吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (3): 674-681.doi: 10.13229/j.cnki.jdxbgxb20221434

• 通信与控制工程 • 上一篇    

考虑周车信息的自车期望轨迹预测

田彦涛1(),许富强1,王凯歌1,郝子绪1,2   

  1. 1.吉林大学 通信工程学院,长春 130022
    2.吉林大学 交通学院,长春 130022
  • 收稿日期:2022-11-12 出版日期:2023-03-01 发布日期:2023-03-29
  • 作者简介:田彦涛(1958-),男,教授,博士生导师. 研究方向:复杂系统建模、优化与控制. E-mail:tianyt@jlu.edu.cn
  • 基金资助:
    国家自然科学基金联合基金项目(U19A2069)

Expected trajectory prediction of vehicle considering surrounding vehicle information

Yan-tao TIAN1(),Fu-qiang XU1,Kai-ge WANG1,Zi-xu HAO1,2   

  1. 1.College of Communication Engineering,Jilin University,Changchun 130022,China
    2.College of Transportation,Jilin University,Changchun 130022,China
  • Received:2022-11-12 Online:2023-03-01 Published:2023-03-29

摘要:

针对驾驶员期望轨迹预测问题,设计了一种考虑周车信息的自车期望轨迹预测模型。建立了单独的自车和周车历史轨迹信息编码器,将编码后的自车历史轨迹信息送往意图识别模块用于识别驾驶员意图;通过注意力机制对自车和周车编码信息进行处理,并与意图识别的结果共同作为解码器模块的输入,最终输出车辆未来位置。最后,采用数据集对模型进行训练,验证了模型的有效性。

关键词: 车辆工程, 轨迹预测, 神经网络, 注意力机制, 预瞄点

Abstract:

Aiming at the problem of driver's expected trajectory prediction, a vehicle expected trajectory prediction model considering the information of surrounding vehicles was designed. Separate encoders of self vehicle and surrounding vehicles historical track information is established, and the encoded self vehicle historical track information is sent to the intention recognition module to identify the driver's intention. The self vehicle and surrounding vehicles coding information is processed through the attention mechanism, the result of intention recognition is used as the input of the decoder module, and the future position of the vehicle is output. Finally,the data set is used to train the model, and the effectiveness of the model is verified.

Key words: vehicle engineering, trajectory prediction, neural network, attention mechanism, preview point

中图分类号: 

  • U495

图1

模型总体框架"

图2

车辆标识示意图"

图3

GRU循环单元结构图"

图4

预测结果示意图"

图5

轨迹分类依据说明图"

表1

不同模型预测能力对比"

预测时间/sRMSE/m
I-ENDEIA-ENDES-ENDED-ENDE
10.310.280.260.25
20.550.500.470.43
30.960.830.810.78
41.311.241.211.18

图6

不同模型预测能力对比"

图7

换道预测轨迹结果"

图8

换道预测注意力分布"

图9

周车重要性得分分布图"

图10

车道保持预测轨迹结果和重要性得分"

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