

    134454 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Managing Surrogate Models in Optimization

    Keane, Andy J. / Nair, Prasanth B. | Wiley | 2005


    Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

    Mark Mario Morando / Qingyun Tian / Long T. Truong et al. | DOAJ | 2018

    Free access


    Structural optimization using space mapping and surrogate models

    Redhe, M. / Nilsson, L. | Tema Archive | 2002


    TESTING PREDICTIONS FOR AUTONOMOUS VEHICLES

    FAIRFIELD NATHANIEL / FURMAN VADIM | European Patent Office | 2023

    Free access