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Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions

This repository includes the code and dataset information required for reproducing the results in our paper. Besides, we also integrated the source code of our baseline method, DeepTL-Lane-Change-Classification, into this repo. The baseline approach infers the risk level of lane change video clips with deep CNN+LSTM. Our approach incoporates both spatial modeling and temporal modeling in the task of subjective risk assessment.

NOTE: For a more comprehensive implementation of the code from this project and our other related work, please refer to our new open-source tool for AV scene-graph generation and embedding roadscene2vec.

The architecture of our approach is illustrated as below,

As for fabricating the lane-changing datasets, we use Carla CARLA 0.9.8 which is an open-source autonomous car driving simulator. Besides, we also utilized the scenario_runner which was designed for CARLA challenge event. For real-driving datasets, we used Honda-Driving Dataset (HDD) in our experiments. We published the converted scene-graph datasets used in our paper here.

The architecture of this repository is as below:

  • sg-risk-assessment/: this folder consists of all the related source files used for our scene-graph based approach.
  • baseline-risk-assessment/: this folder consists of all the related source files used for the baseline method.
  • sg_risk_assessment.py: the script that triggers our scene-graph based approach.
  • baseline_risk_assessment.py: the script that triggers the baseline model.

To Get Started

We recommend our potential users to use Anaconda as the primary virtual environment. The requirements to run through our repo are as follows,

  • python >= 3.6
  • torch == 1.6.0
  • torch_geometric == 1.6.1

Our recommended command sequence is as follows:

$ conda create --name sg_risk_assessment python=3.6
$ conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
$ python -m pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
$ python -m pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
$ python -m pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
$ python -m pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.6.0+cu101.html
$ python -m pip install torch-geometric==1.6.1
$ python -m pip install -r requirements.txt

This set of commands assumes you to have cuda10.1 in your local. Please refer to the installation guides of torch and pytorch_geometric if you have different environment settings.

Usages

For running the sg-risk-assessment in this repo, you may refer to the following commands:

$ python sg_risk_assessment.py --pkl_path risk-assessment/scenegraph/synthetic/271_dataset.pkl

# --pkl_path + [wherever path that stores the downloaded pkl]
# For tuning hyperparameters view the config class of sg_risk_assessment.py

For running the baseline-risk-assessment in this repo, you may refer to the following commands:

$ python baseline_risk_assessment.py --load_pkl True --pkl_path risk-assessment/scene/synthetic/271_dataset.pkl

# --pkl_path + [wherever path that stores the downloaded pkl]
# For tuning hyperparameters view the config class of baseline_risk_assessment.py

After running these commands, the expected outputs are a dump of metrics logged by wandb:

wandb:                    train_recall ▁████████████████████
wandb:                   val_precision █▁▅▄▅▄▆▆▆▅▄▄▇▆▅▆▅▇▆▆▆
wandb:                      val_recall ▁████████████████████
wandb:                       train_fpr ▁█▅▅▄▅▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂
wandb:                       train_tnr █▁▄▅▅▅▆▇▇▇▇▇▇▇▇▇▇▇▇▇▇
wandb:                       train_fnr █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                         val_fpr ▁█▄▅▄▅▃▃▃▄▄▅▂▃▃▃▄▂▃▃▃
wandb:                         val_tnr █▁▆▄▆▄▆▆▆▆▅▄▇▆▆▆▆▇▆▆▆
wandb:                         val_fnr █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                      best_epoch ▁▁▂▂▂▂▃▃▄▄▄▄▅▅▅▅▅▇▇▇█
wandb:                   best_val_loss █▃▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                    best_val_acc ▁▆█▇█████████████████
wandb:                    best_val_auc ▁▅▆▆▇▇▇▇████▇▇▇▇▇████
wandb:                    best_val_mcc ▁▇███████████████████
wandb:           best_val_acc_balanced ▁████████████████████
wandb:                       train_mcc ▁▇▇▇▇▇███████████████
wandb:                         val_mcc ▁▇███████████████████

A graphical visualization of the model outputs including loss and additional metrics can be viewed by creating and linking your runs to wandb.

Citation

Please kindly consider citing our paper if you find our work useful for your research

@article{yu2020scene,
  title={Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions},
  author={Yu, Shih-Yuan and Malawade, Arnav V and Muthirayan, Deepan and Khargonekar, Pramod P and Faruque, Mohammad A Al},
  journal={arXiv preprint arXiv:2009.06435},
  year={2020}
}