@inproceedings{6017b3e713a94c9fa57c5a86023fa39d,
title = "Pedestrian Density Based Path Recognition and Risk Prediction for Autonomous Vehicles",
abstract = "Human drivers continually use social information to inform their decision making. We believe that incorporating this information into autonomous vehicle decision making would improve performance and importantly safety. This paper investigates how information in the form of pedestrian density can be used to identify the path being travelled and predict the number of pedestrians that the vehicle will encounter along that path in the future. We present experiments which use camera data captured while driving to evaluate our methods for path recognition and pedestrian density prediction. Our results show that we can identify the vehicle's path using only pedestrian density at 92.4% accuracy and we can predict the number of pedestrians the vehicle will encounter with an accuracy of 70.45%. These results demonstrate that pedestrian density can serve as a source of information both perhaps to augment localization and for path risk prediction.",
author = "Kasra Mokhtari and Ali Ayub and Vidullan Surendran and Wagner, {Alan R.}",
note = "Funding Information: This work was supported by Air Force Office of Scientific Research contract FA9550-17-1-0017. Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020 ; Conference date: 31-08-2020 Through 04-09-2020",
year = "2020",
month = aug,
doi = "10.1109/RO-MAN47096.2020.9223554",
language = "English (US)",
series = "29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "517--524",
booktitle = "29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",
address = "United States",
}