A comprehensive intention prediction method considering vehicle interaction

W. Cai, Ganglei He, Jianlong Hu, Haiyan Zhao, Yuhai Wang, B. Gao
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引用次数: 2

Abstract

In this paper, an interactive intention prediction method is proposed. Firstly, the Hidden Markov Model integrated with Gaussian Mixture Model is modeled for current behavior recognition and its parameters are trained through NGSIM dataset. Then, a trajectory prediction method based on Frenet frame is used to predict the future traffic situation, considering which future behavior reasoning is realized by maximum expected utility theory. The final intention prediction result is a combination of historical trajectory recognition and future behavior reasoning. The simulation results show that the proposed method has the ability of reasonably reflecting the interaction process between vehicles and the prediction performance is good.
一种考虑车辆交互的综合意图预测方法
本文提出了一种交互式意向预测方法。首先,对基于高斯混合模型的隐马尔可夫模型进行建模,并利用NGSIM数据集对隐马尔可夫模型参数进行训练;然后,采用基于Frenet框架的轨迹预测方法预测未来交通状况,并利用最大期望效用理论实现未来行为推理;最终的意图预测结果是历史轨迹识别和未来行为推理的结合。仿真结果表明,该方法能够较好地反映车辆间的相互作用过程,预测效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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