A Study of the Identification Method of Driving Fatigue Based on Physiological Signals

Xu, Shili; Zhao, Xiaohua; Zhang, Xingjian; Rong, Jian

doi:10.1061/41186(421)229

Cited by 6 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu [20] validated the effectiveness of the Beijing University of Technology driving simulator, and corroborated that the driving simulator is effective in terms of capturing drivers’ physiological and psychological parameters. Ding et al [7,8,9] also validated the effectiveness of the driving simulator on travel speed in downhill segments equipped with SRMs, compared to speed data collected in the field.…”

Section: Introductionmentioning

confidence: 91%

Evaluation Research of the Effects of Longitudinal Speed Reduction Markings on Driving Behavior: A Driving Simulator Study

Ding

Zhao

et al. 2016

IJERPH

Self Cite

View full text Add to dashboard Cite

The objective of this paper is to explore the effects of longitudinal speed reduction markings (LSRMs) on vehicle maneuvering and drivers’ operation performance on interchange connectors with different radii. Empirical data were collected in a driving simulator. Indicators—relative speed change, standard deviation of acceleration, and gas/brake pedal power—were proposed to characterize driving behavior. Statistical results revealed that LSRMs could reduce vehicles’ travel speed and limit drivers’ willingness to increase speed in the entire connector. To probe the impacts of LSRMs, the connecter was split into four even sections. Effects of LSRMs on driving behavior were stronger in the second and the final sections of connectors. LSRMs also enhanced drivers’ adaptability in the first three quarters of a connector when the radius was 50 m. Drivers’ gas pedal operation would be impacted by LSRMs in the entire connector when the radius was 50 m. LSRMs could only make drivers press brake pedal more frequently in the second section with 80 m and 100 m radius. In the second quarter section of a connector—from the FQP (the first quartile point) to the MC (the middle point of curve)—LSRMs have better effects on influencing vehicle maneuvering and drivers’ operation performance.

show abstract

Section: Introductionmentioning

confidence: 91%

Evaluation Research of the Effects of Longitudinal Speed Reduction Markings on Driving Behavior: A Driving Simulator Study

Ding

Zhao

et al. 2016

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…The performance of the driving simulator can substantially influence the experimental test results. In order to achieve the reliable and unbiased experimental data, our driving simulator in Beijing University of Technology has been well calibrated and validated in many previous studies (Xu, 2012;Li et al, 2013;Ding et al, 2014). For example, about 200 post-drive subjective questionnaires were collected from different experiments using the driving simulator.…”

Section: Discussion and Research Limitationsmentioning

confidence: 99%

A generic approach for examining the effectiveness of traffic control devices in school zones

Zhao

Ding

et al. 2015

Accident Analysis & Prevention

Self Cite

View full text Add to dashboard Cite

“…Signals such as Electroencephalography (EEG) and electrocardiogram (ECG) were extracted and analyzed to discriminate between the drivers' drowsiness and alertness states [20]. Vezard et al [17] proposed a genetic algorithm to detect alertness of individuals using EEG signals recorded using 58 electrodes.…”

Section: Related Workmentioning

confidence: 99%

Cascaded multimodal analysis of alertness related features for drivers safety applications

Abouelenien

Burzo

Mihalcea

2015

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

View full text Add to dashboard Cite

Drowsy driving has a strong influence on the road traffic safety. Relying on improvements of sensorial technologies, a multimodal approach can provide features that can be more effective in detecting the level of alertness of the drivers.In this paper, we analyze a multimodal alertness dataset that contains physiological, environmental, and vehicular features provided by Ford to determine the effect of following a multimodal approach compared to relying on single modalities. Moreover, we propose a cascaded system that uses sequential feature selection, time-series feature extraction, and decision fusion to capture discriminative patterns in the data. Our experimental results confirm the effectiveness of our system in improving alertness detection rates and provide guidelines of the specific modalities and approaches that can be used for improved alertness detection.

show abstract

A Study of the Identification Method of Driving Fatigue Based on Physiological Signals

Cited by 6 publications

References 1 publication

Evaluation Research of the Effects of Longitudinal Speed Reduction Markings on Driving Behavior: A Driving Simulator Study

Evaluation Research of the Effects of Longitudinal Speed Reduction Markings on Driving Behavior: A Driving Simulator Study

A generic approach for examining the effectiveness of traffic control devices in school zones

Cascaded multimodal analysis of alertness related features for drivers safety applications

Contact Info

Product

Resources

About