Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2021

24.04.2019

Collaborative Mobile Edge Computing in eV2X: A Solution for Low-Cost Driver Assistance Systems

verfasst von: Arghavan Keivani, Farzad Ghayoor, Jules-Raymond Tapamo

Erschienen in: Wireless Personal Communications | Ausgabe 3/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Collision avoidance systems are of great importance in advanced driver assistance systems (ADAS) to increase the level of safety. However, as a result of their excessive costs, are not considered as a feature in lower-end vehicles. The concept of vehicular communication has emerged recently and added a new dimension for promoting safety in automotive. In this paper, the opportunities for implementation of the cost-effective ADAS through next generation of vehicular communication are discussed. A conceptual model for a vision-based driver assistance system based on collaborative mobile edge computing (MEC) is proposed. Cloudlets are the edges of the 5G cellular network and are capable of providing MEC solutions to their connected devices. The proposed system monitors the vehicles driving in front of an ego car and notifies the driver upon detecting hazardous conditions caused by other vehicles movements. This system can be employed as a cost-effective driver assistance system and has the potential to reach the mass market and be used in all ranges of vehicles including the lower-end models.
Vorheriger Artikel Effectiveness Comparison of the Traditional Problem-Based Learning and the Proposed Problem-Based Blended Learning in Creative Writing: A Case Study in Thailand
Nächster Artikel A Method of Football-Offside Detection Using Multiple Cameras for an Automatic Linesman Assistance System

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Keivani, A., Ghayoor, F., & Tapamo, J. R. (2017). A vision-based driver assistance system using collaborative edge computing. In Global Wireless Summit (GWS). Cape Town.
2.
Trivedi, M. M., Gandhi, T., & McCall, J. (2007). Looking-in and looking-out of a vehicle: Computer-vision-based enhanced vehicle safety. IEEE Transactions on Intelligent Transportation Systems, 8(1), 108–120.CrossRef
3.
Cherng, S., et al. (2009). Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system. IEEE Transactions on Intelligent Transportation Systems, 10(1), 70–82.CrossRef
4.
Sivaraman, S., & Trivedi, M. M. (2013). Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1773–1795.CrossRef
5.
Horgan, J., et al. (2015). Vision-based driver assistance systems: Survey, taxonomy and advances. In IEEE 18th international conference on intelligent transportation systems (ITSC). Las Palmas.
6.
Nieto, M., et al. (2016). Optimising computer vision based ADAS: Vehicle detection case study. IET Intelligent Transport Systems, 10(3), 157–164.CrossRef
7.
Velez, G., & Otaegui, O. (2017). Embedding vision-based advanced driver assistance systems: A survey. IET Intelligent Transport Systems, 11(3), 103–112.CrossRef
8.
Nieto, M., et al. (2015). On creating vision-based advanced driver assistance systems. IET Intelligent Transport Systems, 9(1), 59–66.CrossRef
9.
He, W., Yan, G., & Xu, L. D. (2014). Developing vehicular data cloud services in the IoT environment. IEEE Transactions on Industrial Informatics, 10(2), 1587–1595.CrossRef
10.
Keivani, A., Ghayoor, F., & Tapamo, J. R. (2018). A review of recent methods of task scheduling in cloud computing. In 19th IEEE mediterranean electrotechnical conference (MELECON). Marrakesh.
11.
Mearian, L. (2013). Self driving cars could create 1 Gb of data a second. Computer World.
12.
Shi, W., et al. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.CrossRef
13.
Mukhtar, A., Xia, L., & Tang, T. B. (2015). Vehicle detection techniques for collision avoidance systems: A review. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2318–2338.CrossRef
14.
Dollar, P., et al. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 743–761.CrossRef
15.
Sivaraman, S., & Trivedi, M. M. (2013). Vehicle detection by independent parts for urban driver assistance. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1597–1608.CrossRef
16.
Gaikwad, V., & Lokhande, S. (2015). Lane departure identification for advanced driver assistance. IEEE Transactions on Intelligent Transportation Systems, 16(2), 910–918.
17.
Mogelmose, A., Trivedi, M. M., & Moeslund, T. B. (2012). Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1484–1497.CrossRef
18.
Fritsch, J., et al. (2008). Towards a human-like vision system for driver assistance. In 2008 IEEE intelligent vehicles symposium. IEEE.
19.
Sotelo, M. A., et al. (2004). Vision-based adaptive cruise control for intelligent road vehicles. In 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS)(IEEE Cat. No. 04CH37566). IEEE.
20.
Keivani, A., Tapamo, J. R., & Ghayoor, F. (2017). Motion-based moving object detection and tracking using automatic k-means. In IEEE AFRICON. Cape Town.
21.
Ren, B., et al. (2014). Vision-based forward collision warning system design supported by a field-test verification platform. In IEEE intelligent vehicles symposium proceedings. Dearborn.
22.
Chen, G., et al. (2014). A forward collision avoidance system adopting multi-feature vehicle detection. In IEEE international conference on consumer electronics. Taipei.
23.
Lin, H.Y., et al. (2012). Lane departure and front collision warning using a single camera. In IEEE international symposium on intelligent signal processing and communications systems (ISPACS). Taipei.
24.
Gu, Q., et al. (2015). Vision-based multi-scaled vehicle detection and distance relevant mix tracking for driver assistance system. Optical Review, 22(2), 197–209.CrossRef
25.
Ozaki, N., et al. (2015). Implementation and evaluation of image recognition algorithm for an intelligent vehicle using heterogeneous multi-core SoC. In 20th Asia and South Pacific design automation conference. Chiba.
26.
Petrovai, A., Danescu, R., & Nedevschi, S. (2015). A stereovision based approach for detecting and tracking lane and forward obstacles on mobile devices. In IEEE intelligent vehicles symposium (IV). Seoul.
27.
802.11-2016 I.S.N. (2016). Standard for information technology—specific requirements—part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications.
28.
Sun, S. H., et al. (2017). Support for vehicle-to-everything services based on LTE. IEEE Wireless Communications, 23(3), 4–8.CrossRef
29.
Araniti, G., et al. (2013). LTE for vehicular networking: A survey. IEEE Communications Magazine, 51(5), 148–157.CrossRef
30.
Chen, S., et al. (2017). Vehicle-to-everything (v2x) services supported by LTE-based systems and 5G. IEEE Communications Standards Magazine, 1(2), 70–76.CrossRef
31.
GPP. (2017). Study on enhancement of 3GPP support for 5G V2X Services, 3GPP.
32.
33.
Ojeda-Andablo, J.A., et al. (2016). Support and monitoring trajectory paths for vehicles using mobile devices. In International conference on electronics, communications and computers (CONIELECOMP). Cholula.
34.
Meng, R., et al. (2015). OmniView: A mobile collaborative system for assisting drivers with a map of surrounding traffic. In International conference on computing, networking and communications (ICNC). Garden Grove.
35.
Satyanarayanan, M., et al. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4), 14–23.CrossRef
36.
Andrews, J. G., et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.CrossRef
37.
Samsung, Galaxy S8 Specifications. Samsung.
38.
García-Pérez, C., Merino, P. (2017). Experimental evaluation of fog computing techniques to reduce latency in LTE networks. In Emerging Telecommunications Technologies (pp. 1–17).
Metadaten
Titel
Collaborative Mobile Edge Computing in eV2X: A Solution for Low-Cost Driver Assistance Systems
verfasst von
Arghavan Keivani
Farzad Ghayoor
Jules-Raymond Tapamo
Publikationsdatum
24.04.2019
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06401-2

Weitere Artikel der Ausgabe 3/2021

Wireless Personal Communications 3/2021 Zur Ausgabe

OriginalPaper

Smart Care Environment with Food Recognition for Personalization Support: A Case Study of Thai Seniors

OriginalPaper

Shifted 2-N-PSK Method for the Detection of Pilot Contamination Attacks

OriginalPaper

A Threat Analysis of Human Bond Communications

OriginalPaper

Effectiveness Comparison of the Traditional Problem-Based Learning and the Proposed Problem-Based Blended Learning in Creative Writing: A Case Study in Thailand

OriginalPaper

The Wireless Access for Future Smart Cities as a Large Scale Complex Cyber Physical System

OriginalPaper

2D Pose-Invariant Face Recognition Using Single Frontal-View Face Database

Neuer Inhalt

    Bildnachweise