Influence of Passive Fatigue and Take-Over Request Lead Time on Drivers’ Take-Over Performance

Hadi, Ali Muhammad; Li, Qingkun; Wang, Wenjun; Yuan, Quan; Cheng, Bo

doi:10.1007/978-3-030-50943-9_32

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…This issue was already introduced by Liu and Green (2017), albeit only in forward collision warning. During transitions of control, although longer time-budget allows for a higher situational awareness Gold et al 2016;Clark and Feng 2017;Hadi et al 2020), Zeeb et al (2016 argued that drivers' response times are not representative of the quality of the take-over process. Nevertheless, following the above definitions, depending on the adopted instrumentation and metric, the start and end of both take-over and handover processes are defined.…”

Section: Further Consideration and Future Challengesmentioning

confidence: 99%

When terminology hinders research: the colloquialisms of transitions of control in automated driving

et al. 2022

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During the last 20 years, technological advancement and economic interests have motivated research on automated driving and its impact on drivers’ behaviour, especially after transitions of control. Indeed, once the Automated Driving System (ADS) reaches its operational limits, it is forced to request human intervention. However, the fast accumulation and massive quantity of produced studies and the gaps left behind by standards have led to an imprecise and colloquial use of terms which, as technology and research interest evolve, creates confusion. The goal of this survey is to compare how different taxonomies describe transitions of control, address the current use of widely adopted terms in the field of transitions of control and explain how their use should be standardized to enhance future research. The first outcome of this analysis is a schematic representation of the correspondence among the elements of the reviewed taxonomies. Then, the definitions of “takeover” and “handover” are clarified as two parallel processes occurring in every transition of control. A second set of qualifiers, which are necessary to unequivocally define a transition of control and identify the agent requesting the transition and the agent receiving the request (ADS or the driver), is provided. The “initiator” is defined as the agent requesting the transition to take place, and the “receiver” is defined as the agent receiving that request.

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Section: Further Consideration and Future Challengesmentioning

confidence: 99%

When terminology hinders research: the colloquialisms of transitions of control in automated driving

et al. 2022

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“…Moreover, Körber, Cingel, Zimmermann, and Bengler (2015) found that participants (n = 20) experienced substantial passive fatigue due to monotony after 42 minutes of automated driving using eye-related data. Hadi et al (2020) demonstrated that drivers' (n = 12) takeover performance was significantly worse in various scenarios for fatigued driver in conditional automated driving. Vogelpohl et al (2019) (n = 60) indicated that compared to sleep-deprived drivers in manual driving, drivers in automated driving exhibited facial indicators of fatigue 5 to 25 minutes earlier and their takeover performance was significantly jeopardized.…”

Section: Related Work Driver Fatigue Detection and Predictionmentioning

confidence: 97%

“…Research indicates that the incidence of driver fatigue can be increased by monotonous automated driving (Vogelpohl, Kühn, Hummel, & Vollrath, 2019). This can be dangerous in SAE Level 2 -Level 4 (SAE, 2018) automated vehicles after the driver is out of the control loop for prolonged periods (Hadi, Li, Wang, Yuan, & Cheng, 2020). Depending on the automation level of the vehicle, drivers need a high level of situation awareness in SAE Level 2 (partial automation) automated vehicles and good capabilities to respond to emerging hazards for takeover requests in SAE Level 3 (conditional automation) and Level 4 (high automation) automated vehicles (Collet & Musicant, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Gonçalves, Happee, and Bengler (2016) found that participants felt subjectively fatigued even after as short as 15 minutes of a monitoring task in automated driving and Feldhütter, Gold, Schneider, and Bengler (2017) identified fatigue indicators among 31 participants in a 20-minute automated driving scenario using eye-tracking data. Furthermore, in conditional automated driving, Hadi et al (2020) found that the higher the degree of fatigue was, the worse the takeover performance. Hence, it is critical to detect and predict driver fatigue in monotonous automated driving between SAE Level 2 and Level 4.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Driver Fatigue in Automated Driving with Explainability

Zhou¹,

Alsaid²,

Blommer³

et al. 2021

Preprint

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Research indicates that monotonous automated driving increases the incidence of fatigued driving. Although many prediction models based on advanced machine learning techniques were proposed to monitor driver fatigue, especially in manual driving, little is known about how these black-box machine learning models work. In this paper, we proposed a combination of eXtreme Gradient Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver fatigue with explanations due to their efficiency and accuracy. First, in order to obtain the ground truth of driver fatigue, PERCLOS (percentage of eyelid closure over the pupil over time) between 0 and 100 was used as the response variable. Second, we built a driver fatigue regression model using both physiological and behavioral measures with XGBoost and it outperformed other selected machine learning models with 3.847 root-mean-squared error (RMSE), 1.768 mean absolute error (MAE) and 0.996 adjusted R 2 . Third, we employed SHAP to identify the most important predictor variables and uncovered the black-box XGBoost model by showing the main effects of most important predictor variables globally and explaining individual predictions locally. Such an explainable driver fatigue prediction model offered insights into how to intervene in automated driving when necessary, such as during the takeover transition period from automated driving to manual driving.

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Impacts of Automated Valet Parking Systems on Driver Workload and Trust

Wang,

Su,

et al. 2024

Lecture Notes in Computer Science

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Influence of Passive Fatigue and Take-Over Request Lead Time on Drivers’ Take-Over Performance

Cited by 5 publications

References 10 publications

When terminology hinders research: the colloquialisms of transitions of control in automated driving

When terminology hinders research: the colloquialisms of transitions of control in automated driving

Predicting Driver Fatigue in Automated Driving with Explainability

Impacts of Automated Valet Parking Systems on Driver Workload and Trust

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