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Safety Air Bag System for Motorcycle Using Parallel Neural Networks

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Abstract

Due to the development of leisure sports industry and the increase in delivery demand, the demand for two-wheeled vehicles such as the motorcycle is increasing every year; moreover, the motorcycle accident rate is increasing. The motorcyclist’s body is exposed to the outside, and in cases of accidents, the head and the neck are particularly vulnerable. This paper proposes a study about an air bag equipped with Artificial Intelligence to protect the driver’s neck spine from motorcycle accidents. Through the six-axis sensor, it receives the driver’s motor condition data about the acceleration and angular velocity data and measures real time speed and angle; combines them with algorithms that can judge accidents through Artificial Intelligence learning to activate airbags in real time. Data were collected and learned by dividing the types of accidents; for Artificial Intelligence learning, the general Neural Network method was not used however, a mix of parallel Neural Network with an existing Neural Network were used instead. The Artificial Intelligence learning method proposed in this paper has been found to have more improved accuracy, stability and learning time compared to the existing Neural Network.

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Acknowledgements

This work was supported by the KIEE.

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Authors and Affiliations

  1. Department of Control and Instrumentation Engineering, Pukyong National University, Busan, South Korea

    So-Hyeon Jo, Joo Woo, Jae-Hoon Jeong & Gi-Sig Byun

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  1. So-Hyeon Jo
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  2. Joo Woo
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  3. Jae-Hoon Jeong
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  4. Gi-Sig Byun
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Correspondence to Jae-Hoon Jeong.

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Jo, SH., Woo, J., Jeong, JH. et al. Safety Air Bag System for Motorcycle Using Parallel Neural Networks. J. Electr. Eng. Technol. 14, 2191–2203 (2019). https://doi.org/10.1007/s42835-019-00229-3

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  • DOI: https://doi.org/10.1007/s42835-019-00229-3

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