STCLocker uses spatial and temporal conflict signals to generate multi-autonomous-vehicle deadlock scenarios in CARLA and finds significantly more such scenarios than baseline methods.
Moral Testing of Autonomous Driving Systems
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abstract
Autonomous Driving System (ADS) testing plays a crucial role in their development, with the current focus primarily on functional and safety testing. However, evaluating the non-functional morality of ADSs, particularly their decision-making capabilities in unavoidable collision scenarios, is equally important to ensure the systems' trustworthiness and public acceptance. Unfortunately, testing ADS morality is nearly impossible due to the absence of universal moral principles. To address this challenge, this paper first extracts a set of moral meta-principles derived from existing moral experiments and well-established social science theories, aiming to capture widely recognized and common-sense moral values for ADSs. These meta-principles are then formalized as quantitative moral metamorphic relations, which act as the test oracle. Furthermore, we propose a metamorphic testing framework to systematically identify potential moral issues. Finally, we illustrate the implementation of the framework and present typical violation cases using the VIRES VTD simulator and its built-in ADS.
fields
cs.SE 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems
STCLocker uses spatial and temporal conflict signals to generate multi-autonomous-vehicle deadlock scenarios in CARLA and finds significantly more such scenarios than baseline methods.