  Model-based and learning-based methods are two major types of methodologies
to model car following behaviors. Model-based methods describe the
car-following behaviors with explicit mathematical equations, while
learning-based methods focus on getting a mapping between inputs and outputs.
Both types of methods have advantages and weaknesses. Meanwhile, most
car-following models are generative and only consider the inputs of the speed,
position, and acceleration of the last time step. To address these issues, this
study proposes a novel framework called IDM-Follower that can generate a
sequence of following vehicle trajectory by a recurrent autoencoder informed by
a physical car-following model, the Intelligent Driving Model (IDM).We
implement a novel structure with two independent encoders and a self-attention
decoder that could sequentially predict the following trajectories. A loss
function considering the discrepancies between predictions and labeled data
integrated with discrepancies from model-based predictions is implemented to
update the neural network parameters. Numerical experiments with multiple
settings on simulation and NGSIM datasets show that the IDM-Follower can
improve the prediction performance compared to the model-based or
learning-based methods alone. Analysis on different noise levels also shows
good robustness of the model.
