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Moral Testing of Autonomous Driving Systems

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arxiv 2505.03683 v1 pith:CUE5QUKG submitted 2025-05-06 cs.SE

classification cs.SE
keywords moraltestingadssautonomousdrivingframeworkmeta-principlesmetamorphic
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems

    cs.SE 2025-06 conditional novelty 6.0 of 10

    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.

  2. ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A two-stage trained transformer module, ADReFT, repairs unsafe autonomous driving decisions at runtime, fixing more collisions with smaller interventions than rule-based and anomaly baselines.

  3. Causality-aware Safety Testing for Autonomous Driving Systems

    cs.SE 2025-06 conditional novelty 6.0 of 10

    Causal-Fuzzer uses causal graphs of scene, action, and violation relationships to guide simulation fuzzing, and reports finding more diverse violations and better testing sufficiency than three baselines on Apollo.

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