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.

该研究提出一种基于关注力转换器模型的长序列跟车轨迹预测模型，通过跨层带关注的自注意机制和交叉注意机制在历史驾驶数据和未来 LV 速度数据之间建立联系，从而可以准确预测车辆的 Future FV Speed Profile，在上海自然驾驶研究中该模型表现出比传统智能驾驶模型、全连接神经网络模型、长短时记忆模型更高的准确性。