ACC design for safety and fuel efficiency: the acceptance of safety margins when adopting different driving styles

Pampel, Sanna M.; Jamson, Samantha; Hibberd, Daryl; Barnard, Yvonne · 2019 · OpenAlex-citations

DOI: 10.1007/s10111-019-00571-6

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Summary

This study investigates how drivers’ mental model boundaries for safety margins shift when adopting different driving goals, specifically safe driving versus fuel-efficient (eco) driving. The research addresses a critical design challenge for adaptive cruise control (ACC) and forward collision warning systems: automation limits must align with drivers’ intuitive expectations to ensure acceptance and safe intervention. While previous work established that drivers have natural boundaries for time headway (Th) and time to collision (TC), it was unclear if these boundaries remain constant or vary based on strategic driving objectives. The authors hypothesized that drivers might accept lower safety margins during eco-driving to maintain constant speed, contrasting with the larger margins expected during safe driving. The experiment employed a within-subject design with 16 participants using a desktop driving simulator. Participants completed three 10-minute drives under different instructions: “drive safely,” “drive fuel-efficiently,” and a baseline condition with no specific instructions. The scenario involved a motorway environment where vehicles cut into the participant’s lane, creating varying Th and TC values. Automation systems were disabled to measure natural human behavior. Data collection focused on critical decision points where drivers either maintained nominal car-following or engaged in active braking. The analysis utilized Satisficing Decision Theory (SDT) to model the boundaries between acceptable car-following and rejectable states requiring braking. Fuel consumption was also modeled to validate the effectiveness of the eco-driving instructions. The results demonstrated that both safe and eco-driving instructions led drivers to brake at longer safety margins compared to the baseline condition, contradicting the hypothesis that eco-driving would result in tighter following distances. Statistical analysis of the SDT coefficients revealed significant differences between the conditions. Specifically, the rejectability functions indicated that drivers in the eco condition tended to brake at larger headways, likely to avoid harsh braking and maintain steady speeds through “coasting.” Fuel modeling confirmed that the eco condition resulted in a 2.8% reduction in fuel consumption compared to the baseline. Crucially, no collisions occurred in any condition, indicating that drivers prioritized safety over fuel efficiency in critical situations. The boundary lines separating nominal from braking behavior were less steep for the eco condition, suggesting that time headway had a reduced influence on braking decisions when drivers were focused on efficiency. These findings imply that drivers’ preferences for operational limits of longitudinal automation are not static but shift according to their strategic goals. The assumption of constant natural mental model boundaries is therefore too simplified for system design. The study suggests that ACC and related systems should account for these variable boundaries, potentially by offering distinct “safe” and “eco” modes that adjust automation limits to match the driver’s current objective. Aligning system behavior with these shifting mental models can enhance predictability, reduce mode confusion, and improve both the safety and acceptance of automated driving features. The authors note limitations regarding the small sample size and the use of a desktop simulator, recommending validation with larger groups and more realistic driving conditions.

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Topics

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• eco driving
• following distance

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• Theoretical Contribution: computational model