Drivers’ Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study

Ma, Yanli; Qi, Shouming; Ya-ping, Zhang; Lian, Guan; Lu, Weixin; Chan, Ching-Yao

doi:10.3390/ijerph17155366

Cited by 19 publications

(7 citation statements)

References 40 publications

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“…From the above analysis, it can be seen that the three indicators of fixation duration, saccade duration, and saccade amplitude are all effective in characterizing the driver’s visual workload on the TSIV [ 41 ]. Therefore, these three eye movement indicators can be combined to form a comprehensive indicator of the driver’s visual workload.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Han

Wang

et al. 2022

IJERPH

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To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.

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Section: Discussionmentioning

confidence: 99%

Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Han

Wang

et al. 2022

IJERPH

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“…The duration of visual shock is generally between 0.05 s and 1.00 s at the entrance and exit of the tunnel. To quantify the visual psychological and physiological load of drivers, the converted duration of visual shock is used as an indicator to evaluate the visual load of drivers [ 7 , 13 ], to obtain the visual comfort in the entrance and exit sections, as shown in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Kim [ 12 ] found that panning is the only touch gesture that can be used without negative consequences when operating in-vehicle information systems with visual occlusion while driving. Ma [ 13 ] investigated the visual attention fixation and transition characteristics of drivers under different cognitive workloads. Bitkina [ 14 ] believed that Gaze point, duration, fixation, and pupil diameter significantly influence driving workload.…”

Section: Literature Reviewmentioning

confidence: 99%

Safety evaluation of visual load at entrance and exit of extra-long expressway tunnel based on optimized support vector regression

Shang

et al. 2022

PLoS ONE

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The traffic environment of an extra-long expressway tunnel is more complex than that of a long tunnel, which increases the driving risk. The visual load of drivers can be used to evaluate driving safety and comfort. To reveal drivers’ visual load characteristics at the entrance and exit of extra-long tunnels on mountainous expressways, this study conducted vehicle tests with 12 drivers at Gonghe extra-long tunnel on the Yu-Xiang expressway in the Wulong District. An eye tracker, non-contact multifunctional velocimetry, illuminometer, and other test equipment were used to record drivers’ pupil areas, velocity, and illuminance when entering and leaving the tunnel. The change characteristics of drivers’ pupil areas were studied. The maximum transient velocity value (MTPA) of the pupil area was selected as an index to evaluate the visual load degree. Based on velocity and illuminance coupling, a visual load model was constructed using the optimized support vector machine (GA-SVM). The influence of velocity and illuminance on the MTPA in the tunnel’s approach, entrance, exit, and departure section was analyzed. The results show that drivers’ psychological tension order at the entrance and exit is entrance section ≈ exit section > departure section > approach section. In the approach section, the visual load is mainly affected by environmental illumination. In the entrance and exit sections, the visual load is positively correlated with velocity and negatively correlated with illuminance, and velocity has a greater impact on visual load. In the tunnel departure section, the two variables synergistically influence the driving visual load. The research results provide theoretical support for the safety design and management of extra-long tunnel entrances and exits.

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“…The new results presented in this study suggest that the addition of distractors in the ADEMd enhances the assessment potential of the test concerning visual processing speed and divided and selective attention, with these abilities being relevant for proper driving [ 27 , 50 , 51 ]. This can be seen in the positive correlation encountered only in the drivers between the horizontal time with distractors (Hd adj ) and the three subtests of the UFOV (see Table 3 ).…”

Section: Discussionmentioning

confidence: 99%

Could driving help us to “see better”? A comparative assessment of saccadic efficiency, visual speed, and attention

Gené-Sampedro,

Alonso,

Gene-Morales

et al. 2024

BMC Ophthalmol

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Background This study aimed at comparing drivers’ and non-drivers’ results in the Adult Developmental Eye Movement with Distractors test (ADEMd) and the Useful Field of View test (UFOV). Methods One hundred and twenty Spaniards (mean age 50.90 ± 17.32 years) without eye disease voluntarily participated in this cross-sectional descriptive study. Participants in a single experimental session completed a questionnaire on sociodemographic, health, eyesight, and driving information. They also performed the ADEMd and UFOV tests randomly following standardized protocols. The ADEMd is a visual-verbal test that measures saccadic efficiency and visual attention. Brown-Forsythe (B–F) tests with Games-Howell post-hoc adjustments were conducted to assess differences between groups. Groups were formed according to sex, age (young adults, adults, and older adults), and driver/non-driver for further analysis. Additionally, associations between dependent variables were assessed through Spearman’s correlations. Results Drivers obtained significantly better results in the ADEMd compared with non-drivers. Non-significant differences between drivers and non-drivers were encountered in the UFOV. Additionally, significant differences were observed between sexes and age groups. It is worth highlighting that non-driver’s age significantly correlated with worse ADEMd performance (rho = .637 to .716). This correlation was non-significant in drivers. Similarly, reading hours significantly correlated with better ADEMd performance in non-drivers (rho = − .291 to − .363), but not in drivers. The only significant correlations between ADEMd and UFOV tests were found in drivers (rho = .307 to .410). Conclusion Considering all the discussed results, it could be hypothesized that the driving task promotes abilities, such as oculomotor and cognitive function, which are relevant for the performance in the ADEMd. However, this hypothesis is based on correlational outcomes and further studies should causally assess this possible relation.

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Drivers’ Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study

Cited by 19 publications

References 40 publications

Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Safety evaluation of visual load at entrance and exit of extra-long expressway tunnel based on optimized support vector regression

Could driving help us to “see better”? A comparative assessment of saccadic efficiency, visual speed, and attention

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