Transfer Learning Based Long Short-Term Memory Car-Following Model for Adaptive Cruise Control

Most existing studies modeled Adaptive Cruise Control (ACC) car-following (CF) behavior using conventional CF models which were originally built for human-driving vehicles (HVs) and calibrated with HV data. In this paper, firstly a learnable CF model is proposed by resorting to Long Short-Term Memory (LSTM) for ACC systems, which utilizes the ACC data for model construction and offers extraordinary adaptability and accuracy. Nevertheless, the applicability of the LSTM CF model is hindered by the scarce ACC data problem, as training the model requires a large amount of data. To address the ACC data scarcity problem, a transfer learning strategy for the LSTM model is further developed, leveraging on the large-scale open-source HV data and the similar driving patterns hidden in HV and ACC-equipped vehicles. In the transfer learning based LSTM model, a unified framework incorporating an alignment layer is developed to transfer the useful features from HV data and meanwhile calibrating the CF model with ACC data. Comparison results show that the proposed model outperforms other CF models which are built with only ACC data or using simple transfer learning methods. Further, microscopic simulations are performed to verify the applicability of the transfer learning based LSTM CF model.