Effects of gender, age, experience, and practice on driver reaction and acceptance of traffic jam chauffeur systems

Muslim, Husam; Itoh, Makoto; Liang, Cho Kiu; Antona-Makoshi, Jacobo; Uchida, Nobuyuki · 2021 · Crossref

DOI: 10.1038/s41598-021-97374-5

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Summary

This study investigates how driver demographics—specifically gender, age, driving experience, and prior practice with automated systems—interact with different Automated Driving System (ADS) designs to influence driver reaction, acceptance, and trust. The research addresses a gap in understanding how these human factors affect decision-making during non-critical conditional automation scenarios, particularly when the system requires driver intervention or approval for maneuvers like lane changes. The authors hypothesized that combined demographic effects would outweigh individual factors and that systems requiring more driver control would be less accepted. The experiment utilized a medium-fidelity driving simulator with 40 licensed drivers (20 male, 20 female; ages 22–69). Participants were categorized by gender, age/experience (younger vs. older), and prior practice with automated driving. Each driver experienced four ADS designs during a simulated highway traffic jam scenario: ADS-1 (system slows down, driver must manually change lanes); ADS-2 (system requests manual takeover); ADS-3 (system requests permission to change lanes automatically); and ADS-4 (system executes automatic lane change after a 6-second warning, allowing driver override). The study employed a within-subject repeated measures design, measuring reaction times, control inputs, and post-experiment subjective ratings of acceptance and trust. Results indicated that 92% of drivers chose to change lanes to avoid congestion. Reaction times were significantly influenced by ADS design, gender, age, experience, and practice, with significant interactions between ADS design and demographic groups. ADS-2 and ADS-3 elicited faster reactions than ADS-1 and ADS-4. While individual demographic factors showed limited significant effects in isolation, their combined impact was pronounced. For instance, practiced younger female drivers reacted faster than non-practiced younger males in ADS-1, while practiced older females reacted fastest in ADS-2. Acceptance was highest for ADS-1 and ADS-2 and lowest for ADS-4. Trust ratings were generally above mid-scale but significantly lower for ADS-4, which drivers found difficult to trust due to the short decision window. Notably, ADS-4 was less trusted by practiced younger males than practiced younger females. The findings suggest that the interaction between driver demographics and ADS design is more critical than individual demographic factors alone. Contrary to expectations that human-like automation increases trust, the highly autonomous ADS-4 received lower trust and acceptance, particularly among specific demographic subgroups. The study concludes that ADS designs requiring driver decisions or control are more accepted, and that system capabilities significantly modulate the impact of demographic factors on driver behavior. These insights are valuable for designing human-machine interfaces that account for diverse user populations and varying levels of system autonomy.

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Topics

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• automation
• automation surprise
• acceptance adoption
• trust calibration
• situational awareness
• takeover transitions

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: computational model, conceptual framework