This repository is the code of GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving on the Baidu Apollo Trajectory dataset. GRIP++ is an enhanced version of our GRIP (GRIP: Graph-based Interaction-aware Trajectory Prediction).
This code is shared only for research purposes, and this cannot be used for any commercial purposes.
- Modify "data_root" in data_process.py and then run the script to preprocess the data.
$ python data_process.py- Train the model. We trained the model on a single Nvidia Titan Xp GPU. If your GPU has the same precision, you should get the exact same results. The "training_log.txt" is my training log. If you download the code and run it directly, you should see similar outputs.
$ python main.py
# The following are the first 10 training iterations:
#######################################Train
# |2019-09-20 16:50:43.146035| Epoch: 0/ 500| Iteration: 0| Loss:2.69767785|lr: 0.001|
# |2019-09-20 16:50:43.247776| Epoch: 0/ 500| Iteration: 0| Loss:1.39082634|lr: 0.001|
# |2019-09-20 16:50:43.327926| Epoch: 0/ 500| Iteration: 0| Loss:1.42024708|lr: 0.001|
# |2019-09-20 16:50:43.394658| Epoch: 0/ 500| Iteration: 0| Loss:1.32363927|lr: 0.001|
# |2019-09-20 16:50:43.454833| Epoch: 0/ 500| Iteration: 0| Loss:1.15358388|lr: 0.001|
# |2019-09-20 16:50:43.515517| Epoch: 0/ 500| Iteration: 0| Loss:1.15672326|lr: 0.001|
# |2019-09-20 16:50:43.575027| Epoch: 0/ 500| Iteration: 0| Loss:0.93675584|lr: 0.001|
# |2019-09-20 16:50:43.634769| Epoch: 0/ 500| Iteration: 0| Loss:0.90181452|lr: 0.001|
# |2019-09-20 16:50:43.694374| Epoch: 0/ 500| Iteration: 0| Loss:0.75979233|lr: 0.001|Once you trained the model, you can test the trained models on the testing subset.
- Our model predicts future locations for all observed objects simultaneously.
- Using separate models for different types of objects should achieve better performance.
| Method | Epoch | WSADE | ADEv | ADEp | ADEb | WSFDE | FDEv | FDEp | FDEb |
|---|---|---|---|---|---|---|---|---|---|
| TrafficPredict | 8.5881 | 7.9467 | 7.1811 | 12.8805 | 24.2262 | 12.7757 | 11.121 | 22.7912 | |
| GRIP | Epoch16 | 1.2632 | 2.2511 | 0.718 | 1.8024 | 2.3713 | 4.0863 | 1.3838 | 3.4155 |
| GRIP | Epoch18 | 1.2648 | 2.2515 | 0.7142 | 1.8193 | 2.3677 | 4.0863 | 1.3732 | 3.4274 |
| GRIP | Epoch20 | 1.2721 | 2.24 | 0.717 | 1.8558 | 2.3921 | 4.0762 | 1.3791 | 3.5318 |
| GRIP | Combine | 1.2588 | 2.2400 | 0.7142 | 1.8024 | 2.3631 | 4.0762 | 1.3732 | 3.4155 |
We use the following way to combine multiple results.
- epoch20 -> 1, 2 (car)
- epoch18 -> 3 (pedestrian)
- epoch16 -> 4 (bike)
Please cite our papers if you used our code. Thanks.
@inproceedings{2019itsc_grip,
author = {Li, Xin and Ying, Xiaowen and Chuah, Mooi Choo},
booktitle = {2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC)},
organization = {IEEE},
title = {GRIP: Graph-based Interaction-aware Trajectory Prediction},
year = {2019}
}
@article{li2020gripplus,
title={GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving},
author={Li, Xin and Ying, Xiaowen and Chuah, Mooi Choo},
journal={arXiv preprint arXiv:1907.07792},
year={2020}
}
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