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

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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.

Originele taal-2Engels
Titel2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's5
ISBN van elektronische versie978-1-7281-1217-6
DOI's
StatusGepubliceerd - 1 apr 2019
Evenement89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Maleisië
Duur: 28 apr 20191 mei 2019

Congres

Congres89th IEEE Vehicular Technology Conference, VTC Spring 2019
Verkorte titel VTC Spring 2019
LandMaleisië
StadKuala Lumpur
Periode28/04/191/05/19

Vingerafdruk

Vehicular ad hoc networks
Vehicular Ad Hoc Networks
Object Detection
Packet Loss
Packet loss
Learning systems
Communication
Sensors
Sensor
Intelligent vehicle highway systems
Safety
Traffic
Real-time
Intelligent Vehicle
Data Dissemination
Network performance
Module
Information Sharing
Point Cloud
Network Performance

Citeer dit

Wang, Y., Menkovski, V., Ho, I. W. H., & Pechenizkiy, M. (2019). VANET meets deep learning: the effect of packet loss on the object detection performance. In 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings [8746657] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/VTCSpring.2019.8746657
Wang, Yuhao ; Menkovski, Vlado ; Ho, Ivan Wang Hei ; Pechenizkiy, Mykola. / VANET meets deep learning : the effect of packet loss on the object detection performance. 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Piscataway : Institute of Electrical and Electronics Engineers, 2019.
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title = "VANET meets deep learning: the effect of packet loss on the object detection performance",
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.",
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Wang, Y, Menkovski, V, Ho, IWH & Pechenizkiy, M 2019, VANET meets deep learning: the effect of packet loss on the object detection performance. in 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings., 8746657, Institute of Electrical and Electronics Engineers, Piscataway, 89th IEEE Vehicular Technology Conference, VTC Spring 2019, Kuala Lumpur, Maleisië, 28/04/19. https://doi.org/10.1109/VTCSpring.2019.8746657

VANET meets deep learning : the effect of packet loss on the object detection performance. / Wang, Yuhao; Menkovski, Vlado; Ho, Ivan Wang Hei; Pechenizkiy, Mykola.

2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Piscataway : Institute of Electrical and Electronics Engineers, 2019. 8746657.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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AB - 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.

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PB - Institute of Electrical and Electronics Engineers

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Wang Y, Menkovski V, Ho IWH, Pechenizkiy M. VANET meets deep learning: the effect of packet loss on the object detection performance. In 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers. 2019. 8746657 https://doi.org/10.1109/VTCSpring.2019.8746657