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Moving Obstacle Avoidance Control of Cars Based on Big Data and Just-In-Time Modeling

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2016

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
In this paper a moving obstacle avoidance control problem for cars is considered and a control method based on big data and just-in-time modeling is developed. Just-in-time modeling is a new kind of data-driven control techniques in the big data age and has been applied in various real systems. The main feature of the proposed method via just-in-time modeling is that a gain of the control input to avoid an encountered moving obstacle can be computed from a data-base which includes a lot of driving data in different situations. Especially, the noteworthy advantage of the new control method is small computation time, and hence real-time control can be achieved. From numerical simulations, it turns out that the car can avoid various moving obstacles by the proposed method.
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  • English

REFERENCES

References: 

[1] N. Navet and F. Simonot-Lion ed., Automotive Embedded Systems
Handbook (Industrial Information Technology), Society of Automotive
Engineers Inc., 2013
[2] R. K. Jurgen, Autonomous Vehicles for Safer Driving, Society of
Automotive Engineers Inc., 2013
[3] Konrad Reif ed., Automotive Mechatronics: Automotive Networking,
Driving Stability Systems, Electronics (Bosch Professional Automotive
Information), Springer Vieweg, 2014
[4] R. Kitchin, The Data Revolution: Big Data, Open Data, Data
Infrastructures and Their Consequences, SAGE Publications Ltd; New.,
2014
[5] N. Marz and J. Warren, Big Data: Principles and Best Practices of
Scalable Real-Time Data Systems, Manning Pubns Co., 2015
[6] R. Buyya, R. N. Calheiros, and A. V. Dastjerdi, Big Data: Principles
and Paradigms, Morgan Kaufmann, 2016
[7] A. Stenman, F. Gustafsson, and L. Ljung, “Just in time models for
dynamical systems,” in Proc. of the 35th IEEE Conf. on Decision and
Control, 1996, pp. 1115-1120.
[8] Q. Zheng and H. Kimura, “Just-in-time modeling for function prediction
and its applications,” Asian Journal of Control, vol. 3, pp. 35-44, 2001.
[9] S. Wakao, D. Hosogoshi, and T. Yamamura, “Fundamental study on the
application of Just-In-Time Modeling to wind power estimation,” in
Proc. of European Wind Energy Conference and Exhibition 2006, 2006,
pp. 412-416.
[10] Z. Ge and Z. Song, “A comparative study of just-in-time-learning based
methods for online soft sensor modeling,” Chemometrics and Intelligent
Laboratory Systems, vol. 104, pp. 306-317, 2010.

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