Source code for the following paper:
Zhu, Meixin, et al. "TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer." arXiv preprint arXiv:2202.03183 (2022).
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 real-world car-following events extracted from the Shanghai Naturalistic Driving Study (SH-NDS) and HighD datasets. Results show that the model outperforms the traditional intelligent driver model (IDM), IDM with memory effects, a fully connected neural network model, a long short-term memory (LSTM) based model, and customized state-of-the-art trajectory prediction models 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.
pip install -r requirements.txt
The model has been tested with SH-NDS, highD, and NGSIM. Extracted car-following events are stored in data/raw_data folder. For privacy issues, SH-NDS related data is not published here, please contact the authors for access. You could use highD data for experiments first.
Train transfollower, lstm, and nn models:
python train.py
Train Trajectron++ model:
python train_trajectron.py
Train STAR model:
python train_star.py
Train IDM/IDMM models:
python IDM_calibrate.py or python IDMM_calibrate.py
Test of all data-driven models using model checkpoints stored in checkpoint/:
python test.py
Test IDM/IDMM models:
python IDM_test.py or python IDMM_test.py
Pretrained models are stored in checkpoints/ folder.
Trajectron++: https://github.com/StanfordASL/Trajectron-plus-plus
STAR: https://github.com/Majiker/STAR