Master thesis (text is on Serbian) - Trajectory Forecasting on scenes with multiple moving objects
Install argoverse-api from Argoverse API GitHub.
Install packages:
pip3 install -r src/requirements.txt
Example of config files can be found in src/configs. Config src/configs/vectornet.yaml gives best results for TNT-Vectornet and src/configs/home.yaml gives
best results for HOME.
All training scripts have Tensorboard logging support.
Check config examples in configs directory.
First step for both approaches is HD map vectorization (config section: data_process). Note: Set visualize: True to visualize output.
python3 common_data_processing/script_vectorize_hd_maps.py --cfg [cfg]
Original paper can be found here.
To train TNT-Vectornet it is also required to transform vectorized HD maps (acquired from previous step) into polylines structure (config section: graph/data_process)
Note: Set visualize: True to visualize output.
python3 vectornet/script_transform_to_polylines.py --cfg [cfg]
To train a model run (50 epochs - 4h 45m):
python3 vectornet/script_train_vectornet.py --cfg [cfg]
To evaluate model run (requires trained TNT-Vectornet model):
python3 vectornet/script_evaluate_vectornet.py --cfg [cfg]
Estimated training time is for RTX 3070
Current results:
minADE:- custom (val): 1.03
- original (val): 0.73
- original (test): 0.91
minFDE:- custom (val): 1.91
- original (val): 1.29
- original (test): 1.45
MissRate:- custom (val): 0.30
- original (val): 0.09
- original (test): 0.22
Original paper can be found here
Transformation (rasterization) for HOME model is run during training.
To train HOME: Heatmap Estimation run (12 epochs - 4h 45m):
python3 home/script_train_heatmap.py --cfg [cfg]
To train HOME: Trajectory Forecaster run (30 epochs - 45m):
python3 home/script_train_trajectory_forecaster.py --cfg [cfg]
Estimated training time is for RTX 3070
To evaluate the model run (requires to be trained HOME-Heatmap and HOME-Forecaster model):
python3 home/script_evaluate_home.py --cfg [cfg]
Current results (optimal MR model):
minADE:- custom (val): 0.97
- original (val): -
- original (test): 0.92
minFDE:- custom (val): 1.71
- original (val): 1.28
- original (test): 1.45
MissRate:- custom (val): 0.15
- original (val): 0.07
- original (test): 0.10
@misc{madzemovic_home_tnt-vectornet,
author = {Adzemovic, Momir},
title = {Trajectory Forecasting on scenes with multiple moving objects},
year = {2022},
publisher = {GitHub, MATF},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Robotmurlock/TNT-VectorNet-and-HOME-Trajectory-Forecasting}},
}