Camara, F orcid.org/0000-0002-2655-1228, Bellotto, N, Cosar, S et al. (11 more authors) (2021) Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behaviour. IEEE Transactions on Intelligent Transportation Systems, 22 (9). pp. 5453-5472. 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 II 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 psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV 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: | Review, survey, pedestrians, autonomous vehicles, sensing, detection, tracking, trajectory prediction, pedestrian interaction, microscopic and macroscopic behavior models, game-theoretic models, signalling models, eHMI, datasets |
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) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EU - European Union 723395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jul 2020 15:38 |
Last Modified: | 26 Jul 2022 13:35 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TITS.2020.3006767 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162669 |