psi-benchmark/PSI-Benchmark

Introduction

HomePage (http://pedestriandataset.situated-intent.net/)

Prediction of pedestrian behavior is critical for fully autonomous vehicles to drive in busy city streets safely and efficiently. The future autonomous cars need to fit into mixed conditions with not only technical but also social capabilities. It is important to estimate the temporal-dynamic intent changes of the pedestrians, provide explanations of the interaction scenes, and support algorithms with social intelligence.

The IUPUI-CSRC Pedestrian Situated Intent (PSI-1.0) benchmark dataset has two innovative labels besides comprehensive computer vision annotations. The first novel label is the dynamic intent changes for the pedestrians to cross in front of the ego-vehicle, achieved from 24 drivers with diverse backgrounds. The second one is the text-based explanations of the driver reasoning process when estimating pedestrian intents and predicting their behaviors during the interaction period.

PSI-2.0 covers 196 scenes including 110 scenes from PSI-1.0 and contains the labels from 74 new subjects. Additionally PSI-2.0 includes bounding box annotations for traffic objects and agents which can be linked with text descriptions and reasoning explanations for building vision-language models.

These innovative labels can enable computer vision tasks like pedestrian intent/behavior prediction, vehicle-pedestrian interaction segmentation, and video-to-language mapping for explainable algorithms. The dataset also contains driving dynamics and driving decision-making reasoning explanations.

@article{chen2021psi,
title={Psi: A pedestrian behavior dataset for socially intelligent autonomous car},
author={Chen, Tina and Jing, Taotao and Tian, Renran and Chen, Yaobin and Domeyer, Joshua and Toyoda, Heishiro and
Sherony, Rini and Ding, Zhengming},
journal={arXiv preprint arXiv:2112.02604},
year={2021} }

@inproceedings{jing2022inaction,
title={Inaction: Interpretable action decision making for autonomous driving},
author={Jing, Taotao and Xia, Haifeng and Tian, Renran and Ding, Haoran and Luo, Xiao and Domeyer, Joshua and Sherony,
Rini and Ding, Zhengming},
booktitle={European Conference on Computer Vision},
pages={370--387},
year={2022},
organization={Springer} }