Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking. Camara, F., Bellotto, N., Cosar, S., Nathanael, D., Althoff, M., Wu, J., Ruenz, J., Dietrich, A., & Fox, C. IEEE Transactions on Intelligent Transport Systems, 22(10):6131–6151, IEEE, October, 2021. ![Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract–Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.
@article{lincoln41705,
volume = {22},
number = {10},
month = {October},
author = {Fanta Camara and Nicola Bellotto and Serhan Cosar and Dimitris Nathanael and Mathias Althoff and Jingyuan Wu and Johannes Ruenz and Andre Dietrich and Charles Fox},
title = {Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking},
publisher = {IEEE},
year = {2021},
journal = {IEEE Transactions on Intelligent Transport Systems},
doi = {10.1109/TITS.2020.3006768},
pages = {6131--6151},
keywords = {ARRAY(0x56546f014e70)},
url = {https://eprints.lincoln.ac.uk/id/eprint/41705/},
abstract = {Abstract{--}Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.}
}
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Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. 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