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Emerging Trends in Vehicular Communication Networks

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Emerging Wireless Communication and Network Technologies

Abstract

The potential of connected and autonomous vehicles can be greatly magnified by the synergistic exploitation of a variety of upcoming communication technologies that may be embedded in next-generation vehicles, and by the adoption of context-aware approaches at both the communication and the application levels. In this chapter, we discuss the emerging trends, potential issues, and most promising research directions in the area of intelligent vehicular communication networks, with special attention to the use of different types of data for multi-objective optimizations, including extremely large capacity and reliable information dissemination among automotive nodes.

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Notes

  1. 1.

    Although strictly speaking mmWave frequencies are between 30 and 300 GHz, industry has loosely defined as mmWave bands frequencies above 10 GHz.

  2. 2.

    A preliminary performance comparison between the mm Wave technology and the LTE and the DSRC standards (currently employed for V2I and V2V communications, respectively) has been recently provided in [10] and [11], in relation with future automotive applications’ requirements.

  3. 3.

    The network layer primarily aims at maximizing the throughput while minimizing the packet loss and limiting the overhead. A comprehensive taxonomy of the current routing protocols for vehicular communication systems can be found in [28].

  4. 4.

    The Global Positioning System (GPS) provides an estimate of the current location of a vehicle in an Earth-coordinate frame. Standard GPS is accurate within 10 m, while differential GPS has improved accuracy, with errors limited to about 1 m. Besides spatial information, GPS also offers global time synchronization, with an accuracy of around ± 10 ns.

  5. 5.

    The National Highway Traffic Safety Administration reported that, in 2015 in the US, motor vehicle deaths related to large truck crashes are 11% of the total, which is much higher that the percentage of large trucks among vehicles.

  6. 6.

    The term Machine Learning (ML) generally refers to a wide set of data-driven algorithms that are generic in their definition, but can learn to perform specific tasks after proper training. If the training set is properly chosen, the ML algorithm should be able to generalize its behavior to previously unseen input data sequences, still providing a good estimate of the utility function [V8].

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Padua, Italy

    Marco Giordani, Andrea Zanella & Michele Zorzi

  2. Network Division, TOYOTA InfoTechnology Center, U.S.A., Inc., Mountain View, CA, USA

    Takamasa Higuchi & Onur Altintas

Authors
  1. Marco Giordani
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  2. Andrea Zanella
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  3. Takamasa Higuchi
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  4. Onur Altintas
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  5. Michele Zorzi
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Corresponding author

Correspondence to Marco Giordani .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Karm Veer Arya

  2. Department of Computer Science and Engineering, Indian Institute of information technology (IIIT) Nagpur, Nagpur, Maharashtra, India

    Robin Singh Bhadoria

  3. Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    Narendra S. Chaudhari

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Giordani, M., Zanella, A., Higuchi, T., Altintas, O., Zorzi, M. (2018). Emerging Trends in Vehicular Communication Networks. In: Arya, K., Bhadoria, R., Chaudhari, N. (eds) Emerging Wireless Communication and Network Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-13-0396-8_3

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  • DOI: https://doi.org/10.1007/978-981-13-0396-8_3

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