Take the wheel: Effects of available modalities on driver intervention

Mok, Brian; Johns, Mishel; Gowda, Nikhil; Sibi, Srinath; Ju, Wendy

doi:10.1109/ivs.2016.7535567

Cited by 14 publications

(19 citation statements)

References 6 publications

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“…Selected IVIS, that is, HUD for forward collision warning, or ADAS-that is, brake assist for automatic brakingenhanced driving safety while mitigating age-related declines in critical situations (Caird et al, 2008;Haberstroh et al, 2010;Kramer et al, 2007;Mok et al, 2016). Likewise, active steering or brake assist decreases appropriate stress levels (Haberstroh et al, 2010), increases safety (Guo et al, 2015;Haberstroh et al, 2010;Reimer et al, 2016;Saito & Raksincharoensak, 2016), and increases comfort of the driving task (Mok et al, 2016). Moreover, GPS (global positioning system) with auditory instructions (vs. visual outputs) reduced cognitive workload for drivers with mild Alzheimer's disease (Yi et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Kramer, Cassavaugh, Horrey, Becic, & Mayhugh, 2007;Lester et al, 2015;D. C. Marshall, Wallace, Torner, & Birt-Leeds, 2010, 2011Mok, Johns, Gowda, Sibi, & Ju, 2016;Oh, Ko, & Ji, 2016;Reimer, Mehler, & Coughlin, 2016;Strayer, Cooper, Turrill, Coleman, & Hopman, 2016;Sullivan, Tsimhoni, & Bogard, 2008), Netherlands (n = 4) (Davidse, Hagenzieker, van Wolffelaar, & Brouwer, 2009;Dotzauer, Caljouw, de Waard, & Brouwer, 2013, Germany (n = 3) ( (Caird, Chisholm, & Lockhart, 2008), United Kingdom (n = 1) (Guo, Blythe, Edwards, Pavkova, & Brennan, 2015), France (n = 1) (Bruyas & Simões, 2010), Australia (n = 1) (Yi, Lee, Parsons, & Falkmer, 2014), and Czech Republic (n = 1) (Novotny & Bouchner, 2011).…”

Section: Study Demographicsmentioning

confidence: 99%

“…Intervention: Three studies indicated ADAS positively impact driving safety and comfort (Figure 3). Outcomes: In Mok et al (2016), drivers reported increased comfort with lane keeping assist.…”

Section: Findings Per the Picos Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Smart In-Vehicle Technologies and Older Drivers: A Scoping Review

Classen

Jeghers

Morgan-Daniel

et al. 2019

OTJR: Occupational Therapy Journal of Research

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In-vehicle technologies may decrease crash risk in drivers with age-related declines. Researchers determined the impact of in-vehicle information systems (IVIS) or advanced driving assistance systems (ADAS) on driving. Through a scoping review, the effect of IVIS or ADAS on older drivers' convenience (i.e., meets one's needs), comfort (i.e., physical or psychological ease), or safety (i.e., absence of errors or crashes) was examined. Researchers synopsized findings from 28 studies, including driving simulators and on-road environments. Findings indicated that IVIS or ADAS enhanced safety and mitigated age-related declines. Notably, IVIS may reduce cognitive workload, but may jeopardize safety if the systems are overly complicated. The ADAS enhanced safety and comfort by increasing speed control, lane maintenance, and braking responses. However, no studies addressed convenience. In-vehicle technologies may enhance safety and comfort while driving, if one's cognitive workload is not compromised. Naturalistic studies are needed to elucidate the risks and benefits of IVIS and ADAS for older drivers.

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Section: Discussionmentioning

confidence: 99%

Section: Study Demographicsmentioning

confidence: 99%

See 1 more Smart Citation

Smart In-Vehicle Technologies and Older Drivers: A Scoping Review

Classen

Jeghers

Morgan-Daniel

et al. 2019

OTJR: Occupational Therapy Journal of Research

View full text Add to dashboard Cite

show abstract

“…The identification of these probabilities can be followed by estimation and calculation. Even though there is no empirical estimate of exact probabilities, there is empirical evidence of mode confusion in semiautomated vehicles (e.g., Endsley, 2017;Mok, Johns, Gowda, Sibi, & Ju, 2016).…”

Section: Extensions and Future Workmentioning

confidence: 99%

A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles

Janssen

Boyle

Kun

et al. 2019

International Journal of Human–Computer Interaction

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A Hidden Markov Model framework is introduced to formalize the beliefs that humans may have about the mode in which a semi-automated vehicle is operating. Previous research has identified various "levels of automation," which serve to clarify the different degrees of a vehicle's automation capabilities and expected operator involvement. However, a vehicle that is designed to perform at a certain level of automation can actually operate across different modes of automation within its designated level, and its operational mode might also change over time. Confusion can arise when the user fails to understand the mode of automation that is in operation at any given time, and this potential for confusion is not captured in models that simply identify levels of automation. In contrast, the Hidden Markov Model framework provides a systematic and formal specification of mode confusion due to incorrect user beliefs. The framework aligns with theory and practice in various interdisciplinary approaches to the field of vehicle automation. Therefore, it contributes to the principled design and evaluation of automated systems and future transportation systems.

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“…We will see cars that are self-driving part of the time but must still be human-driven at other times. For comfort and safety, it is important that there is a smooth and swift transition between the self-driving and human-driving modes [10]. A particular risk is that human drivers may not be ready to take over safely, as they may be preoccupied with non-driving tasks [11,12].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

Wang

Cao

et al. 2018

IEEE/ASME Trans. Mechatron.

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In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.

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Take the wheel: Effects of available modalities on driver intervention

Cited by 14 publications

References 6 publications

Smart In-Vehicle Technologies and Older Drivers: A Scoping Review

Smart In-Vehicle Technologies and Older Drivers: A Scoping Review

A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

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