Driver Distraction and Reliance: Adaptive Cruise Control in the Context of Sensor Reliability and Algorithm Limits

Seppelt, Bobbie D; Lees, Fergus N; Lee, John D · 2005 · Crossref

DOI: 10.17077/drivingassessment.1168

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Summary

This study investigates how different types of system failures influence driver reliance on Adaptive Cruise Control (ACC) and the resulting safety implications. Motivated by the high prevalence of rear-end collisions caused by inattention and unsafe following distances, the research addresses the complexity of automation reliability. Specifically, it examines whether drivers appropriately intervene when ACC capabilities are exceeded due to sensor degradation or algorithmic limits, and how a secondary cognitive task interacts with ACC reliance. The experiment utilized a medium-fidelity driving simulator with 16 participants who completed two 18-minute drives: one with manual control and one with ACC available. The design was a 2x2 within-subjects factorial, manipulating automation level (ACC vs. manual) and failure type (traffic vs. rain). In "traffic" periods, ACC braking limits were exceeded as lead vehicle decelerations intensified. In "rain" periods, sensor reliability was degraded by simulated fog and rain, causing temporary detection failures. Throughout the drives, participants performed a continuous secondary task involving listening to and verbally responding to complex restaurant information to induce distraction. Dependent variables included brake response time, time headway (THW), time-to-collision (TTC), collision frequency, and percent reliance on ACC. Results indicated that failure type significantly influenced driver reliance and safety outcomes. In traffic conditions, drivers relied more heavily on ACC, exhibiting longer brake response times but maintaining significantly larger THW and TTC values compared to manual control. This resulted in zero collisions during ACC engagement versus eight in manual control, demonstrating a net safety benefit. Conversely, in rain conditions, sensor failures undermined ACC’s effectiveness; there were no significant differences in brake response, THW, or collision rates between ACC and manual control. Reliance profiles showed hysteresis in both conditions, but drivers disengaged ACC more readily during rain than during traffic. Furthermore, ACC improved headway maintenance during complex secondary tasks, whereas manual control performance declined as task complexity increased. The study concludes that ACC provides a safety benefit primarily when drivers are distracted by complex mental tasks and when system failures are related to braking limits rather than sensor degradation. Drivers tend to over-rely on ACC in traffic situations, allowing the system to compensate for their delayed reactions. However, this reliance becomes dangerous when sensor failures occur, as drivers fail to disengage the system promptly. The findings highlight a mismatch between perceived and actual system competence, suggesting that ACC design must account for how different failure modes affect driver trust and vigilance to ensure appropriate reliance.

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Topics

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• trust calibration
• manual
• situational awareness

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model