Need of Ambient Intelligence for Next-Generation Connected and Autonomous Vehicles

  • Adnan MahmoodEmail author
  • Bernard Butler
  • Quan Z. Sheng
  • Wei Emma Zhang
  • Brendan Jennings
Part of the Computer Communications and Networks book series (CCN)


The automotive industry is shifting its focus from performance and features to safety, entertainment, and driver comfort. In this regard, driver assistance and autonomous driving technology are gaining more attention. Such technology has the potential to reduce road accidents, traffic congestion, and fuel usage. However, vehicles cannot become fully autonomous, until they are able to sense their context efficiently (context sensing), and to use ambient learning to respond appropriately and within short timescales to the data they have sensed. Context sharing will also become essential, because a single vehicle will not be able to gain a holistic view of its context without cooperation from other nearby vehicles and from the roadside infrastructure. Indeed, there are further advantages when a group of vehicles make intelligent decisions based on a common understanding of their context. This chapter highlights the significance of ambient intelligence for next-generation connected and autonomous vehicles, describes its current state of the art, and also shows how its potential might be achieved. One of the main challenges refers to how to provision and coordinate cloud-based services to meet the needs of real-time (low latency) data-intensive (high data rate) ambient intelligence, particularly for safety-critical vehicular safety applications. It indicates how autonomous or semi-autonomous vehicles are likely to make seamless use of any available wireless networking technologies to improve both coverage and reliability and, where feasible, to cache critical content near the network edge so as to minimize the number of network hops and hence service latencies. Both of these approaches should improve the network quality of service afforded to driving applications.


Ambient intelligence Context sensing Context sharing Heterogeneous vehicular networks Edge computing Software-Defined networks VANETs 


  1. 1.
    He Z, Zhang D, Liang J (2016) Cost-efficient sensory data transmission in heterogeneous software-defined vehicular networks. IEEE Sens J 16(20):7342–7354CrossRefGoogle Scholar
  2. 2.
    Fontes RDR, Campolo C, Rothenberg CE, Molinaro A (2017) From theory to experimental evaluation: resource management in software-defined vehicular networks, vol 5. IEEE Access, pp 1–8Google Scholar
  3. 3.
    Zheng K, Zheng Q, Yang H, Zhao L, Hou L, Chatzimisios P (2015) Reliable and efficient autonomous driving: the need for heterogeneous vehicular networks. IEEE Commun Mag 53(12):72–79CrossRefGoogle Scholar
  4. 4.
    3GPP (2015) 3rd Generation partnership project; technical specification group services and system aspects; study on LTE support for vehicle to everything (V2X) services (3GPP TR 22.885 V14.0.0—Technical Report).
  5. 5.
    Thompson C (2010) New details about the fatal Tesla Autopilot crash reveal the driver’s last minutes. Accessed 06 May 2018
  6. 6.
    Choi J, Gonzalez-Prelcic N, Daniels R, Bhat CR, Heath RW (2016) Millimeter wave vehicular communication to support massive automotive sensing. IEEE Commun Mag 54(12):160–167CrossRefGoogle Scholar
  7. 7.
    Nelson P (2016) Just one autonomous car will use 4000 GB of data/day. Accessed 06 May 2018
  8. 8.
    Zheng K, Zheng Q, Chatzimisios P, Xiang W, Zhou Y (2015) Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions. IEEE Commun Surv Tutor 17(4):2377–2396CrossRefGoogle Scholar
  9. 9.
    Bagloee SA, Tavana M, Asadi M et al (2016) Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J Mod Transp 24(4):284–303CrossRefGoogle Scholar
  10. 10.
    Xu W, Zhou H, Cheng N, Lyu F, Shi W, Chen J, Shen X (2018) Internet of vehicles in big data era. IEEE/CAA J Autom Sin 5(1):19–35CrossRefGoogle Scholar
  11. 11.
    Sun S-H, Hu J-J, Peng Y, Pan X-M, Zhao L, Fang J-Y (2016) Support for vehicle-to-everything services based on LTE. IEEE Wirel Commun 23(6):4–8CrossRefGoogle Scholar
  12. 12.
    Amadeo M, Campolo C, Molinaro A (2016) Information-centric networking for connected vehicles: a survey and future perspectives. IEEE Commun Mag 54(2):98–104CrossRefGoogle Scholar
  13. 13.
    Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656CrossRefGoogle Scholar
  14. 14.
    Lu N, Cheng N, Zhang N, Shen X, Mark JW (2014) Connected vehicles: solutions and challenges. IEEE Internet Thing J 1(4):289–299CrossRefGoogle Scholar
  15. 15.
    He Z, Cao J, Liu X (2016) SDVN: enabling rapid network innovation for heterogeneous vehicular communication. IEEE Netw 30(4):10–15CrossRefGoogle Scholar
  16. 16.
    5G PPP (2015) 5G Vision—5G infrastructure public private partnership: the next generation of communication networks and services. Accessed 06 May 2018
  17. 17.
    Marquez-Barja JM, Ahmadi H, Tornell SM, Calafate CT, Cano JC, Manzoni P, DaSilva LA (2015) Breaking the vehicular wireless communications barriers: vertical handover techniques for heterogeneous networks. IEEE Trans Veh Technol 64(12):5878–5890CrossRefGoogle Scholar
  18. 18.
    Mumtaz S, Jornet JM, Aulin J, Gerstacker WH, Dong X, Ai B (2017) Terahertz communication for vehicular networks. IEEE Trans Veh Technol 66(7):5617–5625CrossRefGoogle Scholar
  19. 19.
    Malandrino F, Chiasserini CF, Kirkpatrick S (2016) The impact of vehicular traffic demand on 5G caching architectures: a data-driven study. Veh Commun 8:13–20Google Scholar
  20. 20.
    Deng DJ, Lien SY, Lin CC, Hung SC, Chen WB (2017) Latency control in software-defined mobile-edge vehicular networking. IEEE Commun Mag 55(8):87–93CrossRefGoogle Scholar
  21. 21.
    Liu J, Wan J, Zeng B, Wang Q, Song H, Qiu M (2017) A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun Mag 55(7):94–100CrossRefGoogle Scholar
  22. 22.
    Modesto FM, Boukerche A (2017) An analysis of caching in information-centric vehicular networks. In: 2017 IEEE international conference on communications (ICC). Paris, pp 1–6Google Scholar
  23. 23.
  24. 24.
    Yaqoob I, Ahmad I, Ahmed E, Gani A, Imran M, Guizani N (2017) Overcoming the key challenges to establishing vehicular communication: Is SDN the answer? IEEE Commun Mag 55(7):128–135CrossRefGoogle Scholar
  25. 25.
    Azizian M, Cherkaoui S, Hafid AS (2017) Vehicle software updates distribution with SDN and cloud computing. IEEE Commun Mag 55(8):74–79CrossRefGoogle Scholar
  26. 26.
    Yao H, Bai C, Zeng D, Liang Q, Fan Y (2015) Migrate or not? Exploring virtual machine migration in roadside cloudlet-based vehicular cloud. Concurr Comput Pract Exp 27(18):5780–5792CrossRefGoogle Scholar
  27. 27.
    Joerer S, Segata M, Bloessl B, Lo Cigno R, Sommer C, Dressler F (2014) A vehicular networking perspective on estimating vehicle collision probability at intersections. IEEE Trans Veh Technol 63(4):1802–1812CrossRefGoogle Scholar
  28. 28.
    Händel P, Ohlsson J, Ohlsson M, Skog I, Nygren E (2014) Smartphone-based measurement systems for road vehicle traffic monitoring and usage-based insurance. IEEE Syst J 8(4):1238–1248CrossRefGoogle Scholar
  29. 29.
    Ghose A, Biswas P, Bhaumik C, Sharma M, Pal A, Jha A (2012) Road condition monitoring and alert application: Using in-vehicle Smartphone as Internet-connected sensor. In: 2012 IEEE international conference on pervasive computing and communications workshops. Lugano, pp 489–491Google Scholar
  30. 30.
    Liang X, Li X, Luan TH, Lu R, Lin X, Shen X (2012) Morality-driven data forwarding with privacy preservation in mobile social networks. IEEE Trans Veh Technol 61(7):3209–3222CrossRefGoogle Scholar
  31. 31.
    Mueck M, Karls I (2018) Networking vehicles to everything (Evolving automotive solutions). De|G Press, BerlinGoogle Scholar
  32. 32.
    Ahmed SH, Bouk SH, Kim D, Rawat DB, Song H (2017) Named data networking for software defined vehicular networks. IEEE Commun Mag 55(8):60–66CrossRefGoogle Scholar
  33. 33.
    Sanaei Z, Abolfazli S, Gani A, Buyya R (2014) Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun Surv Tutor 16(1):369–392CrossRefGoogle Scholar
  34. 34.
    Bojarski M, Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD (2016) End to end learning for self-driving cars. arXiv:1604.07316
  35. 35.
    Cohda Wireless (2018). Accessed 06 May 2018
  36. 36.
    Darwish TSJ, Abu Bakar K (2018) Fog based intelligent transportation big data analytics in the internet of vehicles environment: motivations, architecture, challenges, and critical issues. IEEE Access 6:15679–15701CrossRefGoogle Scholar
  37. 37.
    Hou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans Veh Technol 65(6):3860–3873CrossRefGoogle Scholar
  38. 38.
    Baccarelli E, Naranjo PGV, Scarpiniti M, Shojafar M, Abawajy JH (2017) Fog of everything: energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 5:9882–9910CrossRefGoogle Scholar
  39. 39.
    Vigneri L, Spyropoulos T, Barakat C (2016) Storage on wheels: offloading popular contents through a vehicular cloud. In: IEEE 17th International symposium on a world of wireless, mobile and multimedia networks (WoWMoM). Coimbra, pp 1–9Google Scholar
  40. 40.
    Zhou Z, Yu H, Xu C, Zhang Y, Mumtaz S, Rodriguez J (2018) Dependable content distribution in D2D-based cooperative vehicular networks: a big data-integrated coalition game approach, pp 1–12Google Scholar
  41. 41.
    Cui L, Yu FR, Yan Q (2016) When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw 30(1):58–65CrossRefGoogle Scholar
  42. 42.
    Su D, Ahn S (2017) In-vehicle sensor-assisted platoon formation by utilizing vehicular communications. Int J Distrib Sens Netw 13(7):1–12CrossRefGoogle Scholar
  43. 43.
    Nvidia Drive (2018) Scalable AI platform for autonomous driving—World’s first functionally safe AI self-driving platform. Accessed 06 May 2018
  44. 44.
    Gartner Inc. (2017) Gartner says 8.4 billion connected “things” will be in use in 2017, Up 31 percent from 2016. Accessed 06 May 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adnan Mahmood
    • 1
    • 2
    Email author
  • Bernard Butler
    • 2
  • Quan Z. Sheng
    • 1
  • Wei Emma Zhang
    • 1
  • Brendan Jennings
    • 2
  1. 1.Department of ComputingMacquarie UniversitySydneyAustralia
  2. 2.Telecommunications Software and Systems Group (Science Foundation Ireland—CONNECT)Waterford Institute of TechnologyWaterfordRepublic of Ireland

Personalised recommendations