VANET meets deep learning: the effect of packet loss on the object detection performance

Yuhao Wang, Vlado Menkovski, Ivan Wang Hei Ho, Mykola Pechenizkiy

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)
    3 Downloads (Pure)

    Abstract

    The integration of machine learning and inter- vehicle communications enables various active safety measures in internet-of-vehicles. Specifically, the environmental perception is processed by the deep learning module from vehicular sensor data, and the extended perception range is achieved by exchanging traffic-related information through inter-vehicle communications. Under such condition, the intelligent vehicles can not only percept the surrounding environment from self-collected sensor data, but also expand their perception range through the information sharing mechanism of Vehicular Ad-hoc Network (VANET). However, the dynamic urban environment in VANET leads to a number of issues, such as the effect of packet loss on the real-time perception accuracy of the received sensor data. In this work, we propose a point cloud object detection module via an end-to-end deep learning system and enable wireless communications between vehicles to enhance driving safety and facilitate real-time 3D mapping construction. Besides, we build a semi- realistic traffic scenario based on the Mong Kok district in Hong Kong to analyze the network performance of data dissemination under the dynamic environment. Finally, we evaluate the impact of data loss on the deep-learning-based object detection performance. Our results indicate that data loss beyond 50% (which is a common scene based on our simulation) can lead to a rapid decline of the object detection accuracy.

    Original languageEnglish
    Title of host publication2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers
    Number of pages5
    ISBN (Electronic)978-1-7281-1217-6
    DOIs
    Publication statusPublished - 1 Apr 2019
    Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
    Duration: 28 Apr 20191 May 2019

    Conference

    Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
    Abbreviated title VTC Spring 2019
    CountryMalaysia
    CityKuala Lumpur
    Period28/04/191/05/19

    Keywords

    • 3D Point Cloud
    • Autonomous driving
    • Deep learning
    • SUMO
    • VANET

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