论文标题
部分可观测时空混沌系统的无模型预测
A Comprehensive Eco-Driving Strategy for Connected and Autonomous Vehicles (CAVs) with Microscopic Traffic Simulation Testing Evaluation
论文作者
论文摘要
In this paper, a comprehensive Eco-Driving strategy for CAVs is presented.在此设置中，多个驾驶模式计算速度轮廓非常适合同时节省燃料，而高水平（HL）控制器可确保在驾驶模式之间进行光滑的过渡，以进行生态驱动。这款对自我CAV的生态驾驶确定性控制器配备了车辆到基础设施（V2I）和车辆到车辆（V2V）算法。与基线驾驶模式相比，HL控制器可确保HL控制器确保重大的燃油经济性改善，而自我骑士和交通车辆之间没有碰撞，而在不断变化的约束下，正确设置了自我骑士的驾驶模式。
In this paper, a comprehensive Eco-Driving strategy for CAVs is presented. In this setup, multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles while the driving mode of the ego CAV was set correctly under changing constraints.
