Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. Theoretical analysis suggests that the proposed framework can obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed method. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology


    Contributors:
    Feng, Shuo (author) / Feng, Yiheng (author) / Yu, Chunhui (author) / Zhang, Yi (author) / Liu, Henry X. (author)


    Publication date :

    2021-03-01


    Size :

    1161983 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Automated scenario generation for regression testing of autonomous vehicles

    Rocklage, Elias / Kraft, Heiko / Karatas, Abdullah et al. | IEEE | 2017


    Simulation Testing Scenario Generation for Comfort Evaluation of Automated Vehicles

    Yang, Yuhang / Wang, Yafei / Yin, Chengliang et al. | IEEE | 2021