This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Stress Testing for Autonomous Vehicles


    Contributors:


    Publication date :

    2018-06-01


    Size :

    355521 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ADAPTIVE STRESS TESTING FOR AUTONOMOUS VEHICLES

    Koren, Mark / Alsaif, Saud / Lee, Ritchie et al. | British Library Conference Proceedings | 2018


    Adaptive Testing of Controllers for Autonomous Vehicles

    A. C. Schultz / J. J. Grefenstette / K. A. DeJong | NTIS | 1992


    Automatic testing of autonomous vehicles

    NIELSEN ERIK / KAUFMAN CHASE | European Patent Office | 2023

    Free access

    Testing predictions for autonomous vehicles

    FAIRFIELD NATHANIEL / FURMAN VADIM | European Patent Office | 2021

    Free access

    ADAPTIVE AUTONOMOUS BRAKING ACTIONS FOR AUTONOMOUS VEHICLES

    YUAN BAOPING / SUN TIANJIA / SHEN YAOMING | European Patent Office | 2024

    Free access