Research on data-driven and theory-driven hybrid car-following model
Autonomous driving has been advancing rapidly and is poised to replace human drivers in the future.One of the crucial tasks in realizing fully autonomous driving is accurately predicting the following vehicles'behaviors,whose models have been extensively researched.Existing models are categorized into theory-driven models and data-driven models.Theory-driven models,such as the Optimal velocity Model(OV),successfully explain observed traffic behaviors but struggle to handle the varied information of driving behaviors,resulting in poor speed prediction capabilities.Data-driven deep learning prediction models can handle complex driving information but require extensive driving data for model training.To address the limitations of both types of models,we propose a hybrid model integrating the theory-driven model(IDM)with the data-driven model(PSO-CNN-BiLSTM-Att).By integrating the prediction results of the OV model and the PSO-CNN-BiLSTM-Att neural network,our model preserves the controllability provided by the theory-driven model while leveraging the prediction accuracy of the data-driven model.Utilizing NGSIM traffic data,it markedly reduces the prediction error by 88％and 67％respectively compared to those of individual OV theory-driven and PSO-CNN-BiLSTM-Att data-driven models.Meanwhile,our simulations of asymmetric driving behavior styles demonstrate our hybrid model accurately monitors following vehicles'behaviors.
car following behavioroptimal velocity modellong short-term memoryPSOcombination prediction
国家科技期刊平台
NSTL
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