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Conference Paper ML-based Power Seat Control System
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Authors
Kang-Woon Hong, Dong-Hwan Park
Issue Date
2019-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1260-1261
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8940026
Abstract
We propose the power seat control system based on a machine learning approach from passengers' control actions. More specifically, the system adjusts seat in cars by referring to the trained model on behalf of passengers. If they manually adjust the car seat, the system re-trains the inference model. Whenever manual adjustment happens, the model update is repeated. The system can be used in any area of adjustment system such as a power seat and a power mirror. Consequently, the proposed system can improve passenger's safety and satisfaction in driving.
KSP Keywords
Control systems, Machine Learning Approach, Manual adjustment, Model update, control actions, inference model