Abstract
Dear editor, This letter deals with a real-world problem regarding chaotic time series prediction, where a driver-centric velocity prediction model is presented for vehicle intelligent control and advanced driver assistance, i.e., multi-dimension fuzzy predictor. Inspired by fuzzy granulation technology, a finite-state Markov chain (MC) is reinforced to capture probabilities of the transitions between velocity and acceleration and present signals that vary in a continuous range. The predictability of the multi-dimensional fuzzy predictor is examined by comparing two existing MC-based predictors over the two laboratory cycles and one virtual driving cycle, both of which have high accuracy.
Original language | English |
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Article number | 9910369 |
Number of pages | 3 |
Journal | IEEE/CAA Journal of Automatica Sinica |
Early online date | 4 Oct 2022 |
DOIs | |
Publication status | E-pub ahead of print - 4 Oct 2022 |
Keywords
- Vehicles
- Behavioral sciences
- Predictive models
- Support vector machines
- Prediction algorithms
- Markov processes
- Fuels
ASJC Scopus subject areas
- Control and Optimization
- Artificial Intelligence
- Information Systems
- Control and Systems Engineering