Review of approaches to the problem of driver fatigue and drowsiness

Mashko, Alina

doi:10.1109/scsp.2015.7181569

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Its detection is important for safety applications, as it has been shown, for example, that sleepiness impacts driving performance, even more so than fatigue [31,34]. Most systems that aim to detect a sleepy driver rely on signals derived from interaction with the vehicle, such as abnormal steering behaviour, failures in lane keeping or irregular use of the pedals [32]. Furthermore, in [20,30] the authors have demonstrated that sleepiness is one of the supervening cause of an accident amongst the professional drivers.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Representation Learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech

Amiriparian

Winokurow

Karas

et al. 2020

Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-Life Media Challenge and Workshop

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Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence to sequence autoencoders for fully unsupervised representation learning from audio files. In particular, we test the efficacy of our novel approach on the task of speech-based sleepiness recognition. We evaluate the learnt representations from both autoencoders, and conduct an early fusion to ascertain possible complementarity between them. In our frameworks, we first extract Mel-spectrograms from raw audio. Second, we train recurrent autoencoders on these spectrograms which are considered as time-dependent frequency vectors. Afterwards, we extract the activations of specific fully connected layers of the autoencoders which represent the learnt features of spectrograms for the corresponding audio instances. Finally, we train support vector regressors on these representations to obtain the predictions. On the development partition of the data, we achieve Spearman's correlation coefficients of .324, .283, and .320 with the targets on the Karolinska Sleepiness Scale by utilising attention and non-attention autoencoders, and the fusion of both autoencoders' representations, respectively. In the same order, we achieve .311, .359, and .367 Spearman's correlation coefficients on the test data, indicating the suitability of our proposed fusion strategy. CCS CONCEPTS• Computing methodologies → Neural networks.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Representation Learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech

Amiriparian

Winokurow

Karas

et al. 2020

Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-Life Media Challenge and Workshop

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Enhancement of control processes of city buses' traffic safety

Abishev

Temirgalievich

Sarzhanov

et al. 2016

2016 Smart Cities Symposium Prague (SCSP)

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mmDrive: Fine-grained Fatigue Driving Detection Using mmWave Radar

Juncen,

Cao,

Yang

et al. 2023

ACM Trans. Internet Things

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Early detection of fatigue driving is pivotal for safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about \(96\% \) , and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21 bpm , 0.54 bpm , and 2.52 bpm , respectively. And the accuracy of the fatigue detection algorithm we proposed reached \(97.63\% \) .

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Review of approaches to the problem of driver fatigue and drowsiness

Cited by 4 publications

References 2 publications

Unsupervised Representation Learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech

Unsupervised Representation Learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech

Enhancement of control processes of city buses' traffic safety

mmDrive: Fine-grained Fatigue Driving Detection Using mmWave Radar

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