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首页> 外文期刊>SAE International Journal of Electrified Vehicles >Multi-objective Optimization for Connected and Automated Vehicles Using Machine Learning and Model Predictive Control
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Multi-objective Optimization for Connected and Automated Vehicles Using Machine Learning and Model Predictive Control

机译：使用机器学习和模型预测控制对互联和自动驾驶汽车进行多目标优化

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摘要

Connected and automated vehicles have attracted more and more attention, given the benefits in safety and efficiency. This research proposes a novel model predictive control method in order to improve energy efficiency and ensure a safe spacing between vehicles. The proposed algorithm focuses on mixed traffic flow, which is more realistic than one that only includes autonomous vehicles. A high-fidelity energy loss model of an electric vehicle is adopted to improve the control's perfor-mance. A data-driven car-following model using machine learning is integrated in the framework of model predictive control to predict the behavior of human-driven vehicles. Its effectiveness in increasing energy efficiency is validated under two driving cycles. In the case of the scaled urban dynamometer driving schedule, the energy loss and the maximum spacing between the autonomous vehicle and the human-driven vehicle decreases by 6% and 18%, respectively, when compared with the baseline model predictive control without the consideration of interaction between the autono-mous vehicle and the human-driven vehicle. In the scenario of the scaled city driving cycle, the energy loss of the autonomous vehicles also reduces by 3%, while the maximum and average spacing does not change significantly. The sensitivity of the optimization results to several parameters of the energy loss model is finally analyzed, and the robustness of the proposed algorithm is validated.
机译：连接和自动车辆吸引了越来越多的关注,因为好处安全性和效率。小说以模型预测控制方法提高能源效率,确保安全间距的车辆。集中在混合交通流,这是更多现实比一个只包括自治车辆。电动汽车是用来改善控制的性能。车辆使用机器学习模型框架的集成预测模型控制预测人类的行为车辆。效率是在两个驱动周期进行验证。城市测力计的比例驾驶时间表,能量损失和最大自主车辆和之间的间距所有的车辆减少了6%和18%,分别与基线相比模型预测控制没有考虑之间的交互autono-mous车辆和所有的车辆。场景的扩展城市驾驶循环,自主车辆的能量损失减少了3%,而最大和平均水平距没有显著改变。灵敏度的优化结果能量损失模型的几个参数最后分析和鲁棒性算法进行了验证。

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