Human experience knowledge induction based intelligent train driving

As the most sustainable means of modern transportation, the railway trains are eagerly approaching autonomous driving due to their congenital advantages on operating environments compare to, e.g., road traffics. The intelligent automatic train driving aims at train control with a goal of energy efficiency, punctuality and safety. The derivation of an optimized train driving solution by taking advantage of the undulating terrains along a route, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. To tackle the problem, we propose a two-level human driving experience learning framework and employ the fuzzy rule induction method for online generation of the optimized driving solutions. Based on the records of experienced human drivers, a FURIA model was built to learn the driving rules indicating the correlation between the specified features to the decision of a driving sequence. The fuzzy rules can generally find the best-match driving operation under certain running circumstances. The learned model can be used to determine an optimized driving operation in real-time. Validation experiments show that the energy consumption of the proposed solution is around 8.93% lower than that of average human drivers.