Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles

Saleh, K; Abobakr, A; Nahavandi, D; Iskander, J; Attia, M; Hossny, M; Nahavandi, S

Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles

Saleh, K Abobakr, A Nahavandi, D Iskander, J Attia, M Hossny, M Nahavandi, S

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 2020-2026
Issue Date:: 2019

Closed Access

	Filename	Description	Size
	08917291.pdf	Published version	2.53 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Saleh, K
dc.contributor.author	Abobakr, A
dc.contributor.author	Nahavandi, D
dc.contributor.author	Iskander, J
dc.contributor.author	Attia, M
dc.contributor.author	Hossny, M
dc.contributor.author	Nahavandi, S
dc.date	2019-10-27
dc.date.accessioned	2020-06-02T21:14:49Z
dc.date.available	2020-06-02T21:14:49Z
dc.date.issued	2019
dc.identifier.citation	2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 2020-2026
dc.identifier.isbn	978-1-5386-7024-8
dc.identifier.uri	http://hdl.handle.net/10453/141059
dc.description.abstract	One of the main barriers against the full deployment of autonomous vehicles in urban traffic environments is the understanding of the intentions and behaviours of the human around them. Moreover, understanding and predicting intentions of vulnerable road users such as cyclists is still one of the most challenging tasks. In this work, we are proposing a novel framework for the task of intent prediction of cyclists via hand signalling from point cloud scans. We utilised our developed data generation pipeline for generating synthetic point cloud scans of cyclists doing a set of hand signals in urban traffic environments. Then, we feed a sequence of the generated point cloud scans to our framework which jointly segments all cyclists instances and predicts their most probable intended actions in an end-to-end fashion. Our proposed framework has achieved superior results with 83% in F 1 -Measure score over the testing split of our generated dataset. Additionally, the proposed framework outperformed other compared baseline approaches with more than 39% improvement in F 1 -Measure score.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE Intelligent Transportation Systems Conference (ITSC)
dc.relation.ispartof	IEEE Intelligent Transportation Systems Conference
dc.relation.isbasedon	10.1109/ITSC.2019.8917291
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. 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.	en_US
dc.title	Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles
dc.type	Conference Proceeding
utslib.location.activity	Auckland, New Zealand
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-02T21:14:44Z
pubs.finish-date	2019-10-30
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-27
utslib.start-page	2020
dc.location	Piscataway, USA

Abstract:

One of the main barriers against the full deployment of autonomous vehicles in urban traffic environments is the understanding of the intentions and behaviours of the human around them. Moreover, understanding and predicting intentions of vulnerable road users such as cyclists is still one of the most challenging tasks. In this work, we are proposing a novel framework for the task of intent prediction of cyclists via hand signalling from point cloud scans. We utilised our developed data generation pipeline for generating synthetic point cloud scans of cyclists doing a set of hand signals in urban traffic environments. Then, we feed a sequence of the generated point cloud scans to our framework which jointly segments all cyclists instances and predicts their most probable intended actions in an end-to-end fashion. Our proposed framework has achieved superior results with 83% in F 1 -Measure score over the testing split of our generated dataset. Additionally, the proposed framework outperformed other compared baseline approaches with more than 39% improvement in F 1 -Measure score.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141059