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Neural Learning of Predicting Driving Environment
| 题名： | Neural Learning of Predicting Driving Environment |
| 作者： | Murphey, Yi L;Chan, ZhiHang;Kiliaris, Leo;Park, Jungme;Kuang, Ming;Masrur, Abul;Phillips, Anthony; |
| 关键词： | HYBRID PROPULSION, ZHYBRID PROPULSIONZ, SYMPOSIA, ZSYMPOSIAZ, PREDICTIONS, ZPREDICTIONSZ, MODELS, ZMODELSZ, WIRELESS COMPUTER NETWORKS, ZWIRELESS COMPUTER NETWORKSZ, FUEL CONSUMPTION, ZFUEL CONSUMPTIONZ, ONLINE SYSTEMS, ZONLINE SYSTEMSZ, EMISSION, ZEMISSI |
| 摘要： | 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. |
| 总页数： | 8 Pages(s) |
| 报告类型： | 科技报告 |
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