The Impact of N-back-Induced Mental Workload and Time Budget on Takeover Performance

Goodridge, Courtney Michael; Gonçalves, Rafael C.; Arabian, Ali; Horrobin, Anthony; Solernou, Albert; Lee, Yee Thung; Bruneau, Audrey; Lee, Yee Mun; Merat, Natasha · 2025 · Crossref

DOI: 10.31234/osf.io/dpe47_v3

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Summary

This study investigates the impact of mental workload (MWL) and time budget on driver takeover performance from a hands-off Level 2 (L2) automated driving system. While previous research has established that smaller time budgets (higher criticality) generally lead to faster but more erratic responses, the effect of pure cognitive MWL on takeover quality remains inconsistent. The authors aim to determine whether high MWL, induced by a secondary task, interacts with time budget to impair driver performance, specifically testing the Cognitive Control Hypothesis which suggests that cognitively demanding tasks impair subtasks requiring cognitive control while leaving automatised behaviours unaffected. The experiment employed a 2x2 repeated-measures design with 37 participants using a high-fidelity motion-based driving simulator. Drivers engaged in hands-off L2 driving under two MWL conditions: a baseline monitoring condition and a high-load condition involving a verbal 2-back task. Takeover requests were triggered by lead vehicle deceleration, creating rear-end conflict scenarios with either a short time budget (Time to Collision [TTC] = 3 s) or a long time budget (TTC = 5 s). The study utilized Bayesian multilevel modelling to analyze response timing, vehicle control quality (lateral acceleration, jerk, lane position), and gaze behaviour. This approach allowed for precise estimation of effect sizes and uncertainty, addressing limitations in prior studies that relied on less robust statistical methods. The results indicated that 2-back-induced MWL had no consistent or substantial impact on the timing or quality of takeovers. Drivers performed similarly in terms of response time and vehicle control stability regardless of whether they were engaged in the high-load cognitive task. In contrast, time budget significantly influenced performance: drivers responded faster to events with smaller time budgets (3 s TTC) but exhibited more erratic post-takeover lateral control compared to those with larger time budgets (5 s TTC). No significant interaction was found between MWL and time budget, suggesting that the detrimental effects of high criticality were not exacerbated by cognitive load in this specific scenario. The authors conclude that rear-end collision avoidance may rely on automatised behaviours that do not depend heavily on the cognitive resources depleted by the 2-back task. Consequently, pure cognitive MWL did not impair performance in these scenarios. The findings suggest that the Cognitive Control Hypothesis may apply more strongly to takeover scenarios requiring complex decision-making, such as lane changes or hazard perception, rather than simple longitudinal braking responses. This implies that safety systems and driver monitoring strategies should prioritize assessing readiness for cognitively demanding manoeuvres rather than assuming general cognitive impairment affects all takeover types equally.

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Topics

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• takeover transitions
• mental demand
• temporal
• workload measurement
• manual
• automation

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol
• Theoretical Contribution: theory or model