Abstract
Vehicles have become essential tools of transport, offering a great opportunity to exploit the relationship between people and the car. This paper aims to solve an interesting problem, recognizing who the person is through their driving behaviors. Driver identification is useful for quite a few situations, such as car usage authentication, context-based recommendation, and determination of auto-insurance compensation. In this work, we propose WhozDriving, an approach that analyzes drivers’ driving behavior data and extract some sudden changes of driver behaviors as features which can be applied to distinguish different drivers. We propose a supervised learning method to detect anomaly driving trajectory from driving data. Experimental results on driving datasets show that our proposed approach is effective in terms of anomaly detection rate and misclassification anomaly rate.
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Acknowledgement
This work was partially supported by the National Basic Research Program of China (No. 2015CB352400), the National Natural Science Foundation of China (No. 61332005, 61373119), the Fundamental Research Funds for the Central Universities (3102015ZY095).
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He, M., Guo, B., Chen, H., Chin, A., Tian, J., Yu, Z. (2016). WhozDriving: Abnormal Driving Trajectory Detection by Studying Multi-faceted Driving Behavior Features. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_12
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DOI: https://doi.org/10.1007/978-3-319-42553-5_12
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