Pedestrian and cyclist detection and intent estimation for autonomous vehicles : a survey

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Ahmed, Sarfraz and Huda, M. Nazmul and Rajbhandari, Sujan and Saha, Chitta and Elshaw, Mark and Kanarachos, Stratis (2019) Pedestrian and cyclist detection and intent estimation for autonomous vehicles : a survey. Applied Sciences, 9 (11). 2335. ISSN 2076-3417 (https://doi.org/10.3390/app9112335)

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Abstract

As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

ORCID iDs

Ahmed, Sarfraz, Huda, M. Nazmul, Rajbhandari, Sujan ORCID logoORCID: https://orcid.org/0000-0001-8742-118X, Saha, Chitta, Elshaw, Mark and Kanarachos, Stratis;
  • Item type:Article
    ID code:87232
    Dates:
    Date
    Event
    6 June 2019
    Published
    3 June 2019
    Accepted
    12 April 2019
    Submitted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Physics > Institute of Photonics
    Depositing user: Pure Administrator
    Date deposited:08 Nov 2023 11:54
    Last modified:20 Nov 2024 16:25
    URI:https://strathprints.strath.ac.uk/id/eprint/87232
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