Abstract
Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone's built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add "noise" to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver's driving behavior.
Highlights
Driver behavior strongly influences road safety[1] and is currently the main contributor to traffic fatalities
Many recorded incidents are caused by human errors, researchers suggest that drivers who exhibit a more aggressive driving style are more likely to be engaged in an accident on the road[2]
A pooling layer by the name of ‘MaxPool1D’ was applied to reduce the number of feature maps by taking the maximum value over a certain pool size. This layer is recommended in Convolutional Neural Networks (CNN) models as it reduces variance and minimizes computations[13]
Summary
Driver behavior strongly influences road safety[1] and is currently the main contributor to traffic fatalities. Driver profiling attempts to understand and monitor the driver’s behaviour in real-time, leveraging a safer and more responsible driving. Driving style profiling is the process of collecting driving data (e.g., acceleration, braking, speed, turning rate, location) applying them to classification models in order to generate a score to determine whether their driving style is safe or unsafe. This work aims to develop a driver profiling system where a smartphone’s built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Conclusions: Based on the results of preliminary studies, we have determined that proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver’s driving behavior
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