953 research outputs found
Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
This paper addresses the learning task of estimating driver drowsiness from
the signals of car acceleration sensors. Since even drivers themselves cannot
perceive their own drowsiness in a timely manner unless they use burdensome
invasive sensors, obtaining labeled training data for each timestamp is not a
realistic goal. To deal with this difficulty, we formulate the task as a weakly
supervised learning. We only need to add labels for each complete trip, not for
every timestamp independently. By assuming that some aspects of driver
drowsiness increase over time due to tiredness, we formulate an algorithm that
can learn from such weakly labeled data. We derive a scalable stochastic
optimization method as a way of implementing the algorithm. Numerical
experiments on real driving datasets demonstrate the advantages of our
algorithm against baseline methods.Comment: Accepted by ICASSP202
Automated drowsiness detection for improved driving safety
Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers
for 30 facial actions from the Facial Action Coding system were developed
using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin
A Survey on Drivers Drowsiness Detection Techniques
Nowadays, there are many systems are available in market like navigation systems, warning alarm systems etc. to make drivers work easy. Traffic accidents due to human errors cause many deaths and injuries around the world. Drowsiness and sleeping while driving are now identified as one of the reasons behind fatal crashes and highway accidents caused by drivers. Various drowsiness detection techniques research are discussed in this paper. These techniques are classified and then compared using their features. Computer vision bas ed image processing techniques is one of them. This uses various images of driver to detect drowsiness states using his/her eyes states and facial expressions. This technique is on the focus of this survey paper
Driver drowsiness detection based on respiratory signal analysis
Drowsy driving is a prevalent and serious public health issue that deserves attention. Recent studies estimate around 20% of car crashes have been caused by drowsy drivers. Nowadays, one of the main goals in the development of new advanced driver assistance systems is the trustworthy drowsiness detection. In this paper, a drowsiness detection method based on changes in the respiratory signal is proposed. The respiratory signal, which has been obtained using an inductive plethysmography belt, has been processed in real-time in order to classify the driver’s state of alertness as drowsy or awake. The proposed algorithm is based on the analysis of the respiratory rate variability (RRV) in order to detect the fight against to fall asleep. Moreover, a method to provide a quality level of the respiratory signal is also proposed. Both methods have been combined to reduce false alarms due to changes of measured RRV associated not to drowsiness but body movements. A driving simulator cabin has been used to perform the validation tests and external observers have rated the drivers’ state of alertness in order to evaluate the algorithm performance. It has been achieved a specificity of 96.6%, sensitivity of 90.3% and Cohen’s Kappa agreement score of 0.75 on average across all subjects through a leave-one-subject-out cross-validation. A novel algorithm for driver’s state of alertness monitoring through the identification of the fight against to fall asleep has been validated.
The proposed algorithm may be a valuable vehicle safety system to alert drowsiness while drivingPeer ReviewedPostprint (published version
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
A lack of driver's vigilance is the main cause of most vehicle crashes.
Electroencephalography(EEG) has been reliable and efficient tool for drivers'
drowsiness estimation. Even though previous studies have developed accurate and
robust driver's vigilance detection algorithms, these methods are still facing
challenges on following areas: (a) small sample size training, (b) anomaly
signal detection, and (c) subject-independent classification. In this paper, we
propose a generalized few-shot model, namely EEG-Fest, to improve
aforementioned drawbacks. The EEG-Fest model can (a) classify the query
sample's drowsiness with a few samples, (b) identify whether a query sample is
anomaly signals or not, and (c) achieve subject independent classification. The
proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset
and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for
1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for
1-shot and 5-shot support samples in the SADT dataset.Comment: Submitted to peer review journal for revie
A Light on Physiological Sensors for Efficient Driver Drowsiness Detection System
International audienceThe significant advance in bio-sensor technologies hold promise to monitor human physiologicalsignals in real time. In the context of public safety, such technology knows notable research investigations toobjectively detect early stage of driver drowsiness that impairs driver performance under various conditions.Seeking for low-cost, compact yet reliable sensing technology that can provide a solution to drowsy stateproblem is challenging. While some enduring solutions have been available as prototypes for a while, many ofthese technologies are now in the development, validation testing, or even commercialization stages. Thecontribution of this paper is to assess current progress in the development of bio-sensors based driver drowsinessdetection technologies and study their fundamental specifications to achieve accuracy requirements. Existingmarket and research products are then ranked following the discussed specifications. The finding of this work isto provide a methodology to facilitate making the appropriate hardware choice to implement efficient yet lowcostdrowsiness detection system using existing market physiological based sensors
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
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