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Discovering and Understanding Algorithmic Biases in Autonomous Pedestrian Trajectory Predictions

Andrew Bae, Susu Xu

This repo contains the code for our workshop paper: Discovering and Understanding Algorithmic Biases in Autonomous Pedestrian Trajectory Predictions, published in The Fourth Workshop on Continual and Multimodal Learning for Internet of Things (CML-IOT 2022) at SenSys 2022.

Environment

Our code was implemented on a desktop computer with an NVIDIA GeForece RTX 3080 10GB.

Install the conda environment from the yml file:

conda env create -f environment.yml

Data Preparation

The JAAD and PIE datasets can be downloaded from their respective websites. Follow their directions to extract the necessary files. To obtain the TITAN dataset, refer to this page and contact the authors directly.

Training

We used the checkpoints provided by the BiTraP authors for the JAAD and PIE datasets.

Checkpoints for SGNet on JAAD and PIE used to be made availible by the authors, but it seems like their linked folder no longer contain any checkpoints.

We trained the models on the TITAN dataset from scratch.

We provide all of our utilized checkpoints in the checkpoints folder.

Training commands if you would like to train the models yourself:

BiTraP-D

set K in bitrap_np_*INSERT_DATASET* to 1.

cd BiTraP
python tools/train.py --config_file **DIR_TO_THE_YML_FILE**

BiTraP-NP

set K in bitrap_np_*INSERT_DATASET* to 20.

cd BiTraP
python tools/train.py --config_file **DIR_TO_THE_YML_FILE**

SGNet

cd SGNet
python tools/**INSERT_DATASET**/train_deterministic --dataset **INSERT_DATASET** --model SGNet

Testing

A .pkl file will be generated after each test. This will be used later for the data analysis.

BitraP-D

set K in bitrap_np_*INSERT_DATASET* to 1.

cd BiTraP
python tools/test.py --config_file configs/bitrap_np_PIE.yml CKPT_DIR **DIR_TO_CKPT** TEST.AGE **INSERT_AGE** TEST.SPLIT TEST TEST.GENDER **GENDER**

BiTraP-NP

set K in bitrap_np_*INSERT_DATASET* to 20.

cd BiTraP
python tools/test.py --config_file configs/bitrap_np_PIE.yml CKPT_DIR **DIR_TO_CKPT** TEST.AGE **AGE** TEST.SPLIT TEST TEST.GENDER **GENDER** --split test --age **INSERT_AGE** --gender **INSERT_GENDER**

SGNet

cd SGNet
python tools/**INSERT_DATASET**/eval_deterministic --dataset **INSERT_DATASET** --model SGNet --checkpoint **DIR_TO_CKPT** --split test --age **INSERT_AGE** --gender **INSERT_GENDER**

Options for **INSERT_AGE**

JAAD: all, child, adult, elderly, no_label

PIE: all, child, adult, elderly

TITAN: all, child, adult, elderly

Options for **INSERT_GENDER**

JAAD: all, male, female

PIE: all, male, female

TITAN: all

Analysis

Data analysis was done using various Jupyter notebooks. They have been provided in the "notebooks" folder, along with the .pkl files generated from testing.

Citation

@inproceedings{10.1145/3560905.3568433,
author = {Bae, Andrew and Xu, Susu},
title = {Discovering and Understanding Algorithmic Biases in Autonomous Pedestrian Trajectory Predictions},
year = {2023},
isbn = {9781450398862},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3560905.3568433},
doi = {10.1145/3560905.3568433},
booktitle = {Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
pages = {1155–1161},
numpages = {7},
keywords = {trajectory prediction, bias, fairness, algorithm evaluation},
location = {Boston, Massachusetts},
series = {SenSys '22}
}