Camara, F orcid.org/0000-0002-2655-1228, Bellotto, N, Cosar, S et al. (6 more authors) (2020) Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050
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, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior 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 behavior modelling, prediction and interaction control.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. All rights reserved. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Sensors , Cameras , Psychology , Autonomous vehicles , Predictive models , Computational modeling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Funding Information: | Funder Grant number EU - European Union 723395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jul 2020 15:51 |
Last Modified: | 21 Jan 2021 13:54 |
Status: | Published online |
Publisher: | IEEE |
Identification Number: | 10.1109/TITS.2020.3006768 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162670 |