Pedestrian Density Based Path Recognition and Risk Prediction for Autonomous Vehicles

Kasra Mokhtari, Ali Ayub, Vidullan Surendran, Alan R. Wagner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publication29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages517-524
Number of pages8
ISBN (Electronic)9781728160757
DOIs
StatePublished - Aug 2020
Event29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020 - Virtual, Naples, Italy
Duration: Aug 31 2020Sep 4 2020

Publication series

Name29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020

Conference

Conference29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020
Country/TerritoryItaly
CityVirtual, Naples
Period8/31/209/4/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Social Psychology
  • Communication

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