On investigating drivers’ attention allocation during partially-automated driving

Eddine, RJ; Mulatti, C; Biondi, FN · 2024 · Cognitive Research: Principles and Implications

DOI: 10.1186/s41235-024-00549-7

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Summary

This study investigates how drivers allocate visual attention during partially-automated driving (SAE Level 2), specifically addressing whether looking away from the road constitutes simple inattentional blindness or active engagement with peripheral stimuli. While automation reduces manual workload, it shifts the driver’s role to system supervisor, potentially leading to vigilance decrements. The authors sought to determine if this shift results in broader visual scanning or increased processing of distracting, driving-unrelated objects, which could impair the ability to supervise the automation and resume control when necessary. The researchers employed a within-subject experimental design with 27 participants driving a simulated vehicle in both manual and partially-automated modes. Visual attention was measured using a head-mounted eye-tracker, while cognitive engagement was assessed via a surprise two-alternative forced-choice recognition task. Participants drove on a straight highway scenario containing roadside billboards displaying brand logos. After each drive, participants completed the NASA-TLX scale for subjective mental workload and were quizzed on the presence of the billboards encountered during the drive. The study analyzed fixation metrics (number, duration, and frequency) and recognition performance, splitting data into early and late drive periods to examine temporal changes. Results indicated that participants fixated on billboards more frequently and for longer durations during partially-automated driving compared to manual driving. Specifically, the total number of fixations increased significantly, as did the total fixation duration and the number of unique billboards fixated. Crucially, recognition performance was also higher in the automated condition; participants correctly identified more billboards and showed a greater conditional probability of recognizing those they had fixated. This suggests that drivers were not merely looking away but were actively processing peripheral visual information. Additionally, the proportion of time spent fixating on driving-related areas (roadway and mirrors) dropped from 65% in manual mode to 42% in automated mode. Although fixation frequency on billboards decreased toward the end of the automated drive, recognition performance did not decline, indicating sustained attentional processing despite reduced overt visual scanning. The findings imply that partially-automated driving leads to an increased allocation of attention toward peripheral, non-driving-relevant objects. This active processing of distractions, rather than simple inattention, may be more detrimental to safety than previously assumed, as it diverts cognitive resources away from supervising the automated system. The study highlights a critical risk: while automation reduces manual workload, it may inadvertently encourage drivers to engage with distracting elements in the environment, potentially compromising their readiness to resume manual control. These results underscore the need for interface designs that mitigate such attentional shifts and maintain driver vigilance during automated operation.

Key finding

Drivers in partially-automated mode fixated on and recognized more roadside billboards than those in manual mode, indicating increased attentional processing of peripheral stimuli rather than simple inattentional blindness.

Methodology

simulator

Sample size: 27

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.

• peripheral attention
• attention allocation
• situational awareness
• inattentional change blindness
• visual
• gaze based attention detection

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model