Eco-Driving Assistance System for a Manual Transmission Bus Based on Machine Learning

Driving assistance systems (DAS) is a key technology to improve fuel economy for in-use vehicles. This also reduces the operational cost of running a fleet of these vehicles, such as city buses. In this paper, we develop a novel white-box evaluation model using machine learning for a manual transmission bus based on previous research about fuel consumption sensitivity to driving style. Using the proposed evaluation model, an algorithm for learning path planning (LPP) for a driving style is also proposed. The LPP method plans a step-by-step shortest learning path for different driving styles to achieve eco-driving, while increasing the driver's acceptance and adaptation of DAS. Simulation results based on vehicle and engine physical models show that the proposed evaluation model, a pure data model, can be used as an alternative to physical model for the eco-driving prompt strategy. The results of the verification show that the proposed strategy can progressively guide the driver to improve the fuel consumption by 6.25% with minimal changes to driver's driving task and driving style.