Improve the LSTM trajectory prediction accuracy through an exponential weighted loss function

Abstract

Abstract: Although a long-short-term memory (LSTM) network has been widely adopted to predict the vehicle trajectory, the iterative nature of LSTM introduces accumulative errors during training, resulting in a gradual decrease in the accuracy of trajectory prediction over time. Therefore, how to reduce the accumulative errors of the LSTM is a critical issue. To solve this problem, an exponential weighted loss function is used in this paper to weight the smoothed L1 loss function to reduce the cumulative error, enabling the model to predict the future trajectory of the vehicle more accurately, which is validated on US-101 and I-80 datasets from Next Generation Simulation (NGSIM). The simulation results show that the test dataset’s average error at 0.2 s and 5 s is reduced from 0.332 0 m and 7.716 8 m to 0.103 2 m and 6.624 3 m, respectively. The average prediction error is reduced by 14.16%, significantly reducing the cumulative error.

Key words: autonomous driving, trajectory prediction, LSTM, an encoder-decoder model, an exponential loss function

CLC Number:

TP3
U461.91

ZHANG Tong, WANG Zhi-wen, LU Yan-rong, SUN Hong-tao. Improve the LSTM trajectory prediction accuracy through an exponential weighted loss function[J]. Journal of Lanzhou University of Technology, 2023, 49(5): 86-92.

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