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The moral dilemma of autonomous vehicles: Prioritizing pedestrian protection

Year: 2026
Type: article
Abstract: Autonomous vehicles (AVs) are expected to significantly reduce traffic fatalities, yet unavoidable crash scenarios raise unresolved questions about how residual risk should be distributed between vehicle occupants and pedestrians. This study examines whether public support exists for programming AVs to prioritize pedestrian protection and whether such support can be influenced by highlighting the asymmetric risk relationship between motor vehicle users and vulnerable road users. Drawing on research in moral decision-making and moral dilemmas, we conceptualize private motorized travel as a behavioral choice that statistically externalizes risk onto pedestrians. Two experimental studies were conducted in the United Kingdom and China (total N = 1,060). Study 1 tested whether individuals judge harm as more permissible when the harmed party unintentionally generates a threat to others. Study 2 examined whether making this threat asymmetry salient influences preferences regarding AV programming in scenarios where pedestrians and occupants differ in number or rule compliance. Results indicate that participants consider harm more morally permissible when the harmed individual causally contributes to the threat. In the cross-national experiment, emphasizing asymmetric risk exposure increased support for pedestrian-prioritizing AV algorithms, particularly in the Chinese sample, removing a baseline majoritarian support for protecting occupants when pedestrians were fewer or disobeyed rules. Across both samples, substantial support emerged for prioritizing pedestrians when groups were equal in number and rule observance. These findings demonstrate how perceived risk asymmetry shapes public acceptance of algorithmic decision-making in automated mobility systems and inform debates on governance and societal impacts of connected and automated driving.
Language: English
Open Access status: gold
Estimated APC paid: $2,400
OpenAlex ID: W7222286183
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Cites: 36
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