Driver's adaptive glance behavior to in-vehicle information systems

Peng, Yufeng; Boyle, Linda Ng

doi:10.1016/j.aap.2015.08.002

Cited by 27 publications

(10 citation statements)

References 26 publications

(28 reference statements)

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“…Few studies have analysed adaptations in driver behaviour capturing the impact of several explanatory factors and interdependencies between repeated observations over time for the same subject. For this purpose, recent studies have proposed linear mixed-effects models for repeated measures, which can accommodate both fixed and random effects capturing complex error structures (Peng, Boyle, and Lee 2014;Peng and Boyle 2015;Oviedo-Trespalacios et al 2017;Wang et al 2017;Geden, Staicu, and Feng 2018;Saad, Abdel-Aty, and Lee 2018;Albert 2019). Linear mixed-effects models allow to define explicitly a hierarchical structure (e.g.…”

Section: Statistical Analysis Methods For Adaptations In Driver Behavmentioning

confidence: 99%

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto

Farah

Bogenberger

et al. 2020

Transportmetrica A: Transport Science

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Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of these adaptations. This research aims at quantifying the adaptations in speed, acceleration, distance headway and relative speed when drivers resume manual control. We collected driver behaviour data in an on-road experiment with fullrange ACC during peak hours in Munich. We analysed these data using linear mixed-effects models to identify statistically significant changes in driver behaviour characteristics after drivers resumed manual control (transition period). The results reveal that the speed decreased significantly after the system was deactivated and it increased significantly after the system was overruled by pressing the gas pedal. These adaptations might have a substantial impact on traffic efficiency and safety.

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Section: Statistical Analysis Methods For Adaptations In Driver Behavmentioning

confidence: 99%

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto

Farah

Bogenberger

et al. 2020

Transportmetrica A: Transport Science

View full text Add to dashboard Cite

show abstract

“…However, drivers also appeared to adapt their behavior and to compensate the risks of visually distracting activities by reducing speed (Engström, Johansson, & Östlund, 2005) or increasing their time headway to preceding vehicles (Kaber, Liang, Zhang, Rogers, & Gangakhedkar, 2012). Peng and Boyle (2015), nevertheless, have revealed that these behavioral adaptations decrease over time as drivers acclimate to the task.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Gesture-Based In-Vehicle Interaction: User Experience and the Potential to Reduce Driver Distraction

Graichen

Krems

2019

Hum Factors

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Objective: We observe the effects of in-vehicle system gesture-based interaction versus touch-based interaction on driver distraction and user experience. Background: Driver distraction is a major problem for traffic safety, as it is a contributing factor to a number of accidents. Visual distraction in particular has a highly negative impact on the driver. One possibility for reducing visual driver distraction is to use new forms of interaction in the vehicle, such as gesture-based interaction. Method: In this experiment, participants drove on a motorway or in a city scenario while using touch-based interaction or gesture-based interaction. Subjective data, such as acceptance and workload, and objective data, including glance behavior, were gathered. Results: As a result, participants rated their subjective impressions of safe driving as higher when using gesture-based interaction. More specifically, acceptance and attractiveness were higher, and workload was lower. The participants performed significantly fewer glances to the display and the glances were much shorter. Conclusion: Gestures are a positive alternative for in-vehicle interaction since effects on driver distraction are less significant when compared to touch-based interaction. Application: Potential application of this research includes interaction design of typical in-vehicle information and entertainment functions.

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“…In [132], an intelligent notification system is developed to provide an Intelligent Callback Reminder service, where incremental naive Bayes is utilized to understand the driver's situation for providing callback reminder at a right time. It is found that text entry tasks tend to increase glance duration whereas text reading tasks do not, and random coefficient models can reliably estimate individual performance when significant differences exist among different drivers [133]. These two findings are able to guide the design of personalized in-vehicle technologies.…”

Section: Notification Servicesmentioning

confidence: 84%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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Driver's adaptive glance behavior to in-vehicle information systems

Cited by 27 publications

References 26 publications

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Evaluation of Gesture-Based In-Vehicle Interaction: User Experience and the Potential to Reduce Driver Distraction

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

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