ISS-Scenario: Scenario-based Testing in CARLA
<|reference_start|>ISS-Scenario: Scenario-based Testing in CARLA: The rapidly evolving field of autonomous driving systems (ADSs) is full of promise. However, in order to fulfil these promises, ADSs need to be safe in all circumstances. This paper introduces ISS-Scenario, an autonomous driving testing framework in the paradigm of scenario-based testing. ISS-Scenario is designed for batch testing, exploration of test cases (e.g., potentially dangerous scenarios), and performance evaluation of autonomous vehicles (AVs). ISS-Scenario includes a diverse simulation scenario library with parametrized design. Furthermore, ISS-Scenario integrates two testing methods within the framework: random sampling and optimized search by means of a genetic algorithm. Finally, ISS-Scenario provides an accident replay feature, saving a log file for each test case which allows developers to replay and dissect scenarios where the ADS showed problematic behavior.<|reference_end|>
@article{li2024iss-scenario:,
    title={ISS-Scenario: Scenario-based Testing in CARLA},
    author={Renjue Li, Tianhang Qin, Cas Widdershoven},
    journal={arXiv preprint arXiv:2406.15777},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15777},
    primaryClass={cs.SE}
}