Papers › ISS-Scenario: Scenario-based Testing in CARLA
ISS-Scenario: Scenario-based Testing in CARLA
Renjue Li, Tianhang Qin, Cas Widdershoven
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections