Effects of environmental, vehicle and human factors on comfort in partially automated driving: A scenario-based study

Delmas, Maxime; Camps, Valérie; Lemercier, Céline · 2022 · Crossref

DOI: 10.1016/j.trf.2022.03.012

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Summary

This study investigates the factors influencing passenger comfort in partially automated vehicles (SAE Level 3), addressing a gap in literature that has predominantly focused on higher automation levels or the driver’s takeover performance. The research aims to determine how environmental conditions (road type, weather, traffic congestion) and vehicle parameters (speed) affect perceived comfort, and how these effects vary across different human profiles defined by trust in automation, driving style, and locus of control. The researchers employed an online scenario-based methodology using Anderson’s information integration theory. Two hundred and one licensed French-speaking participants evaluated 24 scenarios combining four within-participant factors: road type (highway, secondary, downtown), weather (clear, heavy rain), traffic congestion (few, many vehicles), and vehicle speed (prescribed speed vs. 20 km/h below). Participants rated their perceived comfort on a 20-point scale. Additionally, participants completed the Multidimensional Driving Style Inventory, the Traffic Locus of Control Scale, and a trust-in-automation questionnaire. Data were analyzed using ANOVA to assess main and interaction effects, followed by K-means cluster analysis to identify distinct participant profiles. Results indicated that comfort was significantly reduced by driving in downtown areas, heavy rain, and congested traffic. Crucially, interaction analyses revealed that reducing vehicle speed mitigated the negative impact of heavy rain and high traffic congestion on comfort. Cluster analysis identified four distinct profiles: "Trusting in automation" (high comfort, high trust), "Averse to speed reduction" (moderate comfort, preferred prescribed speed), "Risk averse" (moderate comfort, preferred reduced speed in adverse conditions), and "Mistrusting automation" (low comfort, low trust). The "Risk averse" group was the largest and showed the strongest sensitivity to weather and traffic, preferring lower speeds in adverse conditions. The "Trusting" group remained comfortable across most conditions, while the "Mistrusting" group reported low comfort regardless of conditions. The findings suggest that optimizing comfort in partially automated driving requires adaptive driving styles rather than rigid adherence to speed limits. Specifically, automated systems should reduce speed in response to adverse weather and high traffic density to enhance passenger comfort. Furthermore, the study highlights that human factors, particularly trust in automation, significantly moderate the impact of driving conditions. These insights imply that future automated vehicle designs should account for both environmental variables and individual user profiles to improve acceptance and user experience.

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Topics

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• passenger motion sickness comfort
• acceptance adoption
• situational awareness
• trust calibration
• automation
• traffic density

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• Empirical Findings: self report data
• Theoretical Contribution: conceptual framework, computational model