Deployment of newly developed autonomous vehicle (AV) technology on public roads may result in exposure to the public of high levels of danger. A proactive metric, as opposed to a reactive metric, is required to evaluate the safety of an automated system without relying on the rate of vehicle accidents and fatalities relative to the number of miles driven. Resilience engineering (RE) is a new safety management paradigm that focuses on examining the interaction of complex systems with their environment. This chapter presents the concept of RE and the resilience assessment grid (RAG) as an evaluation tool for measuring the operational resilience of AV systems. This evaluation tool was used to test the responsiveness of a Pure Pursuit controller (PPC) and a Deep Learning controller (DLC) in the CARLA simulator. The path tracking control algorithms were evaluated using traditional reactive and proactive RE measures tested at various speeds. RE proved to be a straightforward method for enhancing performance and assessing the operational robustness of an AV controller and thus is suitable for evaluation of other AV subsystems as well as the AV system itself.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering




    Publication date :

    2023-09-02


    Size :

    25 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Performance Evaluation of an Autonomous Vehicle Using Resilience Engineering

    Rupp, Jeff / Fanas Rojas, Johan / Brown, Nicolas et al. | SAE Technical Papers | 2022


    Performance Evaluation of an Autonomous Vehicle Using Resilience Engineering

    Fanas Rojas, Johan / Brown, Nicolas / Rupp, Jeff et al. | British Library Conference Proceedings | 2022


    Performance Evaluation of an Autonomous Vehicle Using Resilience Engineering

    Fanas Rojas, Johan / Brown, Nicolas / Rupp, Jeff et al. | British Library Conference Proceedings | 2022


    Using Reinforcement Learning and Simulation to Develop Autonomous Vehicle Control Strategies

    Navarro, Anthony / Genc, Sahika / Rangarajan, Premkumar et al. | British Library Conference Proceedings | 2020


    Unlocking resilience through autonomous innovation

    Aditya Bahadur | TIBKAT | 2022

    Free access