Highly automated driving: the role of visuo-attentional and executive abilities in take-over success

Schnebelen, Damien; Mars, Franck; Charron, Camilo; Mecheri, Sami; Lobjois, Régis · 2025 · Frontiers in Psychology

DOI: 10.3389/fpsyg.2025.1685223

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Summary

This study investigates the role of individual visuo-attentional and executive abilities in determining the success of take-over maneuvers during highly automated driving (SAE Level 3). While previous research has focused on exogenous factors like traffic density or alert modality, this work addresses a gap in understanding endogenous, individual-specific characteristics that influence a driver’s ability to regain control safely. The primary objective was to determine whether cognitive and perceptual profiles could predict the likelihood of collision during a critical take-over scenario. The experimental design involved 118 participants aged 20 to 60 who completed a battery of assessments in a driving simulator. Participants first underwent a period of automated driving while engaged in a secondary reading task. They were then presented with a critical take-over request requiring them to avoid a stationary obstacle by changing lanes while two other vehicles approached at higher speeds. Success was defined as completing the maneuver without collision. To assess individual differences, participants completed three visuo-attentional tests (visuomanual coordination, multiple object avoidance, and multiple object tracking) and six cognitive tests measuring executive functions, including working memory (Corsi and N-back tasks), inhibitory control (Flanker and Stop-Signal tasks), cognitive flexibility (Trail Making Test), and spatial planning (Tower of London). Driving experience and age were also recorded. The researchers used partial least-squares regression models to predict take-over success based on these variables. The results identified spatial working memory, visuomanual coordination, and driving experience as the strongest predictors of successful take-over performance. The most accurate predictive model achieved an accuracy of 70.79%. Specifically, higher performance on the Corsi task (spatial working memory) and the visuomanual coordination task correlated with a lower risk of collision. Conversely, higher inhibitory control ability, measured by the Flanker and Stop-Signal tasks, was negatively related to take-over success. The authors suggest that individuals with strong inhibitory control may have exerted excessive cognitive control over the secondary reading task during automated driving, which reduced their cognitive flexibility and impaired their ability to rapidly re-engage with the driving environment when the take-over request occurred. These findings highlight that take-over success is not merely a function of reaction time but depends heavily on specific cognitive and perceptual resources. The study underscores the importance of spatial working memory and sensorimotor coordination in managing complex, dynamic driving transitions. Furthermore, the counterintuitive finding regarding inhibitory control suggests that rigid cognitive engagement with non-driving tasks can be detrimental during automation handovers. This research implies that future human-machine interface designs and driver selection criteria should account for these specific cognitive profiles to enhance safety in highly automated vehicles.

Key finding

Takeover success in highly automated driving is best predicted by spatial working memory, visuomanual tracking ability, and annual mileage, while strong inhibitory control paradoxically reduces takeover success, likely because it locks attention onto the secondary task.

Methodology

simulator

Sample size: 118

Provenance

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Topics

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• takeover transitions
• cognitive capacity variation
• situational awareness
• useful field of view
• automation
• manual

Information type

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