计算机科学
部分可观测马尔可夫决策过程
规划师
过程(计算)
参数化复杂度
马尔可夫决策过程
点(几何)
自动计划和调度
解算器
马尔可夫链
运筹学
人工智能
马尔可夫过程
机器学习
算法
工程类
马尔可夫模型
数学
操作系统
统计
程序设计语言
几何学
作者
Keqi Shu,Huilong Yu,Xingxin Chen,Li Shen,Long Chen,Qi Wang,Li Li,Dongpu Cao
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:27 (1): 234-244
被引量:19
标识
DOI:10.1109/tmech.2021.3061772
摘要
Left turning at unsignalized intersectionis one of the most challenging tasks for urban automated driving, due to the various shapes of intersections and rapidly changing nature of the driving scenarios. This article addresses the challenges of decision making at highly uncertain intersections of different shapes by proposing a generalized critical turning point (CTP)-based hierarchical decision-making and planning method. The high-level planner takes the road map information and generates CTPs using a parameterized extraction model that is proposed and verified by naturalistic driving data. The CTPs are used to generate behavior-oriented paths that could be adapted to various intersections. These modifications help to assure high searching efficiency of the planning process. The low-level planner makes real-time, 2-D planning using a partially observable Markov decision process solver, which can handle the uncertainties of the intersections and make less conservative yet safe actions. With proper modifications, our proposed method can make commute-efficient 2-D planning decisions at unsignalized intersections of various shapes in real time.
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