Driver’s Intention Estimation Based on Bayesian Networks for a Highly-Safe Intelligent Vehicle

Bo Sun; Michitaka Kameyama

doi:10.20965/jrm.2012.p0219

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JRM Vol.24 No.1 pp. 219-225

doi: 10.20965/jrm.2012.p0219

(2012)

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Driver’s Intention Estimation Based on Bayesian Networks for a Highly-Safe Intelligent Vehicle

Bo Sun and Michitaka Kameyama

Graduate School of Information Sciences, Tohoku University, Aoba-yama 6-6-05, Sendai 980-8579, Japan

Received:

May 9, 2011

Accepted:

October 6, 2011

Published:

February 20, 2012

Keywords:

driver’s intention estimation, Bayesian network, trajectory estimation, intelligent vehicle

Abstract

Highly safe intelligent vehicles can significantly reduce vehicle accidents by warning drivers of dangerous situations. Trajectory estimation of target vehicles is expected to be used in highly safe intelligent vehicles. Trajectory estimation requires that we estimate driver intent not detectable by sensors. The Bayesian Network (BN) building we propose for trajectory estimation related to driver intent defines driver intent hierarchically to simplify the BN as much as possible. Causal driver-intent relationships are discussed reflecting real-world motion. This raises the quality of driver-intent estimation and increasing inference performance. Experimental learning based on 2D image processing is presented to acquire probabilistic BN parameters.

Cite this article as:

B. Sun and M. Kameyama, “Driver’s Intention Estimation Based on Bayesian Networks for a Highly-Safe Intelligent Vehicle,” J. Robot. Mechatron., Vol.24 No.1, pp. 219-225, 2012.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. Hariyama and M. Kameyama, “A Collision Detection Processor for Intelligent Vehicles,” IEICE Trans. on Electronics, Vol.E76-C, No.12, pp. 1804-1811, 1993.

[2] [2] S. Kato, N. Hashimoto, T. Ogitsu, and S. Tsugawa, “Driver Assistance Systems with Communication to Traffic Lights – Configuration of Assistance Systems by Receiving and Transmission and Field Experiments –,” J. of Robotics and Mechatronics, Vol.22, No.6 pp. 737-744, 2010.

[3] [3] H. Asano, T. Mizuno, and H. Ide, “Evaluation of Driver’s Temporary Arousal Level by Changes of Nasal Skin Temperature – Effect of Basic Arousal Level,” J. of Robotics and Mechatronics, Vol.20, No.6, pp. 880-886, 2008.

[4] [4] H. Takahashi and K. Kuroda, “Study on Intelligent Vehicle Control Considering Driver Perception of Driving Environment,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.3, No.1, pp. 42-49, 1999.

[5] [5] R. Bishop, “A Survey of Intelligent Vehicle Applications Worldwide,” Proc. of the IEEE Intelligent Vehicles Symposium, pp. 25-30, 2000.

[6] [6] N. Van Dan and M. Kameyama, “Bayesian-Networks-Based Motion Estimation for a Highly-Safe Intelligent Vehicle,” SICE-ICASE Int. Joint Conf., pp. 6023-6026, 2006.

[7] [7] E. Castillo, J. Gutierrez, and A. Hadi, “Expert Systems and Probabilistic Network Models,” Springer, 1997.

[8] [8] F. Jensen, “Bayesian Networks and Decision Graphs,” Springer, 2001.

[9] [9] N. Friedman, M. Linial, I. Nachman, and D. Pe’er, “Using Bayesian Networks to Analyze Expression Data,” J. of Computational Biology, Vol.7, No.3/4, pp. 601-620, 2000.

[10] [10] M. Mayo and A. Mitrovic, “Optimising ITS Behaviour with Bayesian Networks and Decision Theory,” Int. J. of Artificial Intelligence in Education, Vol.12, pp. 124-153, 2001.

[11] [11] D. Corney, “Designing Food with Bayesian Belief Networks,” Evolutionary Design and Manufacture ACDM2000, pp. 83-94, 2000.

[12] [12] M. Neil, N. Fenton, and L. Nielson, “Building Large-Scale Bayesian Networks,” The Knowledge Engineering Review Archive, Vol.15, No.3, 2000.

[13] [13] K. Wojtek Przytula and D. Thompson, “Construction of Bayesian Networks for Diagnostics,” Aerospace Conf. Proc., 2000 IEEE, Vol.5, pp. 193-200, 2000.

[14] [14] B. Chen and Y. Lei, “Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information,” Fourth Int. Conf. on Computer and Information Technology, pp. 137-142, 2004.

[15] [15] Y. Motomura, “BAYONET: Bayesian Network on Neural Network,” Foundations of Real- World Intelligence, pp. 28-37, CSLI publications, Stanford, California, 2001.