Due to the rarity of safety-critical scenarios, the safety of autonomous driving decision-making algorithms cannot be fully guaranteed in open environments, which limits their further applications. Therefore, it is crucial to efficiently generate such scenarios for the safety validation of autonomous driving algorithms. This paper proposes an efficient safety-critical scenario generation method combining dynamic and static parameters that constitute a scenario by alternating the optimization of them. To generate dynamic scenario parameters efficiently, a traffic participants model integrating reinforcement learning and prior traffic knowledge is proposed, which is then optimized under the guidance of a prior risk estimation model. For the generation of static scenario parameters, a posterior risk estimation model is proposed, which learns from the explored parameter trajectories during the dynamic scenario generation, to estimate the risk of unknown states. Finally, three different efficiency metrics are proposed and experiments are conducted on rule-based, optimization-based, and learning-based autonomous driving algorithms to verify the high efficiency and generality of the proposed method. Compared with the baseline model that only considers dynamic parameters, the proposed method averagely improves the generation speed by 151.8% and the success rate by 78.7% with only a few alternate optimizations. In addition, the method is generalizable for different types of autonomous driving algorithms. The safety-critical scenario generation paradigm proposed in this paper can greatly accelerate the safety evaluation for any given algorithm, which is of great significance for the development of autonomous driving.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient Generation of Safety-Critical Scenarios Combining Dynamic and Static Scenario Parameters


    Beteiligte:
    Wang, Zhaoyi (Autor:in) / Li, Xincheng (Autor:in) / Wei, Dengwei (Autor:in) / Wang, Liwen (Autor:in) / Huang, Yanjun (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    5985380 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Scenario Factory: Creating Safety-Critical Traffic Scenarios for Automated Vehicles

    Klischat, Moritz / Liu, Edmond Irani / Holtke, Fabian et al. | IEEE | 2020


    Safety-Critical Scenario Generation by Causal Influence Detection

    Yang, Yibing / Zhang, Chi / Xu, Linhai et al. | IEEE | 2024


    Diversifying Latent Flows for Safety-Critical Scenarios Generation

    Gao, Dingcheng / Qin, Yanjun / Tao, Xiaoming et al. | IEEE | 2024


    Scenarios and Scenario Databases for AV Safety Assurance

    Glasmacher, Christoph / Schuldes, Michael / Klas, Christoph et al. | Springer Verlag | 2024


    Safety Evaluation of Autonomous Driving Based on Safety-Critical Scenario Generation

    Wang, Zhaoyi / Li, Xincheng / Yang, Shuo et al. | IEEE | 2024