Dimensional Evolution of Intelligent Cars Human-Machine Interface considering Take-Over Performance and Drivers’ Perception on Urban Roads

Yang, Hao; Wang, Yueran; Jia, Ruoyu

doi:10.1155/2020/6519236

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…The study was oriented to typical dashboard layouts and used a simple driving simulator to conduct experiments ( Figure 1 ). The simulator was commonly used in driving schools in China, and existing research showed that it could effectively collect people's driving behaviors ( 13 ). Interaction efficiency, eye movement characteristics, mental stress changes and system usability were collected.…”

Section: Methodsmentioning

confidence: 99%

“…For example, from the perspective of take-over performance in intelligent vehicles, the average reacting time of older drivers was at least 1.2 s longer than that of young drivers ( 12 ). The in-vehicle HMI dimensions had a significant impact on drivers' task completion time ( 13 ), but a reasonable layout of the dashboard also played an important role in the driver's recognition efficiency. However, at present, people tended to just study the shape and character encoding of instrument panels ( 14 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Dashboard Layout Effects on Drivers' Searching Performance and Heart Rate: Experimental Investigation and Prediction

Yang

Wang

Jia

2022

Front. Public Health

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Carsharing scale has been increasing rapidly with sharing economy. However, many users are reluctant to rent cars any longer due to the low-quality of interactive experience and usability, especially in terms of the dashboard design. This challenge should be urgently addressed in order to maintain the sustainable development of car-sharing industry and its environmental benefits. This study aims to investigate the relationship between users' driving activities (e.g., searching time, reading time, eye movement, heart rate) and dashboard layout. This study was conducted based on the experimental investigation among 58 respondents who were required to complete driving tasks in four types of cars with different dashboard layouts. Afterwards, a prediction model was developed to predict users heart rate (HR) based on the long short-term memory model, and logistic models were used to examine the relationship between the occurrence probability of minimum HR and dashboard reading. The results showed that the system usability of a dashboard was related to the drivers' eye movement characteristics including fixation duration, fixation times and pupil diameter. Most indicators had significant effects (p < 0.05) on the system usability score of corresponding dashboard. The long short-term memory model network (RMSE = 1.105, MAE = 0.009) was capable of predicting heart rate (HR) that happened in the process of instrument reading, which presented a periodic pattern rather than a continuous increase or decrease. It reflected that the network could better fit the non-linear and time-sequential laws of HR data. Furthermore, the probability of the lowest heart rate occurrence during the interaction with four dashboards was influenced by the average searching time, reading time and reading accuracy that were related to a specific layout. Overall, this study provided a theoretical reference for uncovering users' adaptive behaviors with the central control screen and for the optimal choice of a suitable dashboard layout in interface design.

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Section: Methodsmentioning

confidence: 99%