The core value of simulation-based autonomy tests is to create densely extreme traffic scenarios to test the performance and robustness of the algorithms and systems. Test scenarios are usually designed or extracted manually from the real-world data, which is inefficient with a remarkable domain gap compared with testing in real scenarios. Therefore, it is crucial to automatically generate realistic and diverse dynamic traffic scenarios making autonomy tests efficient. Moreover, scenario generation is expected to be interpretable, controllable, and diversified, which can be hard to achieve simultaneously by methods based on rules or deep networks. In this paper, we propose a dynamic traffic scenario generation method called SceGene, inspired by genetic inheritance and mutation processes in biological intelligence. SceGene applies biological processes, such as crossover and mutation, to exchange and mutate the content of scenarios, and involves the natural selection process to control generation direction. SceGene has three main parts: 1) a new representation method for describing the traffic scenarios’ feature; 2) a new scenario generation algorithm based on crossover, mutation, and selection; and 3) an abnormal scenario information repair method based on the microscopic driving model. Evaluation on the public traffic scenario dataset shows that SceGene can ensure highly realistic and diversified scenario generation in an interpretable and controllable way, significantly improving the efficiency of the simulation-based autonomy tests.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    SceGene: Bio-Inspired Traffic Scenario Generation for Autonomous Driving Testing


    Contributors:
    Li, Ao (author) / Chen, Shitao (author) / Sun, Liting (author) / Zheng, Nanning (author) / Tomizuka, Masayoshi (author) / Zhan, Wei (author)


    Publication date :

    2022-09-01


    Size :

    3040170 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Automated scenario generation and iterative regression testing method for autonomous driving systems

    Duan, Jianli / Wang, Rui / Zhao, Shulian et al. | British Library Conference Proceedings | 2022


    AUTONOMOUS DRIVING SCENARIO GENERATION METHOD, APPARATUS AND SYSTEM

    SHAO KUN / WANG BIN / LIU WULONG et al. | European Patent Office | 2022

    Free access

    Scenario retrieval based automatic scenario generation system and method for autonomous driving

    PARK CHANG GUE / MIN KYOUNG WON / SON HAENG SEON et al. | European Patent Office | 2024

    Free access

    Driving environment scenario generator for autonomous driving testing using digital twin technology

    KIM TAG GON / YANG YOUNG JIN / YOO HO DONG | European Patent Office | 2022

    Free access

    SIMULATION SCENARIO GENERATION BASED ON AUTONOMOUS VEHICLE DRIVING DATA

    LEE RITCHIE | European Patent Office | 2024

    Free access