The Effect of Urgency of Take-Over Requests During Highly Automated Driving Under Distraction Conditions

Naujoks, Frederik; Mai, Christoph; Neukum, Alexandra · 2021 · Crossref

DOI: 10.54941/ahfe100646

archive: archived pipeline: cataloged verified

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Summary

This study investigates the effectiveness of different take-over request (TOR) modalities during highly automated driving, specifically examining how visual versus visual-auditory alerts influence driver reaction times and vehicle control when drivers are distracted. The research addresses a critical safety challenge in automated vehicles: ensuring a safe transition from automated to manual control when system limits are reached, such as missing lane markings. The authors aim to determine if urgent, multi-modal warnings improve driver performance compared to purely visual notifications, particularly under conditions where drivers are engaged in secondary tasks. The experiment utilized a motion-based driving simulator with 16 participants who were randomly assigned to receive either visual or visual-auditory TORs. Participants drove in a highly automated mode, allowing them to engage in a secondary task (reading news articles) while the system handled longitudinal and lateral control. The study employed a mixed between-within subject design, varying the TOR modality as a between-subjects factor and the driving scenario as a within-subjects factor. Three scenarios of increasing difficulty were tested: an "easy" scenario involving missing lane markings on a straight road, a "moderate" scenario requiring a lane change due to temporary work zone lines, and a "difficult" scenario involving loss of lane markings on a high-curvature road. The visual TOR consisted of a flashing symbol on the center console, while the visual-auditory condition added a 1-second, 1000 Hz sinus tone. The results demonstrate that visual-auditory TORs significantly improved driver performance compared to purely visual requests. Drivers in the visual-auditory group exhibited shorter hands-on reaction times (mean of 2.29 seconds) compared to the visual group (mean of 6.19 seconds). Additionally, the visual-auditory group showed better lateral vehicle control, evidenced by lower maximum lateral deviations from the lane center and reduced standard deviation of lane position. While the main effect of modality was significant across all scenarios, the differences were most pronounced in the moderate and difficult situations. In the easy scenario, no significant differences were found between the two modalities. Notably, two drivers in the visual condition failed to take over control in time during the high-curvature scenario, resulting in lane changes, whereas no such failures occurred in the visual-auditory group. The study concludes that purely visual take-over requests may be insufficient for ensuring safe transitions from automated to manual driving, especially when drivers are distracted or facing complex driving situations. The addition of auditory cues provides a significant advantage in alerting drivers and maintaining lane control. However, the authors caution that the specific design of the visual display (center console) may limit generalizability, suggesting that other interfaces like Head-Up Displays might yield different results. The findings highlight the need to balance the urgency of warnings with the risk of false alarms, which could reduce user acceptance. Future research should explore the interplay between TOR design, scenario difficulty, and driver state to optimize safety and usability in highly automated vehicles.

Key finding

Visual-auditory take-over requests result in significantly shorter driver reaction times and improved lateral vehicle control compared to purely visual requests during highly automated driving.

Methodology

simulator

Sample size: 16

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• takeover transitions
• automation
• manual
• visual manual
• temporal
• automation surprise

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol
• Theoretical Contribution: conceptual framework