Mathematical Modeling of the Effects of Speech Warning Characteristics on Human Performance and Its Application in Transportation Cyberphysical Systems

Zhang, Yiqi; Wu, Changxu; Wan, Jingyan

doi:10.1109/tits.2016.2539975

Cited by 24 publications

(10 citation statements)

References 63 publications

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“…Independent variables in QN family of models were more diverse than GOMS and ACT-R. These variables included anthropometry parameters (e.g., angular directions of fingers touching an in-vehicle display) (Jeong, 2018), weather conditions (Deng, Wu, et al, 2019), different in-vehicle monitor positions (Fuller, 2010), emotion status (Sanghavi, 2020), different warning styles (Zhang et al, 2016), and gender (Tsimhoni, 2004). Table 2 summarizes the distributions of variables in QN studies.…”

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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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%

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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“…In previous studies [1], [2], have been demonstrated that a poorly designed alert can be worse than if no alert is emitted because of the distraction it will produce, in [3] we used electroencephalography to detect driver concentration, and we found that some drivers got distracted due to ADAS Alerts.…”

Section: Introductionmentioning

confidence: 85%

A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions

et al. 2017

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Abstract. In this work is presented a novel approach for passive safety in vehicles by Advanced Driver Assistance Systems (ADAS) alert emission in 360° around driver to notify about hazards nearby the vehicle depending on the actions taken by driver per the context. This proposal would create a more robust system compared to current passive ADAS systems since the feedback to driver is in the same direction that hazard is detected (Punctual Sound Source Alert), compared with most assistance systems that emits sounds from the monitor or from the dashboard provoking distractions when emits alerts unnecessarily. The increase of security by this method will allow the driver to be aware of their surroundings even in a very quiet cabin or in a noisy environment. Also, it would detect the steering wheel angle, speed of movement and the activation of turning lights among other alerts, which would allow us to define a critical action during driving; apart from using sensors and cameras aimed at the driver to detect patterns of movement during these critical actions and have a prediction of a possible turn or manoeuvre when driving, refer to Figure 1. It will be necessary a reconfiguration of the alert in frequency, time of action depending upon the level of risk to prevent an accident or to reduce the consequences in an imminent accident.

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“…This thrust of work mainly focuses on how the design of vehicle technology, such as advanced driver-assistance systems (ADAS), connected vehicles (CVs), and automated vehicles (AVs), influences driver behavior. My first modeling work modeled driver reaction time and error rates with different acoustic and semantic features of auditory warnings in connected vehicle systems (Zhang, Wu, & Wan, 2016). In a more recent related work, I developed a cognitive computational driver model to predict the effects of key warning parameters, including warning lead time, warning reliability, and warning styles, on driver response performance (Zhang, Wu, Qiao, Sadek, & Hulme, 2022).…”

Section: Yiqi Zhang Penn State Universitymentioning

confidence: 99%

“Dummies, Learning Modeling Made Easy”: Improving Modeling Education in Human Factors Research

Jeon

Zhang

Bao

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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This panel introduces the current practice of human performance modeling and its applications to Human Factors research and discusses how we can promote modeling education. We argue that there is a necessity for new and more engaging learning tools and environments that can include qualitative and quantitative modeling work in Human Factors curricula to fulfill different levels of approaches in Human Factors research. We hope that this panel can encourage more students and junior researchers to utilize modeling in their work and spark exciting discussions for better modeling practice and education.

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Mathematical Modeling of the Effects of Speech Warning Characteristics on Human Performance and Its Application in Transportation Cyberphysical Systems

Cited by 24 publications

References 63 publications

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

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

A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions

“Dummies, Learning Modeling Made Easy”: Improving Modeling Education in Human Factors Research

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