Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking

Camara, Fanta, Bellotto, Nicola, Cosar, Serhan , Nathanael, Dimitris, Althoff, Mathias, Wu, Jingyuan, Ruenz, Johannes, Dietrich, Andre and Fox, Charles (2020) Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking. IEEE Transactions on Intelligent Transport Systems . ISSN 1524-9050

Full content URL: https://doi.org/10.1109/TITS.2020.3006768

Documents
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking
Accepted Manuscript
[img]
[Download]
Supplemental Material
[img]
[Download]
[img]
Preview
PDF
part1.pdf - Whole Document

739kB
[img]
Preview
PDF
part_1_supplmental.pdf - Supplemental Material

360kB
Item Type:Article
Item Status:Live Archive

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.

Keywords:pedestrians, autonomous vehicles, tracking, trajectory
Subjects:N Business and Administrative studies > N850 Transport Studies
Divisions:College of Science > School of Computer Science
ID Code:41705
Deposited On:21 Aug 2020 15:16

Repository Staff Only: item control page