Drowsy and Fatigued Driving Problem Significance and Detection Based on Driver Control Functions

Eskandarian, Azim; Mortazavi, A; Sayed, Riaz Akbar

doi:10.1007/978-0-85729-085-4_36

Cited by 8 publications

(11 citation statements)

References 30 publications

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“…Evidence suggests that drivers will be even more prone to falling asleep behind the wheel with a higher automation level [5]. Conventional driver state monitoring systems are mainly based on analysing the driving behaviour or eye blink behaviour of the driver [6]. Both approaches are not applicable for detecting sleep in a driver during automated driving periods, because the driver does not necessarily have to gaze on the road and can even close her or his eyes while the automation is active.…”

Section: Introductionmentioning

confidence: 99%

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Wörle

Metz

Thiele

et al. 2019

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Wörle

Metz

Thiele

et al. 2019

IET Intelligent Transport Systems

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“…Samiee et al [24] evaluated the arousal level classified as being alert or drowsy using vehicle dynamic data such as vehicle longitudinal position and duration of eye closures, and showed that the proposed method could differentiate between the alert and the drowsy states with an accuracy of more than 87.78%. Eskandarian et al [25] and McDonald et al [26] indicated the effectiveness of such vehicle-based measures for assessing drowsiness.…”

Section: Prediction Of Point In Time With High Crash Risk By Integratmentioning

confidence: 99%

“…The lower value of EEG-MPF shows that the arousal level is decreased. EEG-α/β can be calculated as the ration of the sum of α-band (8-12 Hz) power and θ-band (4-7 Hz) power to the power of β-band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) power. The higher this value is, the lower the arousal level gets.…”

Section: Calculation Of the Integrated Posterior Probabilities P(h 1 mentioning

confidence: 99%

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Murata¹,

Uragami²

2018

JTTE

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Abstract:The aim of this study was to predict drivers' drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG (Electroencephalography) (EEG-MPF and EEG-α/β), ECG (Electrocradiogram) (RRV3), tracking error, and subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

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“…Drowsiness has been shown to undermine driver performance in controlled settings, such as a driving simulator (Contardi, Pizza, Sancisi, Mondini, & Cirignotta, 2004; Thiffault & Bergeron, 2003; Turkington, Sircar, Allgar, & Elliott, 2001; Yuan, Du, Qu, Zhao, & Zhang, 2016). Drowsy drivers have delayed sensory processing ability and perception, need longer periods to react to external stimuli, and have substantially degraded ability to control their vehicle (Eskandarian, Mortazavi, & Sayed, 2012). In understanding the effects of drowsiness on vehicle control, steering wheel movements have emerged as one of the more sensitive measures of the effect of drowsiness on driving behavior (Krajewski & Sommer, 2009; Anthony D McDonald, Lee, Schwarz, & Brown, 2018; Anthony D. McDonald, Lee, Schwarz, & Brown, 2013).…”

Section: Introductionmentioning

confidence: 99%

Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea

McLaurin

Lee

McDonald

et al. 2018

Transportation Research Part F: Traffic Psychology and Behaviou

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One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models are used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to “driving words.” The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.

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Drowsy and Fatigued Driving Problem Significance and Detection Based on Driver Control Functions

Cited by 8 publications

References 30 publications

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Detecting sleep in drivers during highly automated driving: the potential of physiological parameters

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea

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