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

Wörle, Johanna; Metz, Barbara; Thiele, Christian; Weller, Gert

doi:10.1049/iet-its.2018.5529

Cited by 30 publications

(13 citation statements)

References 29 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trends in physiological parameters were consistent with previous studies [ 54 , 55 , 56 , 57 , 58 ]. Nevertheless, the results showed slight differences in ULF compared to some studies [ 9 , 59 , 60 , 61 , 62 ] which believed that ULF cannot reflect a human’s workload level. After further literature research and analysis, our results were found to be justified.…”

Section: Resultscontrasting

confidence: 74%

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Wang

et al. 2022

Biosensors

View full text Add to dashboard Cite

With the development of the autopilot system, the main task of a pilot has changed from controlling the aircraft to supervising the autopilot system and making critical decisions. Therefore, the human–machine interaction system needs to be improved accordingly. A key step to improving the human–machine interaction system is to improve its understanding of the pilots’ status, including fatigue, stress, workload, etc. Monitoring pilots’ status can effectively prevent human error and achieve optimal human–machine collaboration. As such, there is a need to recognize pilots’ status and predict the behaviors responsible for changes of state. For this purpose, in this study, 14 Air Force cadets fly in an F-35 Lightning II Joint Strike Fighter simulator through a series of maneuvers involving takeoff, level flight, turn and hover, roll, somersault, and stall. Electro cardio (ECG), myoelectricity (EMG), galvanic skin response (GSR), respiration (RESP), and skin temperature (SKT) measurements are derived through wearable physiological data collection devices. Physiological indicators influenced by the pilot’s behavioral status are objectively analyzed. Multi-modality fusion technology (MTF) is adopted to fuse these data in the feature layer. Additionally, four classifiers are integrated to identify pilots’ behaviors in the strategy layer. The results indicate that MTF can help to recognize pilot behavior in a more comprehensive and precise way.

show abstract

Section: Resultscontrasting

confidence: 74%

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Wang

et al. 2022

Biosensors

View full text Add to dashboard Cite

show abstract

“…Studies performed in driving simulators are beneficial in understanding causal analysis as different factors can be controlled [38]. Previous studies have coupled driving simulators with physiological sensors to detect driver states in different contextual settings such as detecting states of being awake versus sleeping in four-hour driving epochs [39], cognitive distraction when the lead vehicle abruptly breaks [40], response to automation takeover when the take over request is offered through different sources [41], and driver's performance and possible cognitive distraction when being exposed to different traffic signs [42]. Driving simulators are safe tools for conducting driving experiments in different environmental conditions, such as crash events, or evaluating drivers' emotional states [43].…”

Section: A Driving Simulatormentioning

confidence: 99%

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

et al. 2021

View full text Add to dashboard Cite

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.INDEX TERMS Naturalistic driving study, physiological sensing,driver state detection, shared-autonomy, contextual awareness, human-in-the-loop systems

show abstract

“…The most commonly investigated physiological-based methods for driver drowsiness detection [22] utilize information based on brain activity (electroencephalography (EEG)) [44][45][46], cardiac activity (electrocardiography (ECG)) [47][48][49], ocular activity (electrooculography (EOG)) [50][51][52], and muscle tone (electromyography (EMG)) [53,54]. Physiological measures are reliable, accurate, and show high potential in differing wakefulness and sleep during driving [5,55]. They change in the very early stages of drowsiness compared to behavioral or vehicle-based ones that have the ability to warn the driver in time [5].…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

View full text Add to dashboard Cite

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

show abstract

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

Cited by 30 publications

References 29 publications

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Contact Info

Product

Resources

About