Abstract
There is an increasing availability of electric vehicles in recent years. With the revolutionary motors and electric modules within the electric vehicles, the instant reactions bring up not only improved driving experience but also the unexpected unsafe driving accidents. Unsafe driving behavior prediction is a challenging tasks, due to the complex spatial and temporal scenarios. However, the rich sensor data collected in the electric vehicles shed light on the possible driving behavior profiling.
In this paper, based on a recent electric vehicle dataset, we analyze and categorize the unsafe driving behaviors into several classes. We then design a deep learning based multi-feature fusion approach for the unsafe driving behavior prediction framework. The proposed approach is able to distinguish the unsafe behaviors from normal ones. Improved performance is also demonstrated in the different feature analysis of unsafe behaviors.
J. Yao—This work was supported by NSFC grant 61972151.
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Huang, J., Lin, H., Yao, J. (2021). Unsafe Driving Behavior Prediction for Electric Vehicles. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_7
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DOI: https://doi.org/10.1007/978-3-030-85896-4_7
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