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A Hybrid Method Combining Markov Prediction and Fuzzy Classification for Driving Condition Recognition.
- Source :
-
IEEE Transactions on Vehicular Technology . Nov2018, Vol. 67 Issue 11, p10411-10424. 14p. - Publication Year :
- 2018
-
Abstract
- Driving condition adaptive control is an effective vehicle fuel-saving technique, and the key challenge lies in improving the recognition accuracy of current driving condition. The state-of-the-art approach is based on recognizing historical driving data with a fixed length sliding window to detect current driving condition. However, few research has been conducted to directly recognize the occurring micro-trip (a speed time series between two starts). That is because at the beginning stage of an occurring micro-trip, its known speed time series is too short to be correctly recognized. In this paper, we addressed this issue by proposing a hybrid method for the occurring micro-trip recognition, and two efforts are made to improve recognition accuracy. First, a hybrid recognition procedure is proposed, which combines the Markov chain prediction model and the fuzzy classification model. Second, a statistic approach is proposed to estimate the best time to switch between above-mentioned two models to achieve higher accuracy in detecting current driving condition. Our evaluation results on real-world driving data show that our proposed solution has better accuracy than the state-of-the-art approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 67
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 132967449
- Full Text :
- https://doi.org/10.1109/TVT.2018.2868965