Predictive Cruise Control of Connected and Autonomous Vehicles : An Adaptive Dynamic Programming Approach

被引:0
|
作者
Odekunle, Adedapo [1 ]
Gao, Weinan [1 ]
Anayor, Chikamso [1 ]
Wang, Xi [1 ]
Chen, Yunfeng [2 ]
机构
[1] Georgia Southern Univ, Dept Elect & Comp Engn, Allen E Paulson Coll Engn & Comp, Statesboro, GA 30460 USA
[2] Georgia Southern Univ, Dept Civil Engn & Construct Management, Allen E Paulson Coll Engn & Comp, Statesboro, GA 30460 USA
来源
IEEE SOUTHEASTCON 2018 | 2018年
关键词
Vehicular platoon; data-driven learning strategy; adaptive dynamic programming;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper proposes a novel predictive cruise control method for a platoon of connected and autonomous vehicles. The main objective is to minimize idle time of vehicular platoon using information from the traffic lights. First, the reference velocity is determined for each vehicle in the platoon. Second, a data-driven learning strategy, named adaptive dynamic programming (ADP), is employed to develop an optimal state-feedback controller without any prior knowledge of the platooning system dynamics. This resultant controller regulates the headway, velocity and acceleration of each vehicle to accommodate both safety and trip time reduction goals. A numerical simulation is demonstrated to ascertain the effectiveness of the proposed approach.
引用
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页数:6
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