Everything Polynomial: An Efficient and Robust Multi Modal Trajectory Predictor Baseline for Autonomous Driving

This repository contains the official implementation of "Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations", forthcoming.

Gettting Started

Step 1: Clone this repository and:

cd Everything-Polynomial/Argo2

Step 2: Create a conda environment and install the dependencies by running setup.sh:

Step 3: install the Argoverse 2 API(0.3.0) and download the Argoverse 2 Motion Forecasting Dataset following the Argoverse 2 User Guide. Rename the split to {split}_A2 (e.g. train to train_A2). The dataset directory should be organized as follows:

/data/datasets/
├── train_A2/
| ├── aaaa-aaaa-aaaa/
| ├── aaaa-aaaa-aaab/
| ├── aaaa-aaaa-aaac/
└── val_A2/
| ├── aaaa-aaaa-aaaa/
| ├── aaaa-aaaa-aaab/
| ├── aaaa-aaaa-aaac/

Step 4 (optional): # Download Waymo Open Motion Dataset (we use the scenario protocol form dataset), rename with {split}_WO and organize the data as follows:

/data/datasets/
├── train_WO/
| ├── training.tfrecord-00000-of-01000/
| ├── training.tfrecord-00001-of-01000/
| ├── training.tfrecord-00002-of-01000/
└── val_WO/
| ├── validation.tfrecord-00000-of-00150/
| ├── validation.tfrecord-00001-of-00150/
| ├── validation.tfrecord-00002-of-00150/

Install the Waymo Open Dataset API as follows:

pip install waymo-open-dataset-tf-2-6-0

Preprocessing

Modify the split in preprocess_argo2.py run:

python preprocess_argo2.py

for Waymo:

python preprocess_waymo.py

Note: The preprocessing of complete Argoverse 2 takes almost two days on my machine. This is due to the tracking is done sequentially to each object. Additionally, fitting all the lane segments also takes considerable time.

We provide the preprocessed data for Argoverse 2 (train, val, test) and Waymo with 5s history (val). We have removed the scenario with id 598d73573ff233fa from the WO validation due to the tracking issue with the target agent."

Training

Modify the model_name in train.py to either "EP_F" or "EP_Q" and run:

python train.py

Note: Training EP_F takes ~6 hours on a single Tesla T4. Training EP_Q takes ~2.5 days on a single Tesla T4.

During training, the checkpoints will be saved in logs/weights/model_name/data automatically. To monitor the training process:

tensorboard --logdir logs/weights/

Evaluation

To evaluate the prediction performance, please run the notebook evaluation and submission.ipynb

Pretrained Models

We provide the checkpoints of pretrained EP_F and EP-Q (with and without homogenizatioin) in checkpoints/. You can evaluate the pretrained models using the aforementioned evaluation script, or have a look at the training process via TensorBoard:

tensorboard --logdir checkpoints/

Pretrained Models & Results

In-Distribution Quantitative Results (6s Prediction Horizon)

Model	Dataset	Split	Checkpoint	minADE (K=1)	minFDE (K=1)	minADE (K=6)	minFDE (K=6)
EP_F	A2	Val	EP_F	1.88	4.55	0.79	1.53
EP_F	A2	Test	EP_F	1.89	4.57	0.80	1.53
EP_Q	A2	Val	EP_Q	2.12	5.39	0.83	1.68
EP_Q	A2	Test	EP_Q	2.13	5.42	0.84	1.68

Out-of-Distribution Quantitative Results (4.1s Prediction Horizon)

Model	Dataset	Split	Checkpoint	minADE (K=1)	minFDE (K=1)	minADE (K=6)	minFDE (K=6)
EP_F	A2	Val	EP_F	1.09	2.57	0.48	0.87
EP_F	WO	Val	EP_F	1.30	3.41	0.60	1.34
EP_Q	A2	Val	EP_Q	1.16	2.82	0.49	0.92
EP_Q	WO	Val	EP_Q	1.13	2.93	0.53	1.14

Results could be slightly different due to retrain.

Acknowledgements

This repo benefits from QCNet, Forecast-MAE. Thanks for their great works.

License

This repository is licensed under BSD-3-Clause.