Skip to main content
SpringerLink
Log in

Artificial Intelligence and Internet of Things for Autonomous Vehicles

  • Chapter
  • First Online:
Nonlinear Approaches in Engineering Applications

Abstract

Artificial Intelligence (AI) is a machine intelligence tool providing enormous possibilities for smart industrial revolution. Internet of Things (IoT) is the axiom of industry 4.0 revolution, including a worldwide infrastructure for collecting and processing of the data/information from storage, actuation, sensing, advanced services and communication technologies. The combination of high-speed, resilient, low-latency connectivity, and technologies of AI and IoT will enable the transformation towards fully smart Autonomous Vehicle (AV) that illustrate the complementary between real world and digital knowledge for industry 4.0. The purpose of this articla is to examine how the latest approaches in AI and IoT can assist in the search for the Autonomous Vehicles. It has been shown that human errors are the source of 90% of automotive crashes, and the safest drivers drive ten times better than the average (Wu et al. Accident Analysis and Prevention, 117, 21–31, 2018). The automated vehicle safety is significant, and users are requiring 1000 times smaller acceptable risk level. Some of the incredible benefits of AVs are: (1) increasing vehicle safety, (2) reduction of accidents, (3) reduction of fuel consumption, (4) releasing of driver time and business opportunities, (5) new potential market opportunities, and (6) reduced emissions and dust particles. However, AVs must use large-scale data/information from their sensors and devices.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, K.-F., Sasidharan, L., Thorc, C. P., & Chena, S.-Y. (2018). Crash sequence based risk matrix for motorcycle crashes. Accident Analysis and Prevention, 117, 21–31.

    Article  Google Scholar 

  2. Sherif, A. B. T. (2017). Privacy-preserving ride sharing scheme for autonomous vehicles in big data era. IEEE Internet of Things Journal, 4, 611–618.

    Article  Google Scholar 

  3. Xu, W. (2018). Internet of vehicles in big data era. IEEE/CAA Journal of Automatica Sinica, 5, 19–35.

    Article  Google Scholar 

  4. Morgan Stanley Research. (2018). Car of the future is shared, autonomous, electric. Retrieved from https://www.morganstanley.com/ideas/car-of-future-is-autonomous-electric-shared-mobility/.

  5. Lin, P.-H., Wooders, A., Wang, J. T.-Y., & Yuan, W. M. (2018). Artificial intelligence, the missing piece of online education? IEEE Engineering Management Review, 46, 25–28.

    Article  Google Scholar 

  6. Harvard Business Review. (2018). The business of artificial iintelligence. Retrieved from https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence.

  7. Elsayed, G. F., et al. (2018). Adversarial examples that fool both computer vision and time-limited humans. Cornell University, arXiv:1802.08195v3.

    Google Scholar 

  8. McCorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. Natick: A.K. Peters Ltd.

    Google Scholar 

  9. Sutton, R. S., & Barto, A. G. (2014). Reinforcement learning: An introduction. Cambridge: The MIT Press.

    MATH  Google Scholar 

  10. Szepesvári, C. (2009). Algorithms for reinforcement learning. San Rafael: Morgan & Claypool Publishers.

    MATH  Google Scholar 

  11. Kusy, M., & Zajdel, R. (2015). Application of reinforcement learning algorithms for the adaptive computation of the smoothing parameter for probabilistic neural network. IEEE Transactions on Neural Networks and Learning Systems, 26, 2163–2175.

    Article  MathSciNet  Google Scholar 

  12. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.

    MATH  Google Scholar 

  13. Francois-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3–4), 219–354.

    Article  Google Scholar 

  14. Li, L., Lv, Y., & Wang, F.-Y. (2016). Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica, 3, 247–254.

    Article  MathSciNet  Google Scholar 

  15. Lanctot, R. (2017). Accelerating the future: The economic impact of the emerging passenger economy. Retrieved from https://newsroom.intel.com/newsroom/wp-content/uploads/sites/11/2017/05/passenger-economy.pdf

  16. Saiai Company. (2018). Automated & unmanned vehicles. Retrieved from https://www.sae.org/automated-unmanned-vehicles/.

  17. Lavasani, M., Jin, X., & Du, Y. (2016). Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.3141/2597-09.

    Article  Google Scholar 

  18. Marzbani, H., Khayyam, H., To, C. N., Voquoc, D., & Jazar, R. N. (2019). Autonomous vehicles, autodriver algorithm, and vehicle dynamics. IEEE Transactions on Vehicular Technology, 68(4), 3201–3211.

    Article  Google Scholar 

  19. To, C., Quốc, D., Simic, M., Khayyam, H., & Jazar, R. (2018). Autodriver autonomous vehicles control strategy. Procedia Computer Science, 126, 870–877.

    Article  Google Scholar 

  20. Jazar, R. N. (2018). Vehicle dynamics: Theory and application. New York: Springer.

    Google Scholar 

  21. Jazar, R. N. (2019). Advanced vehicle dynamics. New York: Springer.

    Book  Google Scholar 

  22. Rubaiyat, A. H. M., Fallah, Y., Li, X., Bansal, G., & Infotechnology, T. (2018). Multi-sensor data fusion for vehicle detection in autonomous vehicle applications. Electronic Imaging, Autonomous Vehicles and Machines, 6, 257–267.

    Google Scholar 

  23. Kocić, J., Jovičić, N., & Drndarević, V. (2018). Sensors and sensor fusion in autonomous vehicles. In 26th Telecommunications forum TELFOR.

    Google Scholar 

  24. Loebis, D., Sutton, R., & Chudley, J. (2002). Review of multisensor data fusion techniques and their application to autonomous underwater vehicle navigation. Journal of Marine Engineering and Technology, 1, 3–14.

    Article  Google Scholar 

  25. Viegas, P. B., Oliveira, P., & Silvestre, C. (2018). Discrete-time distributed Kalman filter design for formations of autonomous vehicles. Control Engineering Practice, 75, 55–68.

    Article  Google Scholar 

  26. Hardt, P. E., Duda, R. O., & Stork, D. G. (2001). Pattern classification. New York: Wiley.

    MATH  Google Scholar 

  27. Kong, Y., & Guo, S. (2006). Urban traffic controller using fuzzy neural network and multisensors data fusion. International Conference on Information Acquisition, 2006, 404–409.

    Google Scholar 

  28. Starzacher, A. (2010). Multi-sensor real-time data fusion on embedded computing platforms. Klagenfurt: Alpen-Adria-Universitat Klagenfurt Fakultat fur Technische Wissenschaften.

    Google Scholar 

  29. Stanford University. (2016). Artificial intelligence and life in 2030. One hundred year study on artificial intelligence (AI100). Retrieved from https://ai100.stanford.edu.

  30. Gadam, S. (2018). Artificial intelligence and autonomous vehicles. Retrieved from https://medium.com/datadriveninvestor/artificial-intelligence-and-autonomous-vehicles-ae877feb6cd2

  31. MCCA Global TEC Forum. (2018). Autonomous vehicles: Navigating the legal and regulatory issues of a driverless world. Retrieved from https://www.mcca.com/wp-content/uploads/2018/04/Autonomous-Vehicles.pdf.

  32. IIoT World. (2018). Five challenges in designing a fully autonomous system for driver less cars. Retrieved from https://iiot-world.com/artificial-intelligence/five-challenges-in-designing-a-fully-autonomous-system-for-driverless-cars/

  33. Crawford, B., Khayyam, H., Milani, A. S., & Jazar, R. (2019). Big data modelling approaches for engineering applications. In R. N. Jazar (Ed.), Nonlinear approaches in engineering applications. New York: Springer.

    Google Scholar 

  34. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645–1660.

    Article  Google Scholar 

  35. Javadi, B., Calheiros, R. N., Matawie, K. M., Ginige, A., & Cook, A. (2018). Smart nutrition monitoring system using heterogeneous internet of things platform. In G. Fortino, A. B. M. S. Ali, M. Pathan, A. Guerrieri, & G. Di Fatta (Eds.), Internet and distributed computing systems (pp. 63–74). Fiji: Springer.

    Chapter  Google Scholar 

  36. Vermesan, O., et al. (2018). Automated driving progressed by internet of things. In: European Union’s Horizon 2020 Research and Innovation Programme (2014-2020), SINTEF.

    Google Scholar 

  37. Mehdipour, F., Javadi, B., & Mahanti, A. (2016). FOG-engine: Towards Big data analytics in the fog. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Internarional Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 640–646.

    Google Scholar 

  38. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50, 30–39.

    Article  Google Scholar 

  39. Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017). Survey on fog computing: architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, 98, 27–42.

    Article  Google Scholar 

  40. Mehdipour, F., Javadi, B., Mahanti, A., & Ramirez-Prado, G. (2019). Fog computing realization for big data analytics. In Fog and edge computing: Principles and paradigms (pp. 259–290). New York: Wiley.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, RMIT University, Melbourne, VIC, Australia

    Hamid Khayyam & Mahdi Jalili

  2. School of Computing, Engineering, and Mathematics, Western Sydney University, Sydney, NSW, Australia

    Bahman Javadi

  3. Xiamen University of Technology, Xiamen, China

    Reza N. Jazar

  4. School of Engineering, RMIT University, Bundoora, VIC, Australia

    Reza N. Jazar

Authors
  1. Hamid Khayyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bahman Javadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdi Jalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reza N. Jazar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Khayyam .

Editor information

Editors and Affiliations

  1. Xiamen University of Technology, Xiamen, China, School of Engineering, RMIT University, Bundoora, VIC, Australia

    Reza N. Jazar

  2. Xiamen University of Technology, Xiamen, China, University of Regina, Industrial Systems Engineering, REGINA, SK, Canada

    Liming Dai

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Khayyam, H., Javadi, B., Jalili, M., Jazar, R.N. (2020). Artificial Intelligence and Internet of Things for Autonomous Vehicles. In: Jazar, R., Dai, L. (eds) Nonlinear Approaches in Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-18963-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18963-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18962-4

  • Online ISBN: 978-3-030-18963-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation