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Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

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ABSTRACT With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).

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Accepted albo Published Version
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Copyright (2020 Taylor & Francis Group, LLC)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
CYBERNETICS AND SYSTEMS nr 51, strony 103 - 114,
ISSN: 0196-9722
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Zhang H., Li F., Wang J., Zhou Y., Sanin C., Szczerbicki E.: Experience-Based Cognition for Driving Behavioral Fingerprint Extraction// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.103-114
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1080/01969722.2019.1705547
Bibliografia: test
  1. Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 46 (3): 175-185. doi:10.1080/00031305.1992.10475879 otwiera się w nowej karcie
  2. Berndt, H., Emmert, J., & Dietmayer, K. (2008). Continuous Driver Intention Recognition with Hidden Markov Models. International IEEE Conference on Intelligent Transportation Systems (pp.1189-1194). IEEE. otwiera się w nowej karcie
  3. Carvalho, E., Ferreira, B. V., Ferreira, J., Souza, C. D., Carvalho, H. V., & Suhara, Y., et al. (2017). Exploiting the use of recurrent neural networks for driver behavior profiling. International Joint Conference on Neural Networks (pp.3016-3021). IEEE. otwiera się w nowej karcie
  4. Donges, E. (1978). A two-level model of driver steering behavior. Human Factors the Journal of the Human Factors & Ergonomics Society, 20(6), 691-707. otwiera się w nowej karcie
  5. Hess, R. A., & Modjtahedzadeh, A. (1990). A control theoretic model of driver steering behavior. Control Systems Magazine IEEE, 10(5), 3-8. otwiera się w nowej karcie
  6. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. otwiera się w nowej karcie
  7. Hu, J., Xu, L., He, X., & Meng, W. (2017). Abnormal driving detection based on normalized driving behavior. IEEE Transactions on Vehicular Technology, 66(8), 6645-6652. otwiera się w nowej karcie
  8. Jain, A., Koppula, H. S., Soh, S., Raghavan, B., Singh, A., & Saxena, A. (2016). Brain4cars: car that knows before you do via sensory-fusion deep learning architecture. arXiv preprint arXiv:1601.00740 otwiera się w nowej karcie
  9. Kuge, N., Yamamura, T., Shimoyama, O., & Liu, A. (2000). A Driver Behavior Recognition Method Based on a Driver Model Framework. Proceedings of the Society of Automotive Engineers World Congress. otwiera się w nowej karcie
  10. Lecun Y, Bengio Y, Hinton G.(2015). Deep learning. Nature, 2015, 521(7553):436. otwiera się w nowej karcie
  11. Liu, A. (2008). Modeling and prediction of human driver behavior. in Proc. 9th Int. Conf. Human-Comput. Interaction.
  12. Macadam, C. C. (2007). Application of an optimal preview control for simulation of closed-loop automobile driving. IEEE Transactions on Systems Man & Cybernetics, 11(6), 393-399. otwiera się w nowej karcie
  13. McRuer D.. (1980). Paper: human dynamics in man-machine systems. Automatica, 16(3), 237-253. otwiera się w nowej karcie
  14. Meyer-Delius, D., Plagemann, C., & Burgard, W. (2009). Probabilistic situation recognition for vehicular traffic scenarios. IEEE International Conference on Robotics and Automation (pp.459-464). IEEE. otwiera się w nowej karcie
  15. Morton, J., Wheeler, T. A., & Kochenderfer, M. J. (2017). Analysis of recurrent neural networks for probabilistic modeling of driver behavior. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1289-1298. otwiera się w nowej karcie
  16. Olabiyi, O., Martinson, E., Chintalapudi, V., & Guo, R. (2017). Driver action prediction using deep (bidirectional) recurrent neural network.
  17. Oliver, N., & Pentland, A. P. (2000). Graphical models for driver behavior recognition in a SmartCar. Intelligent Vehicles Symposium, 2000. IV 2000. Proceedings of the IEEE (pp.7-12). IEEE. otwiera się w nowej karcie
  18. Sak, H., Senior, A., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Computer Science, 338-342.
  19. Saleh, K., Hossny, M., & Nahavandi, S. (2018). Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks. IEEE, International Conference on Intelligent Transportation Systems (pp.1-6). IEEE. otwiera się w nowej karcie
  20. Sanin C, Szczerbicki E.(2006) Using Set of Experience in the Process of Transforming Information into Knowledge. International Journal of Enterprise Information Systems, 2006, 2(2):45-62. otwiera się w nowej karcie
  21. Sanin, C., Zhang, H., Shafiq, I., Waris, M. M., Oliveira, C. S. D., & Szczerbicki, E. (2018). Experience based knowledge representation for internet of things and cyber physical systems with case studies. Future Generation Computer Systems. otwiera się w nowej karcie
  22. Schnelle, S., Wang, J., Su, H. J., & Jagacinski, R. (2016). A personalizable driver steering model capable of predicting driver behaviors in vehicle collision avoidance maneuvers. IEEE Transactions on Human-Machine Systems, PP(99), 1-11. otwiera się w nowej karcie
  23. Wang, W., Xi, J., & Zhao, D. (2018). Learning and inferring a driver's braking action in car-following scenarios. IEEE Transactions on Vehicular Technology, PP(99), 1-1. otwiera się w nowej karcie
  24. Zeng, X., & Wang, J. (2017). A stochastic driver pedal behavior model incorporating road information. IEEE Transactions on Human-Machine Systems, 47(5), 614-624. otwiera się w nowej karcie
  25. Zhang, H., Li, F., Wang, J., Wang, Z., Sanin, C., & Szczerbicki, E. (2017a). otwiera się w nowej karcie
  26. Experience-oriented intelligence for internet of things. Journal of Cybernetics, 48(3), 162-181. otwiera się w nowej karcie
  27. Zhang H, Sanin C, & Szczerbicki E. (2017b). Towards Neural Knowledge DNA. Journal of Intelligent & Fuzzy Systems, 2017, 32(2):1575-1584. otwiera się w nowej karcie
  28. Zhang, H., Sanin, C., & Szczerbicki, E. (2010). Gaining knowledge through experience: developing decisional dna applications in robotics. Cybernetics & Systems, 41(8), 628-637. otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 118 razy

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