Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 674-681.doi: 10.13229/j.cnki.jdxbgxb20221434

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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

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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

CLC Number: 

  • U495

Fig.1

Overall framework of the model"

Fig.2

Schematic diagram of vehicle identification"

Fig.3

GRU cycle unit structure diagram"

Fig.4

Schematic diagram of prediction results"

Fig.5

Explanation of track classification basis"

Table 1

Comparison of prediction ability of different models"

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

Fig.6

Comparison of prediction ability of different models"

Fig.7

Lane change prediction trajectory results"

Fig.8

Lane change prediction attention distribution"

Fig.9

Importance scores of surrounding vehicles"

Fig.10

Lane keeping prediction trajectory results and importance score"

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