Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
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.
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
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
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
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.
Nieto, M., et al. (2016). Optimising computer vision based ADAS: Vehicle detection case study. IET Intelligent Transport Systems, 10(3), 157–164. CrossRef
Velez, G., & Otaegui, O. (2017). Embedding vision-based advanced driver assistance systems: A survey. IET Intelligent Transport Systems, 11(3), 103–112. CrossRef
Nieto, M., et al. (2015). On creating vision-based advanced driver assistance systems. IET Intelligent Transport Systems, 9(1), 59–66. CrossRef
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
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.
Mearian, L. (2013). Self driving cars could create 1 Gb of data a second. Computer World.
Shi, W., et al. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. CrossRef
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
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
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
Gaikwad, V., & Lokhande, S. (2015). Lane departure identification for advanced driver assistance. IEEE Transactions on Intelligent Transportation Systems, 16(2), 910–918.
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
Fritsch, J., et al. (2008). Towards a human-like vision system for driver assistance. In 2008 IEEE intelligent vehicles symposium. IEEE.
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.
Keivani, A., Tapamo, J. R., & Ghayoor, F. (2017). Motion-based moving object detection and tracking using automatic k-means. In IEEE AFRICON. Cape Town.
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.
Chen, G., et al. (2014). A forward collision avoidance system adopting multi-feature vehicle detection. In IEEE international conference on consumer electronics. Taipei.
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.
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.
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.
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.
Sun, S. H., et al. (2017). Support for vehicle-to-everything services based on LTE. IEEE Wireless Communications, 23(3), 4–8. CrossRef
Araniti, G., et al. (2013). LTE for vehicular networking: A survey. IEEE Communications Magazine, 51(5), 148–157. CrossRef
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
GPP. (2017). Study on enhancement of 3GPP support for 5G V2X Services, 3GPP.
Google. (2017). Google Play. Google.
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.
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.
Satyanarayanan, M., et al. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4), 14–23. CrossRef
Samsung, Galaxy S8 Specifications. Samsung.
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).
- Collaborative Mobile Edge Computing in eV2X: A Solution for Low-Cost Driver Assistance Systems
- Springer US
Wireless Personal Communications
An International Journal
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X