Skip to main content

Endowing Intelligent Vehicles with the Ability to Learn User’s Habits and Preferences with Machine Learning Methods

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Abstract

A private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants’ routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle’s users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior.

*The work received financial support from European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) and national funds (Project “Easy Ride: Experience is everything”, ref POCI-01-0247-FEDER-039334), and R &D Units Project Scope: UIDB/00319/2020 and UIDB/00013/2020.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Andropov, S., Guirik, A., Budko, M., Budko, M.: Network anomaly detection using artificial neural networks. In: 2017 20th Conference of Open Innovations Association (FRUCT), pp. 26–31. IEEE (2017)

    Google Scholar 

  2. Belkin, M., Hsu, D., Ma, S., Mandal, S.: Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proc. National Acad. Sci. 116(32), 15849–15854 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  3. Eagle, N., Pentland, A.S.: Eigenbehaviors: identifying structure in routine. Behav. Ecol. Sociobiol. 63(7), 1057–1066 (2009)

    Article  Google Scholar 

  4. Fernandes, C., Ferreira, F., Erlhagen, W., Monteiro, S., Bicho, E.: A deep learning approach for intelligent cockpits: learning drivers routines. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 173–183. Springer (2020). https://doi.org/10.1007/978-3-030-62365-4_17

  5. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  6. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)

    Article  Google Scholar 

  7. Guimarães, P., Ferreira, F., Silva, A.C., Erlhagen, W., Monteiro, S., Bicho, E.: A data recording mobile application to create datasets of vehicle users’ routines. In: 2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 167–172. IEEE (2022)

    Google Scholar 

  8. Guliyev, N.J., Ismailov, V.E.: On the approximation by single hidden layer feedforward neural networks with fixed weights. Neural Netw. 98, 296–304 (2018)

    Article  MATH  Google Scholar 

  9. Ibrahim, R., Shafiq, M.O.: On predicting taxi movements modes in Porto city using classification and periodic pattern mining. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1197–1204. IEEE (2019)

    Google Scholar 

  10. Jia, R., Khadka, A., Kim, I.: Traffic crash analysis with point-of-interest spatial clustering. Accid. Anal. Prev. 121, 223–230 (2018)

    Article  Google Scholar 

  11. Park, S.H., Kim, B., Kang, C.M., Chung, C.C., Choi, J.W.: Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1672–1678. IEEE (2018)

    Google Scholar 

  12. Roth, C., Kang, S.M., Batty, M., Barthélemy, M.: Structure of urban movements: polycentric activity and entangled hierarchical flows. PloS One 6(1), e15923 (2011)

    Article  Google Scholar 

  13. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  14. Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 1–21 (2017)

    Article  MathSciNet  Google Scholar 

  15. Simmons, R., Browning, B., Zhang, Y., Sadekar, V.: Learning to predict driver route and destination intent. In: 2006 IEEE Intelligent Transportation Systems Conference, pp. 127–132. IEEE (2006)

    Google Scholar 

  16. Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wojtak, W., et al.: Towards endowing intelligent cars with the ability to learn the routines of multiple drivers: a dynamic neural field model. In: International Conference on Computational Science and Its Applications, pp. 337–349. Springer (2021). https://doi.org/10.1007/978-3-030-86973-1_24

  18. Yang, S.X., Meng, M.: An efficient neural network approach to dynamic robot motion planning. Neural Netw. 13(2), 143–148 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Estela Bicho .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barbosa, P. et al. (2022). Endowing Intelligent Vehicles with the Ability to Learn User’s Habits and Preferences with Machine Learning Methods. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21753-1_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics