  Car-following refers to a control process in which the following vehicle (FV)
tries to keep a safe distance between itself and the lead vehicle (LV) by
adjusting its acceleration in response to the actions of the vehicle ahead. The
corresponding car-following models, which describe how one vehicle follows
another vehicle in the traffic flow, form the cornerstone for microscopic
traffic simulation and intelligent vehicle development. One major motivation of
car-following models is to replicate human drivers' longitudinal driving
trajectories. To model the long-term dependency of future actions on historical
driving situations, we developed a long-sequence car-following trajectory
prediction model based on the attention-based Transformer model. The model
follows a general format of encoder-decoder architecture. The encoder takes
historical speed and spacing data as inputs and forms a mixed representation of
historical driving context using multi-head self-attention. The decoder takes
the future LV speed profile as input and outputs the predicted future FV speed
profile in a generative way (instead of an auto-regressive way, avoiding
compounding errors). Through cross-attention between encoder and decoder, the
decoder learns to build a connection between historical driving and future LV
speed, based on which a prediction of future FV speed can be obtained. We train
and test our model with 112,597 real-world car-following events extracted from
the Shanghai Naturalistic Driving Study (SH-NDS). Results show that the model
outperforms the traditional intelligent driver model (IDM), a fully connected
neural network model, and a long short-term memory (LSTM) based model in terms
of long-sequence trajectory prediction accuracy. We also visualized the
self-attention and cross-attention heatmaps to explain how the model derives
its predictions.
