基于决策树和BN的自动驾驶车辆行为决策方法

doi:10.12305/j.issn.1001-506X.2022.10.18

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (10): 3143-3154.doi: 10.12305/j.issn.1001-506X.2022.10.18

基于决策树和BN的自动驾驶车辆行为决策方法

刘延钊^1,^2,*, 黄志球^1,^2,³, 沈国华^1,^2,³, 王金永^1,², 徐恒^1,²

1. 南京航空航天大学计算机科学与技术学院, 江苏南京 211106
2. 南京航空航天大学高安全系统的软件开发与验证技术工业和信息化部重点实验室, 江苏南京 211106
3. 软件新技术与产业化协同创新中心, 江苏南京 210093

收稿日期:2021-06-01 出版日期:2022-09-20 发布日期:2022-10-24
通讯作者: 刘延钊
作者简介:刘延钊 (1995—), 男, 硕士研究生, 主要研究方向为数据挖掘、机器学习、自动驾驶行为决策|黄志球 (1965—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为工业软件、智能化软件工程、网络空间安全、大数据与云计算|沈国华 (1976—), 男, 副教授, 硕士研究生导师, 博士, 主要研究方向为需求工程、可信计算、软件度量、知识图谱、本体、语义Web、软件安全性、服务隐私|王金永 (1983—), 男, 博士研究生, 主要研究方向为无人系统的时空同步约束建模、基于深度强化学习的决策验证、无人系统安全性分析|徐恒 (1994—), 男, 博士研究生, 主要研究方向为系统安全性建模分析

Behavioral decision-making methods of autonomous vehicles based on decision tree and BN

Yanzhao LIU^1,^2,*, Zhiqiu HUANG^1,^2,³, Guohua SHEN^1,^2,³, Jinyong WANG^1,², Heng XU^1,²

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210093, China

Received:2021-06-01 Online:2022-09-20 Published:2022-10-24
Contact: Yanzhao LIU

摘要/Abstract

摘要：

交通环境中存在着众多影响自动驾驶车辆行为决策安全的不确定性因素, 准确并及时地处理不确定性因素对自动驾驶车辆安全至关重要。因此, 建立了以人工驾驶行为分类为基础的贝叶斯网络(Bayesian network, BN)行为决策模型。利用决策树分类算法对人工驾驶行为进行分类, 利用BN建模驾驶环境并生成最优驾驶动作, 此方法既可以及时地分析人类驾驶员行为类别, 又能够充分考虑驾驶场景中的不确定性因素。利用仿真工具PreScan设计仿真实验, 仿真结果表明行为决策模型能够给出安全、合理的自动驾驶车辆行为。

关键词: 自动驾驶车辆, 行为决策, 贝叶斯网络, 决策树, PreScan仿真

Abstract:

There are many factors that affect autonomous vehicles' behavioral decision-making in the traffic environment. It is very important for the safety of autonomous vehicles to handle uncertainty factors accurately and timely. To this end, we design a behavioral decision-making model of Bayesian network (BN) based on classification of human driving behaviors. The decision tree classification algorithm is used to classify the driving behaviors of human driving vehicles, The BN is used to model the driving scene and create the best driving behavior. It not only analyzes the human driving behavioral style timely, but also takes into account the uncertain factors in the driving scene. We design the PreScan simulation experiments, the simulation results show that the behavioral decision model can provide safe and reasonable behavior of autonomous vehicles.

Key words: autonomous vehicles, behavioral decision-making, Bayesian network (BN), decision tree, PreScan simulation

中图分类号:

TP311

刘延钊, 黄志球, 沈国华, 王金永, 徐恒. 基于决策树和BN的自动驾驶车辆行为决策方法[J]. 系统工程与电子技术, 2022, 44(10): 3143-3154.

Yanzhao LIU, Zhiqiu HUANG, Guohua SHEN, Jinyong WANG, Heng XU. Behavioral decision-making methods of autonomous vehicles based on decision tree and BN[J]. Systems Engineering and Electronics, 2022, 44(10): 3143-3154.

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节点名称	节点含义
车道	车道数量
车道线障碍物交通灯人行道行人右前方车辆驾驶行为类别自动驾驶车辆	道路线类型是否存在障碍物交通灯颜色是否存在人行道行人的行为右前方车辆驾驶行为右前方车辆行为分类自动驾驶车辆行为

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基于决策树和BN的自动驾驶车辆行为决策方法

Behavioral decision-making methods of autonomous vehicles based on decision tree and BN

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