Vehicles with Automated Driving Systems (ADS) operate in a high-dimensional continuous system with multiagent interactions. This continuous system features various types of traffic agents (non-homogeneous) governed by continuous-motion ordinary differential equations (differential-drive). Each agent makes decisions independently that may lead to conflicts with the subject vehicle (SV), as well as other participants (non-cooperative). A typical vehicle safety evaluation procedure that uses various safety-critical scenarios and observes resultant collisions (or near collisions), is not sufficient enough to evaluate the performance of the ADS in terms of operational safety status maintenance. In this paper, we introduce a Model Predictive Instantaneous Safety Metric (MPrISM), which determines the safety status of the SV, considering the worst-case safety scenario for a given traffic snapshot. The method then analyzes the SV's closeness to a potential collision within a certain evaluation time period. The described metric induces theoretical guarantees of safety in terms of the time to collision under standard assumptions. Through formulating the solution as a series of minimax quadratic optimization problems of a specific structure, the method is tractable for real-time safety evaluation applications. Its capabilities are demonstrated with synthesized examples and cases derived from real-world tests.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Model Predictive Instantaneous Safety Metric for Evaluation of Automated Driving Systems


    Beteiligte:
    Weng, Bowen (Autor:in) / Rao, Sughosh J. (Autor:in) / Deosthale, Eeshan (Autor:in) / Schnelle, Scott (Autor:in) / Barickman, Frank (Autor:in)


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    1368925 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Stochastic Model Predictive Control With a Safety Guarantee for Automated Driving

    Brudigam, Tim / Olbrich, Michael / Wollherr, Dirk et al. | IEEE | 2023


    Model Predictive Trajectory Planning for Automated Driving

    Yi, Boliang / Bender, Philipp / Bonarens, Frank et al. | IEEE | 2019


    Automated Functional Safety Analysis of Automated Driving Systems

    Kölbl, Martin / Leue, Stefan | BASE | 2018

    Freier Zugriff

    Safety verification of automated driving systems

    Kianfar, Roozbeh | Online Contents | 2013