期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 24, 期 1, 页码 827-837出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3216748
关键词
Uncertainty; TV; Forecasting; Trajectory; Vehicles; Biological system modeling; Predictive models; Connected vehicles; autonomous vehicles; energy efficiency; machine learning; optimal control; artificial neural network (ANN); long short-term memory (LSTM)
Accurate prediction of the preceding vehicle's trajectory is crucial in car-following scenarios to minimize energy consumption of automated vehicles. This study presents a novel design strategy for data-driven vehicle speed predictors to improve energy efficiency in following connected and automated vehicles. The proposed loss function, based on weighted-mean-squared error, effectively reduces the energy consumption of an electric vehicle compared to conventional loss functions.
In car-following scenarios, accurate previews of the preceding vehicle's trajectory are essential for minimizing the energy consumption of a following automated vehicle. This work presents a novel design strategy for data-driven vehicle speed predictors to increase the energy efficiency of the following connected and automated vehicle. The loss function is formulated as a weighted-mean-squared error, where the weights are tuned based on the influence of uncertainty at an individual prediction step on the energy consumption of the automated following vehicle. The efficacy of the proposed loss function is validated by applying it to training representative predictors and testing the predictors in the energy-optimal control of the following vehicle. The energy saving of the optimal controller is evaluated by comparing the electricity consumption of a battery electric vehicle with the human driver following, emulated by an intelligent driver model. Simulation results show that the energy consumption of an electric vehicle is reduced by an average of 12% compared to a human driver following demonstrated by an intelligent driver model, whereas using a conventional loss function, a mean-squared error loss function, reduces by 10%.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据