Monitoring driver cognitive load using functional near infrared spectroscopy in partially autonomous cars

Sibi, Srinath; Ayaz, Hasan; Kuhns, David; Sirkin, David; Ju, Wendy

doi:10.1109/ivs.2016.7535420

Cited by 39 publications

(36 citation statements)

References 28 publications

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“…Most of the previous fNIRS related workload studies in the transportation domain (Kojima et al, 2005; Tomioka et al, 2009; Ayaz et al, 2012; Gateau et al, 2015; Foy et al, 2016; Sibi et al, 2016) focused only on frontal brain activations, primarily due to the ease in preparing the optodes around the forehead. On comparing the multivariate predictions from the whole head measurements with only the 12 frontal channels around the forehead, the mean Pearson correlation dropped by 37.7%.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

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Section: Discussionmentioning

confidence: 99%

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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“…With the recent trend towards semi‐autonomous vehicles, a particular challenge is to determine opportune times to adjust the level of automation due to user preference, critical events, or failures of automation. In a pilot study, we observed that autonomous driving conditions reduced fNIRS workload conditions relative to manual driving while a variety of secondary tasks imposed additional demands even in the autonomous state (Sibi, Ayaz, Kuhns, Sirkin, & Ju, ), similarly to reductions observed in cruise control (Tsunashima & Yanagisawa, ). Assessment of cognitive workload may help autonomous vehicles detect distraction and identify safe ways to restore manual control.…”

Section: Towards Real‐time Monitoring Of Individuals and Interactionmentioning

confidence: 81%

The Age of Neuroergonomics: Towards Ubiquitous and Continuous Measurement of Brain Function with fNIRS

Curtin

Ayaz

2018

Jpn Psychol Res

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Neuroergonomics is an emerging field that investigates the human brain in relation to behavioral performance in natural environments and everyday settings. Functional near-infrared spectroscopy (fNIRS), a noninvasive brain-monitoring technology that relies on optical techniques to detect changes of cortical hemodynamic responses to human perceptual, cognitive, and motor functioning, is an ideal candidate tool. Ultraportable wearable and wireless fNIRS sensors are already breaking the limitations of traditional neuroimaging approaches that have imposed limitations on experimental protocols, data-collection settings, and task conditions at the expense of ecological validity. This review summarizes emerging trends for fNIRS applications, from aerospace to medicine, with diverse populations and towards clinical solutions. We will review recent studies, such as mental workload assessment of specialized operators performing standardized and complex cognitive tasks and development of expertise during practice of complex cognitive and visuomotor tasks (ranging from aircraft piloting and robot control). Various recent synergistic fNIRS applications for human-human and human-machine interaction, including synthetic speech perception, interpersonal neural synchronization, and brain computer interfaces, highlight the potential use and are ushering the dawn of a new age in applied neuroscience and neuroengineering.Key words: functional near-infrared spectroscopy (fNIRS), neuroadaptive systems, human-machine interaction, mobile brain/body imaging (MoBI), brain-computer interface (BCI).

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“…The transition from manual to automated driving and vice versa is prone to fail in the stateof-the-art. Recent research showed that drivers exhibit low cognitive load when monitoring automated driving compared to doing a secondary task [284]. Even though some experimental systems can recognize driver-activity with a driver facing camera based on head and eye-tracking [285], and prepare the driver for handover with visual and auditory cues [286] in simulation environments, a real world system with an efficient handover interaction module does not exist at the moment.…”

Section: Human Machine Interfacementioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Monitoring driver cognitive load using functional near infrared spectroscopy in partially autonomous cars

Cited by 39 publications

References 28 publications

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

The Age of Neuroergonomics: Towards Ubiquitous and Continuous Measurement of Brain Function with fNIRS

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

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