The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior

Faure, Vérane; Lobjois, Régis; Benguigui, Nicolas

doi:10.1016/j.trf.2016.04.007

Cited by 130 publications

(67 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Means for all the cases are shown in Table I. This includes the data that was recorded and the normalised data via the use of Equation 4. Results of the raw data, see Figure 11, show that the methods elicited results that for visuals (Gexp), on average, would be on the scale of similar to very similar (50 -75) for WH and GI and on the scale of slightly similar to similar (25 -50) for RC (see Figure 4).…”

Section: Resultsmentioning

confidence: 99%

“…In particular, driving simulators are frequently used to study the behaviour of drivers under distinct scenarios which would be difficult to replicate in the real world [2]- [4]. In order to fully exploit VEs, a high level of confidence is needed to ensure that decisions taken in the virtual world are equivalent to those that would be taken in the real world scenario being considered.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Subjective Evaluation of High-Fidelity Virtual Environments for Driving Simulations

Debattista

Bashford-Rogers

Harvey

et al. 2018

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subjective Evaluation of High-Fidelity Virtual Environments for Driving Simulations

Debattista

Bashford-Rogers

Harvey

et al. 2018

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

“…Unpredicted complexity (e.g. traffic jam, trouble machine) also leads the pressure in time management [22][23]. Drivers need to estimate a traffic jam, distance, fuel, and budget.…”

Section: Subjective Analysismentioning

confidence: 99%

“…[25] and [18] has supported that drivers need to focus to operate the vehicle and concentrate on the road such as a sign, lamp, announcement, etc. In addition, the brake is not lightly handled and traffic jam rises fatigue and physical demand [14,18,23]. Additionally, drivers' frustration originates from feelings of uncertainty which stems from a sense of inability to fulfill the need.…”

Section: Subjective Analysismentioning

confidence: 99%

A Comparative Analysis of Mental Workload between Train and Bus Drivers

Soewardi

Putra

2018

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. Human error is one of the crucial problems in land transport, sea transport, and air transport that inflicting some undesirable occurrence such as delay, cancellation, or accident. This incident causes inconvenience and apprehension for passengers. Hence, drivers should avoid some errors in driving by improving alertness and reducing the excessive workload. This study presents a comparative study about mental workload between train and bus drivers based on subjective and objective criteria. NASA-TLX method is used to measure subjective variables by distributing questionnaires. While the content of a drivers' Salivary α-amylase (SAA) is measured as an objective parameter before and after driving. 30 drivers are participated in this study who have experience for more than 3 years. Statistical analysis is conducted to test the hypothesis for a difference. The result of this study shows that bus drivers' SAA level is significantly higher (Asymp. Sig. 0.49) than train drivers for both before (91.99 kU/l) and after (83.19 kU/l) driving activity. Similarly, weighted workload subjectively of bus drivers' mental workload (1051) is higher than train drivers (1031) insignificantly (Asymp. Sig. 0.561).

show abstract

“…Our study concentrates on the oculometrics of cognitive load, specifically the metrics of pupillary dilations (Marandi, Madeleine, Omland, Vuillerme, & Samani, ), blinks (Faure, Lobjois, & Benguigui, ), and eye movements (Vrij et al, ), which were explored in an interview setting.…”

Section: Introductionmentioning

confidence: 99%

Assessing cognitive load using oculometrics to identify deceit during interviews

Mitre‐Hernandez

Sánchez-Rodríguez

Zataraín-Cabada

et al. 2018

Applied Cognitive Psychology

View full text Add to dashboard Cite

Lying requires a long-term memory search and is a cognitive load task. Telling a spontaneous lie is associated with a higher cognitive load compared with the truth, although when memories are planned before telling a lie, the cognitive load can be less compared with the truth; therefore, telling a spontaneous lie could be associated with a higher cognitive load compared with a planned lie. In this study, we examined cognitive load, as measured through oculometrics, to identify deceit in interviews. Twenty-four subjects were questioned, and their answers classified as spontaneous lies, planned lies, and truths. Results show that saccades and fixations were associated with a higher cognitive load when telling spontaneous lies compared with telling truths. Blinks and pupillometry show that subjects require greater cognitive load when telling truths compared with planned lies. Finally, telling spontaneous lies required higher cognitive load compared with planned lies, as assessed by blinks.

show abstract

The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior

Cited by 130 publications

References 47 publications

Subjective Evaluation of High-Fidelity Virtual Environments for Driving Simulations

Subjective Evaluation of High-Fidelity Virtual Environments for Driving Simulations

A Comparative Analysis of Mental Workload between Train and Bus Drivers

Assessing cognitive load using oculometrics to identify deceit during interviews

Contact Info

Product

Resources

About