Multitasking Driver Cognitive Behavior Modeling

Liu, Yanfei; Wu, Zhaohui

doi:10.1109/is.2006.348393

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The ACT-R method was mainly used to model driving behavior (primary task) such as lateral movement, steering, and NDRT performance (Table 4). Other dependent variables in driving behavior category included reaction time (Liu & Wu, 2007), hand/foot movement time (Cao et al, 2014; Scharfe & Russwinkel, 2019), and driver’s urgent behavior frequency (Liu & Wu, 2005). ACT-R was also used to model driver attention allocation responses such as detection error (Salvucci, 2009).…”

Section: Resultsmentioning

confidence: 99%

“…There was no study that used ACT-R to model driver’s cognitive workload (Table 4). In the “NA” category, there were some studies that compared the model performance with empirical data (Khosroshahi et al, 2016; Liu & Wu, 2007; Teo & Grace, 2018). Unlike GOMS, ACT-R was used to model autonomous driving related measures such as takeover reaction time (Scharfe & Russwinkel, 2019, 2020).…”

Section: Resultsmentioning

confidence: 99%

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A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Park

Zahabi

2022

Hum Factors

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Objective This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. Background HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. Method A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. Results The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. Conclusion The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. Application The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

Park

Zahabi

2022

Hum Factors

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“…These were artificial intelligence programs that analyzed flight data including sensor reports on nearby aircraft, and issued alerts to the pilot when necessary. Multi-tasking in driving was first studied as switching between driving tasks (gaze, lane change) in ACT-R [24]. It was extended to vehicle steering and map reading in QN-MHP [47].…”

Section: Existing Models and Related Workmentioning

confidence: 99%

Using tactile displays to maintain situational awareness during driving

Howard

Sundareswara

Daily

et al. 2013

2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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We present a system to reduce the decay of situational awareness when the driver of a vehicle is distracted by a secondary task like texting. According to Wickens' Multiple Resource Theory, the driver can attend to the under-used tactual modality in parallel with the preoccupied visual modality. Charron and Koechlin found that under dual-tasking conditions, the executive function of each task is located in a different part of frontal cortex. We expand on that theory based on brain anatomy, and propose that these two areas are both active but with different qualities of access to cognitive resources. Based on these theories, we employ tactile arrays to represent the local traffic situation to the driving task, when the driver is occupied with a distracting secondary task. We describe our cognitive model and the design of the tactile display, and show results of predictions of time to return to vigilant driving from a secondary task with and without the tactual modality inputs.

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