Sensing Driving Dynamics with Ubiquitous Sensors

Exploring the sensing capabilities of mobile devices (e.g., smartphones, wearables) is the key enabler for novel transportation applications. We have been developing a light-weight mobile system for sensing vehicle dynamics with IMUs. A time-series analytics algorithm harvests the morphological pattern of IMU data to detect vehicle steerings with linear-time complexity.

Faculty

• Kang G. Shin

Graduate Students

• Dongyao Chen
• Kyong-Tak Cho
• Sihui Han
• Zhizhuo Jin
• Arun Ganesan
• Noah T. Curran

Publications

• Noah T. Curran, Arun Ganesan, Mert D. Pesé, and Kang G. Shin, Using Phone Sensors to Augment Vehicle Reliability, in the 11th IEEE Conference on Communications and Network Security (CNS '23), Orlando, FL, October 2023.
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• Dongyao Chen and Kang G. Shin, TurnsMap: Evaluating the risk of left turns with mobile crowdsensing and deep learning, in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'19), London, UK, September 2019.
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• Dongyao Chen, Kyong-Tak Cho, Sihui Han, Zhizhuo Jin, and Kang G. Shin, Invisible Sensing of Vehicle Steering with Smartphones, in 2015 ACM International Conference on Mobile Systems, Applications, and Services (MobiSys '15), Florence, Italy, May 2015.
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