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

Version 2 2024-06-04, 02:21
Version 1 2020-01-21, 02:03
conference contribution
posted on 2024-06-04, 02:21 authored by K Saleh, A Abobakr, D Nahavandi, J Iskander, M Attia, M Hossny, S Nahavandi
© 2019 IEEE. 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 F1-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 F1-Measure score.

History

Pagination

2020-2026

Location

Auckland, New Zealand

Start date

2019-10-27

End date

2019-10-30

ISBN-13

9781538670248

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ITSC 2019 Proceedings of the IEEE Intelligent Transportation Systems Conference

Event

Intelligent Transportation Systems. Conference (2019 : Auckland, New Zealand)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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