Neural Learning of Predicting Driving Environment
Abstract
Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2008
- Accession Number
- ADA490066
Entities
People
- Abul Masrur
- Anthony E. Phillips
- Jungme Park
- Leo Kiliaris
- Ming Kuang
- Yi L. Murphey
- Zhihang Chan
Organizations
- United States Army Tank Automotive Research, Development and Engineering Center