TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer

• URL: http://arxiv.org/abs/2202.03183v1
• Date: Fri, 4 Feb 2022 07:59:22 GMT
• Title: TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer
• Authors: Meixin Zhu, Simon S. Du, Xuesong Wang, Hao (Frank) Yang, Ziyuan Pu, Yinhai Wang
• Abstract summary: We develop a long-sequence car-following trajectory prediction model based on the attention-based Transformer model. We train and test our model with 112,597 real-world car-following events extracted from the Shanghai Naturalistic Driving Study (SH-NDS)
• Score: 44.93030718234555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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