Aggressive driving behaviour classification using smartphone's accelerometer sensor

Sanjay Kumar Sonbhadra, Sonali Agarwal, Muhammad Syafrullah, Krisna Adiyarta


Aggressive driving is the most common factor of road accidents, and millions of lives are compromised every year. Early detection of aggressive driving behaviour can reduce the risks of accidents by taking preventive measures. The smartphone's accelerometer sensor data is mostly used for driving behavioural detection. In recent years, many research works have been published concerning to behavioural analysis, but the state of the art shows that still, there is a need for a more reliable prediction system because individually, each method has it's own limitations like accuracy, complexity etc. To overcome these problems, this paper proposes a heterogeneous ensemble technique that uses random forest, artificial neural network and dynamic time wrapping techniques along with weighted voting scheme to obtain the final result. The experimental results show that the weighted voting ensemble technique outperforms to all the individual classifiers with average marginal gain of 20%.


Driving pattern; Dynamic time wrapping (DTW); Sliding window; Random forests; Accelerometer; Time series classification

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