吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (5): 1392-1404.doi: 10.13229/j.cnki.jdxbgxb20190078

• • 上一篇    

汽车纵向自动驾驶的因果推理型决策

高振海(),孙天骏,何磊()   

  1. 吉林大学 汽车仿真与控制国家重点实验室,长春 130022
  • 收稿日期:2019-01-18 出版日期:2019-09-01 发布日期:2019-09-11
  • 通讯作者: 何磊 E-mail:gaozh@jlu.edu.cn;jlu_helei@jlu.edu.cn
  • 作者简介:高振海(1973-),男,教授,博士生导师.研究方向:汽车辅助驾驶与智能驾驶.E-mail:gaozh@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51775236);科技部国家重点研发项目(2017YFB0102600)

Causal reasoning decision⁃making for vehicle longitudinal automatic driving

Zhen-hai GAO(),Tian-jun SUN,Lei HE()   

  1. State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130022, China
  • Received:2019-01-18 Online:2019-09-01 Published:2019-09-11
  • Contact: Lei HE E-mail:gaozh@jlu.edu.cn;jlu_helei@jlu.edu.cn

摘要:

针对汽车纵向自动驾驶决策过程的因果关联问题,建立了车辆跟驰行为的马尔可夫决策过程模型,利用真实驾驶员驾驶模拟器实验数据与驾驶风险原则确定了模型中的状态集和动作集,并根据车辆的行驶状态设计了相应的回报函数,进而基于增强Q学习算法对该模型进行求解,提出了以上决策过程的因果推理机制。最终,通过在随机工况下的仿真测试,验证了该方法的可行性与有效性。

关键词: 车辆工程, 自动驾驶, 决策算法, 马尔科夫决策过程, 增强Q学习算法

Abstract:

In order to solve cause-and-effect problems during decision-making, in this paper, the Markov Decision Process (MDP) model is established by the analysis of car-following at first. Then, the state set and the action set are designed through the combination of driving simulator experimental data and driving risk principle. Third, the reward functions are designed according to different driving states. Finally, a causal reasoning mechanism during the process of decision-making is proposed and reinforcement Q-learning algorithm is applied to solve the MDP model. The feasibility and effectiveness of the proposed method are verified through the simulation tests with random driving conditions.

Key words: vehicle engineering, automatic driving, decision-making algorithm, Markov decision process, reinforcement Q learning

中图分类号: 

  • U462.1

图1

汽车纵向自动驾驶系统"

图2

基于真实驾驶员决策过程的增强学习原型"

表1

MDP模型的构成元素"

元素名称 符号
状态集 S
动作集 A
状态转移概率 P
阻尼系数(折扣因子) γ
回报函数 R

图3

激进型驾驶员模拟跟车实验数据"

图4

稳健型驾驶员模拟跟车实验数据"

图5

保守型驾驶员模拟跟车实验数据"

图6

理想车间距控制模型"

表2

模型参数列表"

变 量 名 称 符号
当前本车车速/(m·s-1) Vx
当前相对距离/m D
当前相对速度/(m·s-1) V r
当前本车加速度/(m·s-2) Ax
设定理想车速/(m·s-1) V _set
设定理想距离/m D _set
下一时刻本车车速/(m·s-1) Vx 1
下一时刻相对距离/m D 1
下一时刻相对速度/(m·s-1) V r1
下一时刻本车加速度/(m·s-2) A

图7

单步状态转移过程"

图8

汽车纵向自动驾驶的仿真模型"

图9

本车跟随低速匀速行驶前车的仿真结果"

图10

本车跟随中速匀速行驶前车的仿真结果"

图11

本车跟随高速匀速行驶前车的仿真结果"

图12

前车低速随机变速行驶工况曲线"

图13

本车跟随低速随机变速行驶前车的仿真结果"

图14

前车中速随机变速行驶工况曲线"

图15

本车跟随中速随机变速行驶前车的仿真结果"

图16

前车高速随机变速行驶工况曲线"

图17

本车跟随高速随机变速行驶前车的仿真结果"

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