Fuel-Saving-Oriented Collaborative Driving Strategy for Commercial Vehicles Based on Driving Style Recognition

Abstract

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver’s operational intentions while surpassing the driver’s fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver’s intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers.

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

“…According to research, ecological driving can decrease fuel consumption by 15% to 25% and reduce greenhouse gas emissions by at least 30% [18]. In contrast, engines and vehicles utilizing the latest technology are estimated to save approximately 10% to 12% in total fuel costs [19]. Ecological driving encompasses various factors, such as driving speed, acceleration, deceleration, route selection, and idle speed [20][21][22].…”