A Two-Stage Machine Learning Method for Highly-Accurate Drunk Driving Detection

Harkous, Hasanin; Artail, Hassan

doi:10.1109/wimob.2019.8923366

Cited by 15 publications

(13 citation statements)

References 11 publications

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“…The states identification is treated as a supervised classification using a machine learning model, namely Random Forest. In [24], a two-stage data-driven approach based on Markov models together with Recurrent Neural Networks is presented to detect drunk driving using onboard vehicle sensors. Specifically, several sensory data are collected and processed by Recurrent Neural Networks to predict the longitudinal acceleration in a supervised manner.…”

Section: Related Workmentioning

confidence: 99%

Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector

2022

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This study presents an effective data-driven anomaly detection scheme for drunk driving detection. Specifically, the proposed anomaly detection approach amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a feature extractor with the Isolation Forest (iF) scheme to detect drivers' drunkenness status. We used the t-SNE model to exploit its capacity in reducing the dimensionality of nonlinear data by preserving the local and global structures of the input data in the feature space to obtain good detection. At the same time, the iF scheme is an effective and unsupervised treebased approach to achieving good detection of anomalies in multivariate data. This approach only employs normal events data to train the detection model, making them more attractive for detecting drunk drivers in practice. To verify the detection capacity of the proposed t-SNE-iF approach in reliably detecting drivers with excess alcohol, we used publically available data collected using a gas sensor, temperature sensor, and a digital camera. The overall detection system proved a high detection performance with AUC around 95%, demonstrating the proposed approach's robustness and reliability. Furthermore, compared to the Principal Component Analysis (PCA), Incremental PCA (IPCA), Independent component analysis (ICA), Kernel PCA (kPCA), and Multi-dimensional scaling (MDS)-based iForest, EE, and LOF detection schemes, the proposed t-SNE-based iF scheme offers superior detection performance of drunk driver status.INDEX TERMS Anomaly detection; Driver Drunk Detection; t-distributed Stochastic Neighbor Embedding; isolation forest.

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Section: Related Workmentioning

confidence: 99%

Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector

2022

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“…Harkous et al [3] address the given problem using a 2-phase machine learning system. In phase 1, the vehicle simulator provides time-series sensor data.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

Kumar¹,

Kumar²,

Singh³

et al. 2022

Scientific Programming

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Although the statistics show a slow decline in traffic accidents in many countries over the last few years, drunk or drug-influenced driving still contributes to enough shares in those records to act. Nowadays, breath analysers are used to estimate breath alcohol content (BAC) by law enforcement as a preliminary alcohol screening in many countries. Therefore, since breath analysers or field sobriety testers do not accurately measure BAC, the analysis of blood samples of individuals is required for further action. Many researchers have presented various approaches to detect drunk driving, for example, using sensors, face recognition, and a driver’s behaviour to confound the shortcomings of the time-honoured approach using breath analysers. But each one has some limitations. This study proposed a plan to distinguish between drivers’ states, that is, sober or drunk, by the use of transfer learning from the convolutional neural network (CNN) features to the random forest (RF) features with an accuracy of up to 93%, which is higher than that of existing models. With the same dataset, to validate our research, a comparative analysis was performed with other existing model classifiers such as the simple vector machine (SVM) with an accuracy of 65% and the K-nearest neighbour (KNN) with an accuracy of 62%, and it was found that our approach is an optimized approach in terms of accuracy, precision, recall, F1-score, AUC-ROC curve, and Matthew’s correlation coefficient (MCC) with confusion matrix.

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“…According to NHTSA and its four, above-mentioned cues that a driver is DUI, vehicle-based indicators and related vehicle-centric sensors are of interest. Relevant CAN-bus parameters, and indicators such as wheel steering and lane discipline, are widely used to detect instances of DUI [ 245 , 246 , 247 , 248 , 249 , 250 ]. Harkous et al [ 247 ] identify drunk-driving behaviors using HMMs based on vehicle-sensors data, available via the CAN bus.…”

Section: State 5: Under the Influencementioning

confidence: 99%

“…They found that longitudinal-acceleration sensors achieve the best average classification accuracy for distinguishing between sobriety and intoxication. Harkous and Artail [ 248 ] extend the above work by replacing each HMM by a recurrent neural network (RNN). Likewise, Berri and Osório [ 245 ] use features such as speed, acceleration, braking, steering wheel angle, distance to the center lane, and geometry of the road (straight or curved) to detect DUI of alcohol.…”

Section: State 5: Under the Influencementioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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A Two-Stage Machine Learning Method for Highly-Accurate Drunk Driving Detection

Cited by 15 publications

References 11 publications

Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector

Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector

An Optimized Approach Using Transfer Learning to Detect Drunk Driving

Survey and Synthesis of State of the Art in Driver Monitoring

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