基于Social-LSTM的多船舶协同轨迹预测模型实现，本项目使用PyTorch构建了Social-LSTM模型，进行实验验证了考虑船舶间相互影响的Social-LSTM轨迹预测模型相比于普通LSTM模型的预测准确性。
PyTorch implementation for Social-LSTM, which is built to predict multi-vessel trajectories. Experiments have been done to demonstrate that Social-LSTM can predict better trajectories than LSTM.
This project has been forked initially from https://github.com/quancore/social-lstm. If you find this code useful in your research, please cite the paper CVPR16_Social_LSTM.
- criterion.py: Python script for loss functions.
Including a GaussianLikehood loss function and a RMSE loss function. - utils.py: Python script for handling input train/test/validation data and preprocess it.
DataLoader class includes time_preprocess function, data load function, batch function and other data process function. - model.py: Python script includes Social-LSTM and Vanilla-LSTM.
Social-LSTM model implementation, Vanilla-LSTM model implementation and related functions. - helper.py: Python script includes various helper methods.
- train.py: Python script for training Social-LSTM model.
- train_vlstm.py: Python script for training Vanilla-LSTM model.
- test.py: Python script for model testing and getting output txt file for submission.
- validation.py: Python script for externally evaluate a trained model by getting validation error.
- visualize.py: Python script for visualizing predicted trajectories during train/test/validation sessions.
| Model | Neighbor Size | Mean Error | Final Error |
|---|---|---|---|
| Vanilla-LSTM | 0 | 0.6430 | 2.0371 |
| Social-LSTM | 0.021 | 0.6323 | 2.0572 |
| Social-LSTM | 0.020 | 0.6363 | 1.9084 |
| Social-LSTM | 0.019 | 0.6422 | 2.0148 |
Subjective display of predicted trajectories
