基于LSTM概率多模态预期轨迹预测方法

doi:10.19562/j.chinasae.qcgc.2023.07.005

Abstract

Abstract:

To address the problem that unimodal trajectory prediction cannot adequately represent the future prediction space and solve the inherent uncertainty of trajectory prediction， this paper constructs a driving behavior intention recognition and traffic vehicle expectation trajectory prediction model. The driving behavior intention recognition module identifies the probability of lane keeping， left lane change， right lane change， left acceleration lane change and right acceleration lane change of the predicted vehicle； the traffic vehicle expected trajectory prediction module uses an encoder-decoder architecture to output multiple behaviors and trajectories of the predicted vehicle that may occur within the next 6 seconds. The model is trained， validated and tested with the HighD dataset. The test results show that the multi-modal probability distribution generated by the intention recognition-based expected trajectory prediction model can improve the driving safety of the vehicle， significantly improve the trajectory prediction accuracy compared with other methods， and have obvious advantages in predicting long time domain trajectories.

Key words: trajectory prediction, behavioral intent recognition, LSTM, interactive behavior

Zhenhai Gao, Mingxi Bao, Fei Gao, Minghong Tang. The Method of Probabilistic Multi-modal Expected Trajectory Prediction Based on LSTM[J].Automotive Engineering, 2023, 45(7): 1145-1152.

Figures/Tables 10

References 25

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评价指标		精确率 p	召回率 r	F1-score	准确率 a
预测意图	直线行驶	0.989 0	0.992 4	0.990 7	0.983 4
	左换道	0.973 0	0.972 7	0.972 8
	右换道	0.973 2	0.981 8	0.977 5
	左加速换道	0.923 0	0.815 7	0.866 0
	右加速换道	0.920 7	0.817 6	0.872 0

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The Method of Probabilistic Multi-modal Expected Trajectory Prediction Based on LSTM

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