Abstract
In this paper, we present the functionalities of an intelligent connected vehicle. It is equipped with various sensors and connected objects that enable communication between the driver and its environment. This system provides assistance towards safe and green driving. The driving assistance may be directed towards the driver (semi-autonomous vehicle) or completely towards the vehicle (self-driving, autonomous vehicle). The assistance is based on the driving context which is the fusion of parameters representing the context of the driver, the vehicle and the environment. This cyber-physical vehicle has three main components: the embedded system, the networking and real-time system and the intelligent system. The architecture for data transfer within the connected vehicle is implemented through publish-subscribed infrastructure in which services are transferred and controlled in an orderly manner. These functionalities are tested both in the laboratory and on the road with satisfactory results. This is the fruit of labor of a consortium composed of five industrial and two academic partners.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
PSA Peugeot-Citroën: https://www.groupe-psa.com/en/.
- 2.
Continental France: https://www.continental-corporation.com/fr-fr.
- 3.
References
Rajkumar, R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: Design Automation Conference, Anaheim, CA, USA (2010)
Wang, S.: Develop Vehicle Control Systems as CPS for Next-Generation Automobiles, March 2015
Estl, H.: Paving the way to self-driving cars with advanced driver assistance systems, August 2015
Hina, M.D., Thierry, C., Soukane, A., Ramdane-Cherif, A.: Cognition of driving context for driving assistance. In: Presented at the ICAIA 2018: 20th International Conference on Artificial Intelligence and Applications, Kuala Lumpur, Malaysia (2018)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013)
Hina, M.D., Guan, H., Deng, N., Ramdane-Cherif, A.: CASA: safe and green driving assistance system for real-time driving events. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 15, pp. 987–1002. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56994-9_67
Hina, M.D., Guan, H., Ramdane-Cherif, A., Deng, N.: Secured data processing, notification and transmission in a human-vehicle interaction system. In: 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016, Rio de Janeiro, Brazil (2016)
Sentinel, T.M.: Phantom Auto’ will tour city. In: Google News Archive, ed. (1926)
Carnegine Mellon University - The Robotics Institute, NavLab: The Carnegie Mellon University Navigation Laboratory. www.cs.cmu.edu/afs/cs/project/alv/www. Accessed Feb 2018
Kanade, T.: Autonomous land vehicle project at CMU. In: Presented at the 14th ACM Annual Conference on Computer Science (1986)
University of Parma: VisLab, Italy - Public Road Urban Driverless-Car Test 2013 - World premiere of BRAiVE, ed. (2013)
Parkinson, S., Ward, P., Wilson, K., Miller, J.: Cyber threats facing autonomous and connected vehicles: future challenges. IEEE Trans. Intell. Transp. Syst. 18, 2898–2915 (2017)
Geng, H.: Connected vehicle. In: Internet of Things and Data Analytics Handbook, ed. Wiley Telecom (2017)
RAC Australia: Autonomous vehicle survey (2016). https://rac.com.au/-/media/files/rac-website/pdfs/about-rac/publications/reports/2016/autonomous-vehicles-survey.pdf
Ho, J.Y., Koh, W.Y., Veeravalli, B., Wong, J.W., Guo, H.: Secure sensing inputs for autonomous vehicles. In: Presented at the TENCON 2017, Penang, Malaysia (2017)
Foley & Lardner LLP: 2017 Connected Cars & Autonomous Vehicles Survey (2017)
Louridas, P., Ebert, C.: Machine learning. IEEE Softw. 33, 110–115 (2016)
Tchankue, P., Wesson, J., Vogts, D.: Using machine learning to predict driving context whilst driving. In: Presented at the SAICSIT 2013, South African Institute for Computer Scientists and Information Technologists, East London, South Africa (2013)
Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2011)
Li, L., Wen, D., Zheng, N.-N., Shen, L.-C.: Cognitive cars: a new frontier for ADAS research. IEEE Trans. Intell. Transp. Syst. 13, 395–407 (2012)
Ithape, A.A.: Artificial intelligence and machine learning in ADAS. In: Presented at the Vector India Conference, Pune, India (2017)
PSA Group: Car Easy Apps: Co-designing the connected car of the future (2016). https://www.groupe-psa.com/en/newsroom/automotive-innovation/car-easy-apps/
PSA Group: Car Easy Apps: PSA Peugeot Citroën’s Application programming interface (2014). https://www.youtube.com/watch?v=3cTsNeKZDTU
Oracle: Architectural Strategies for Cloud Computing. Oracle White Paper in Enterprise Architecture, August 2009
Yousif, M.: The state of the cloud. IEEE Cloud Comput. 4, 4–5 (2017)
Microsoft: Publish/Subscribe (2018). https://msdn.microsoft.com/en-us/library/ff649664.aspx
w3schools.com: JSON – Introduction (2018). https://www.w3schools.com/js/js_json_intro.asp
Flanders, J.: Service station - more on REST. MSDN Mag. 27(7) (2009). http://msdn.microsoft.com/en-us/magazine/dd942839.aspx
W3C: Latest SOAP versions (2018). https://www.w3.org/TR/soap/
Dumas, B., Lalanne, D., Oviatt, S.: Multimodal interfaces: a survey of principles, models and frameworks. In: Lalanne, D., Kohlas, J. (eds.) Human Machine Interaction. LNCS, vol. 5440, pp. 3–26. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00437-7_1
Oviatt, S.L.: Multimodal interfaces. In: Jacko, A.S.J. (ed.) The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, pp. 286–304, 2nd edn. CRC Press (2008)
Peschl, M.F., Stary, C.: The role of cognitive modeling for user interface design representations. Mind Mach. 8, 203–236 (1998)
Kelly III., J.E.: Computing, cognition and the future of knowing: how humans and machines are forging a new age of understanding (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Hina, M.D., Dourlens, S., Soukane, A., Ramdane-Cherif, A. (2019). Signal Processing, Control and Coordination in an Intelligent Connected Vehicle. In: Zitouni, R., Agueh, M. (eds) Emerging Technologies for Developing Countries. AFRICATEK 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-030-05198-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-05198-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05197-6
Online ISBN: 978-3-030-05198-3
eBook Packages: Computer ScienceComputer Science (R0)