Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid

Beckers, Niek; Siebert, Luciano Cavalcante; Bruijnes, Merijn; Jonker, Catholijn; Abbink, David · 2022 · Crossref

DOI: 10.1038/s41598-022-19876-0

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Summary

This study investigates the public’s attribution of culpability in crashes involving partially automated vehicles, specifically examining whether people consider a driver’s diminished ability to take control when assigning blame. The research is motivated by a normative gap: while manufacturers and the public often hold drivers legally and morally responsible for crashes in partially automated systems, human factors literature suggests that prolonged supervision of automation degrades driver vigilance and situation awareness, making immediate takeover unreasonable. The authors question whether the public’s blame attribution aligns with the driver’s actual capacity to avoid accidents. To address this, the researchers conducted an online vignette study with 250 participants. Participants were randomly assigned to one of five scenarios describing a crash where a partially automated vehicle failed and requested an immediate takeover, which the driver failed to execute. The scenarios varied the driver’s distraction level (not distracted, short distraction, long distraction) and the source of distraction (intentional, such as using entertainment systems, or unintentional, such as mind-wandering). Participants rated the driver’s situation awareness and ability to intervene, then assigned responsibility on a 100-point scale to the driver, the automated vehicle, and the manufacturer. They also provided textual motivations for their judgments. The results indicate that participants primarily blamed the driver across all scenarios, regardless of distraction level or source. Although participants correctly recognized that distracted drivers had lower situation awareness and reduced ability to take control, this perceived inability did not significantly reduce the blame attributed to the driver. There was no significant shift in responsibility toward the vehicle or manufacturer, even when the driver’s distraction was unintentional. Thematic analysis of participant comments revealed that blame was largely based on normative arguments: participants believed drivers voluntarily committed to supervising the vehicle and failed to do so, ignoring the inherent difficulties of maintaining vigilance during automated driving. The study concludes that there is a significant mismatch between public perception and normative standards of culpability. The public holds drivers responsible even when their ability to control the outcome is compromised by the automation itself. This "culpability gap" suggests that current liability frameworks and public understanding may be unreasonable, as they ignore the detrimental impact of automation on human performance. The authors argue that responsibility should be balanced with ability, implying that blame should shift toward manufacturers when automation design impairs the driver’s capacity to intervene. The findings highlight the need for improved public awareness regarding human-factor limitations in automated driving and suggest that current legal and social norms may unfairly penalize drivers for systemic design flaws.

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Topics

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• automation surprise
• situational awareness
• pre crash contributing factors
• automation
• mode awareness
• manual

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• Theoretical Contribution: conceptual framework, theory or model