基于本体和BN的无人车行为决策方法

doi:10.12305/j.issn.1001-506X.2021.02.20

Abstract

Abstract:

Compared with expressway traffic environment, urban traffic environment has higher complexity and uncertainty. Therefore, autonomous vehicles need behavior decision system to give safe and effective driving actions in driving scenarios. In this paper, a behavior decision model combined ontology semantic and probabilistic reasoning of Bayesian network (BN) is proposed. Based on the multi-source heterogeneous information and domain expert experience in autonomous driving scene, the ontology is establishd and carried out probability expansion to convert ontology to BN, which can obtain the best driving action in the current driving scene through BN. This method not only realizes the formal description and sharing of driving scene domain knowledge, but also takes into account the uncertainty in driving scene. Finally, the effectiveness of the proposed method in autonomous vehicle behavior decision is verified by Prescan/Simulink joint simulation experiment.

Key words: autonomous vehicle, behavior decision-making, ontology, Bayesian network(BN), Prescan joint simulation

CLC Number:

TP311

Xue SUN, Zhiqiu HUANG, Guohua SHEN, Jinyong WANG, Heng XU. Behavior decision method of autonomous vehicle based on ontology and BN[J]. Systems Engineering and Electronics, 2021, 43(2): 452-465.

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属性名	定义域	值域
在车道上	自身车辆类	车道类
有左车道	车道类	车道类
有障碍物	障碍物类	自身车辆类
有左车道线	车道类	车道线类
执行驾驶行为	车辆类	行为类
左前方障碍物	自身车辆类	障碍物类
有道路标志	车道类	道路标志类

属性名	定义域	值域
当前速度	自身车辆类	双精度浮点型
可以左变道	自身车辆类	布尔型
车道数量	车道类	双精度浮点型
车道线类型	车道线类	字符型
与障碍物的距离	障碍物	双精度浮点型
与路口的距离	路口	双精度浮点型
与障碍物的相对速度	障碍物	双精度浮点型

节点	节点含义
车道	道路车道数
车道线	道路线情况
障碍物	周围有无障碍车辆
左车道	车辆在左车道行驶
右车道	车辆在右车道行驶
前方车辆	无人驾驶车辆前方车辆可能采取的驾驶动作
右前方车辆	无人驾驶车辆右前方车辆可能采取的驾驶动作
右后方车辆	无人驾驶车辆右后方车辆可能采取的驾驶动作
自身车辆	无人驾驶车辆与周围车辆的相对速度和相对距离
横向行为	无人驾驶车辆可能采取的横向驾驶动作
纵向行为	无人驾驶车辆可能采取的纵向驾驶动作

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Behavior decision method of autonomous vehicle based on ontology and BN

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