A regime-embedded GRU/LSTM car-following model classifies driving states and uses them to reduce single-step acceleration, speed, and spacing errors on NGSIM data.
Analysis of asymmetric driving behavior using a self-learning approach,
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A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics
A regime-embedded GRU/LSTM car-following model classifies driving states and uses them to reduce single-step acceleration, speed, and spacing errors on NGSIM data.