Abstract
In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.
Highlights
Improving the fuel efficiency of commercial vehicles provides an environmental benefit, and an economic one
We aimed to determine the input representations that improve the performance of a pre-existing model based controller using predictive cruise control technologies
The biggest challenge in this study was to find the input mapping that best predicts the Predictive Cruise Control (PCC) desired velocity offset from the actual vehicle velocity
Summary
Improving the fuel efficiency of commercial vehicles provides an environmental benefit, and an economic one. Most work in this area focuses on directly designing vehicles with better fuel efficiency. A secondary approach is to improve the fuel efficiency of a given vehicle by driving it more efficiently. Driving at lower speeds on the highway, braking smoothly and accelerating slowly are all known methods for saving fuel. It is impractical for a driver to determine the optimal driving speed given a schedule, terrain and road incline. Providing a learning algorithm to predict the best speed at any given time would significantly improve fuel consumption in commercial vehicles
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