GRIP

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).

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License

This code is shared only for research purposes, and this cannot be used for any commercial purposes.

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Training

1. Modify "data_root" in data_process.py and then run the script to preprocess the data.

$ python data_process.py

2. 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|

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Submission

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)

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Citation

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}
}