Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

Lee, Seul Chan; Yoon, Sol Hee; Ji, Yong Gu

doi:10.1016/j.ergon.2019.06.001

Cited by 17 publications

(3 citation statements)

References 16 publications

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“…Assessing the driver's workload is still a challenging task and a variety of methods and data sources like physiological data, eye-tracking but also kinematic data are explored [15,27,32,34,39]. Multiple approaches tackle the task of predicting task completion times [11,16,22,35], as well as visual demand [20,[28][29][30] to assess, already in early development stages, how demanding the interaction with the in-vehicle touchscreen is. However, most of the current approaches are based on questionnaires, explicit user observation, or performance-related measurements recorded during lab experiments or small-scale naturalistic driving studies.…”

Section: User Behaviour Evaluation Of Touchscreen-based Ivissmentioning

confidence: 99%

Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems

Ebel

Lingenfelder

Vogelsang

2021

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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With modern In-Vehicle Information Systems (IVISs) becoming more capable and complex than ever, their evaluation becomes increasingly difficult. The analysis of large amounts of user behavior data can help to cope with this complexity and can support UX experts in designing IVISs that serve customer needs and are safe to operate while driving. We, therefore, propose a Multi-level User Behavior Visualization Framework providing effective visualizations of user behavior data that is collected via telematics from production vehicles. Our approach visualizes user behavior data on three different levels: (1) The Task Level View aggregates event sequence data generated through touchscreen interactions to visualize user flows. (2) The Flow Level View allows comparing the individual flows based on a chosen metric. (3) The Sequence Level View provides detailed insights into touch interactions, glance, and driving behavior. Our case study proves that UX experts consider our approach a useful addition to their design process. CCS CONCEPTS• Human-centered computing → Visualization systems and tools; Systems and tools for interaction design; • Applied computing → Transportation.

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Section: User Behaviour Evaluation Of Touchscreen-based Ivissmentioning

confidence: 99%

Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems

Ebel

Lingenfelder

Vogelsang

2021

13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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“…However, due to the cumulative and linear characteristics of KLM method, prediction on single-glance duration was not available. Another recent study added new rules to KLM when drivers searched an item from an in-vehicle display (Lee et al, 2019), which resulted a fairly high correlation between the model estimates and human-subject data.…”

Section: Discussionmentioning

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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“…With the development of automobile technology, it appears to be a normal configuration for automobiles to be equipped with varied in-vehicle information systems (IVIS), such as advanced driver assistance systems, in-vehicle information and entertainment systems, and smart driving systems [7][8][9][10]. Nowadays, while an increasing number of IVIS have been integrated with smart devices and sensors to deliver entertainment and information services through audio interfaces and allow for voice commands, their introduction is also likely to increase MWL on drivers as well, potentially resulting in detrimental effects on driving performance [11]. While interacting with an IVIS, drivers rely heavily on visual and auditory channels to receive entertainment and information services [7].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Huang,

Zhang,

Zhang

et al. 2024

Sensors

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Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers’ mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers’ mental states.

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Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

Cited by 17 publications

References 16 publications

Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems

Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems

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

Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

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