How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

Bianchi Piccinini, Giulio; Lehtonen, Esko; Forcolin, Fabio; Engström, Johan; Albers, Deike; Markkula, Gustav; Lodin, Johan; Sandin, Jesper · 2019 · Crossref

DOI: 10.1177/0018720819875347

archive: archived pipeline: cataloged verified

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Summary

This study addresses the lack of validated computational models for predicting driver brake reaction times (BRTs) during silent failures of Adaptive Cruise Control (ACC). While automated driving systems are expected to reduce crashes caused by human error, drivers supervising these systems may experience complacency or skill degradation. The authors aimed to develop and test computational models that predict BRT distributions in rear-end scenarios, comparing manual driving with Cruise Control (CC) against ACC driving where the system fails without alerting the driver. The researchers proposed two alternative models for ACC silent failures: a "looming prediction model," which assumes drivers maintain a mental model of ACC behavior and react to deviations from expected visual looming cues, and a "lower gain model," which assumes reduced driver arousal leads to slower evidence accumulation for braking. These models were validated using data from a driving simulator study involving 49 participants. The study employed a within-subject design where participants drove with CC and ACC in a high-fidelity simulator. During ACC trials, the system silently failed during specific lead vehicle braking events with varying deceleration rates (2.5–4.5 m/s²). The computational models were initially parameterized using data from previous studies and then fitted to the new simulator data. The results confirmed that BRTs were significantly shorter as kinematic criticality increased (higher lead vehicle deceleration) in both CC and ACC conditions. Crucially, BRTs were significantly delayed when driving with ACC compared to CC, confirming the negative impact of automation on reaction speed. Statistical analysis revealed no significant interaction between kinematic criticality and driving mode, providing tentative support for the looming prediction model over the lower gain model, which predicted such an interaction. However, the initial a priori model predictions overestimated BRTs, requiring parameter fitting to the empirical data. The study concludes that both models can predict BRTs for ACC driving, but the looming prediction model offers a distinct advantage: it can predict average BRTs for ACC failures using the exact same parameters fitted to manual CC driving data. This parsimony makes the looming prediction model a robust tool for assessing the safety benefits of automated driving systems. The findings highlight the importance of accounting for delayed driver responses in safety assessments and suggest that drivers rely on predictive mental models of automation rather than simply experiencing reduced arousal.

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Topics

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• braking response
• automation surprise
• situational awareness
• mental model of traffic
• vigilance
• anticipation

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: computational model, theory or model