Abstract
Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.
Highlights
One of the leading causes of death among young people are motor vehicle crashes [1], young drivers are 5 to 10 times more likely to experience injuries related to road crashes, and young males have a higher crash rate than young females [2]
We propose the use of low-cost Internet of Things (IoT) sensors to characterize the air and detect the presence of alcohol in the vehicle by processing the signals with genetic algorithms
In order to detect drunk drivers: (1) alcohol presence in the vehicle is measured and stored using seven alcohol sensors, (2) the measurements are standardized according to the sensorspecific sensibility and its longitudinal behavior, (3) statistical features are extracted from the normalized signals, (4) a genetic algorithm is used to train several models in order to find the optimal subset of features within the dataset, and (5) a model that accurately classifies drunk and non-drunk drivers is constructed
Summary
One of the leading causes of death among young people are motor vehicle crashes [1], young drivers are 5 to 10 times more likely to experience injuries related to road crashes, and young males have a higher crash rate than young females [2]. Among social and situational factors include: the presence of passengers of similar age that may distract the driver [3], fatigue is a risk factor among young people as they are affected by sleepiness more often [4], and social and economic status plays and important role as they social group may affect their driving behaviors by encouraging them to take greater risks [5]. Exposure related factors include the weather condition, as it plays an important influence on the crash rates, as the young people exhibit less experience dealing with such conditions, such as snow, fog, rain, black ice conditions, etc., [10]. The time increase the risk for young people as their are more likely to crash at night and over the weekend [12], as we can see those risk factors plays an important role, measuring the safety efficiency of the drivers is very important [13]
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