引用本文:尹慧琳,伍淑莉,王亚伟,等.基于层次因子图的智能车环境感知和态势认知模型[J].控制与决策,2020,35(10):2528-2534
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基于层次因子图的智能车环境感知和态势认知模型
尹慧琳,伍淑莉,王亚伟,王杰
(同济大学中德学院,上海200092)
摘要:
为了提升智能车的环境认知能力,根据数据信息的抽象化程度不同提出一种基于层次因子图的智能车环境感知和态势认知模型.首先,基于人类驾驶认知的分层记忆机理,按照被处理信息由低到高的抽象层次,将环境认知分为环境目标感知和态势认知两大任务模块,提出层次化框架;然后,确定层次因子图的拓扑结构并实现层次因子图模型,目标感知层具体体现为多源信息融合和目标跟踪,态势认知层具体体现为车辆变道等态势预测;最后,基于PreScan仿真环境数据、NGSIM真实驾驶数据集及DBNet自动驾驶实测数据集3种数据,验证所提出方法的有效性,并与现有的卡尔曼滤波方法和隐马尔科夫模型方法进行比较,以验证层次因子图在跟踪、融合、态势预测正确率和准确率方面的优势.
关键词:  环境感知  态势认知  因子图  多源信息融合  智能车  目标跟踪
DOI:10.13195/j.kzyjc.2019.0049
分类号:TP273
基金项目:国家自然科学基金项目(61701348);科技部国家重点研发计划新能源汽车专项项目(2016YFB0100901, 2018YFB0105101).
An intelligent vehicle environment perception and situation cognition model based on hierarchical factor graph
YIN Hui-lin,WU Shu-li,WANG Ya-wei,WANG Jie
(Chinesisch-Deutsches Hochschulkolleg,Tongji University,Shanghai200092,China)
Abstract:
In order to improve the environment cognition ability of intelligent vehicles, this paper proposes a hierarchical factor graph based environment perception and situation cognition model according to the abstraction level of data information. Firstly, based on the hierarchical memory mechanism of human driving cognition, according to the abstract level of processed information from low to high, environment cognition is divided into two task modules: object perception and situation awareness. Then, the topological structure of the hierarchical factor graph is determined and the hierarchical factor graph model is realized. The object perception layer is embodied in multi-source information fusion and object tracking. The situation awareness layer is embodied in situation prediction such as vehicle lane change. Finally, based on PreScan simulation environment data, NGSIM real driving data set and DBNet real data set, the effectiveness of the proposed method is verified. Compared with the existing Kalman filter method and hidden Markov model method, the advantages of hierarchical factor diagram in tracking, fusion, situation prediction accuracy and accuracy are verified.
Key words:  environment perception  situation cognition  factor graph  multi-source information fusion  intelligent vehicle  object tracking

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