Pedestrians’ Intention Recognition Method using Hidden Semi-Markov Model: The Case of Crossing the Crosswalk

Authors

  • Junsik Kong Sungkyunkwan University, Department of Industrial Engineering, Suwon, Korea
  • Jaewoong Kang Sungkyunkwan University, Department of Industrial Engineering, Suwon, Korea
  • Jaeung Lee Sungkyunkwan University, Department of Industrial Engineering, Suwon, Korea
  • Mye Sohn Sungkyunkwan University, Department of Industrial Engineering, Suwon, Korea

Keywords:

Human Intention Recognition, Machine Learning, Hidden Semi Markov Model, Elder Pedestrian

Abstract

It is very important to ensure that elder people can perform safe outdoor activities, especially crossing the crosswalk. In this paper, we propose a novel system that can recognize intentions of the elder pedestrians in the vicinity of traffic lights to support the safe crossing. In order to recognize the intention, we applied Hidden Semi-Markov Model (HSMM), which is the most widely adopted method in this field of research. Our system consists of three functions: spatial context identification, HSMM-based learning, and intention recognition. To implement our system, we used GPS data collected from sensors embedded in the elder pedestrians’ smartphone, traffic lights data collected through Open API, and pre-classified activity data for activity learning. In the experimental section, to evaluate the performance of our system, we conducted experiments to find optimum k of k-prototype clustering and to determine the number of hidden states. The key contribution of this paper is to recognize the intentions from the pedestrians’ point of view for the safety of the pedestrians, not the intention of the driver for safe driving of the car.

References

Geman, Oana, et al. "Challenges and trends in Ambient Assisted Living and intelligent tools for disabled and elderly people." Computational Intelligence for Multimedia Understanding (IWCIM), 2015 International Workshop on. IEEE, 2015.

Kupiainen, Tiina, and Tiina Jansson. "Aged People’s Experiences of Gerontechnology Used at Home: A narrative literature review." (2017).

Statistics Korea [KOSTAT], 2015 Statistics on the Aged, 2015, Retrieved at http://kostat.go.kr/

Korea Traffic Accident Analysis System [KoROAD], Traffic accident statistics summary for 2015, Retrieved on July 27, 2015

Asher, Laura, et al. "Most older pedestrians are unable to cross the road in time: a cross-sectional study." Age and ageing 41.5 (2012): 690-694.

Kong J., et al., “Recognition of pedestrians’ intention around traffic lights using Hidden Markov Model”, In International Conference on Internet (ICONI) 2017, 2017, December.

Hashimoto, Yoriyoshi, et al. "Probability estimation for pedestrian crossing intention at signalized crosswalks." Vehicular Electronics and Safety (ICVES), 2015 IEEE International Conference on. IEEE, 2015.

Tran, Duy, et al. "A Hidden Markov Model based driver intention prediction system." Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on. IEEE, 2015.

Berndt, Holger, Jorg Emmert, and Klaus Dietmayer. "Continuous driver intention recognition with hidden markov models." Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on. IEEE, 2008.

He, Lei, Chang-fu Zong, and Chang Wang. "Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model." Journal of Zhejiang University SCIENCE C 13.3 (2012): 208-217.

Völz, Benjamin, et al. "A data-driven approach for pedestrian intention estimation." Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on. IEEE, 2016.

Huang, Xianyi. "Driver lane change intention recognition by using entropy-based fusion techniques and support vector machine learning strategy." (2012).

Köhler, Sebastian, et al. "Early detection of the pedestrian's intention to cross the street." Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, 2012.

Kumar, Puneet, et al. "Learning-based approach for online lane change intention prediction." Intelligent Vehicles Symposium (IV), 2013 IEEE. IEEE, 2013.

Schulz, Andreas Th, and Rainer Stiefelhagen. "Pedestrian intention recognition using latent-dynamic conditional random fields." Intelligent Vehicles Symposium (IV), 2015 IEEE. IEEE, 2015.

Diederichs, Frederik, et al. "Application of a Driver Intention Recognition Algorithm on a Pedestrian Intention Recognition and Collision Avoidance System." UR: BAN Human Factors in Traffic. Springer Vieweg, Wiesbaden, 2018. 267-284.

Schulz, Andreas. "Video-based Pedestrian Intention Recognition and Path Prediction for Advanced Driver Assistance Systems." (2016).

Dominguez-Sanchez, Alex, Miguel Cazorla, and Sergio Orts-Escolano. "Pedestrian movement direction recognition using convolutional neural networks." IEEE Transactions on Intelligent Transportation Systems 18.12 (2017): 3540-3548.

Zhao M., Käthner D., & Jipp M., “Modeling driver intention and behavior at roundabouts”, Interdisziplinärer Workshop Kognitive Systeme 2015 (4), 2015, March.

Salomonson, Ivar, and Karthik Murali Madhavan Rathai. Mixed driver intention estimation and path prediction using vehicle motion and road structure information. Diss. Master thesis, Chalmers University of Technology, Gothenburg, Sweden, 2015.

Uusitalo, Laura. "Advantages and challenges of Bayesian networks in environmental modelling." Ecological modelling 203.3-4 (2007): 312-318.

Krauthausen, Peter, and Uwe D. Hanebeck. "Intention recognition for partial-order plans using dynamic bayesian networks." Information Fusion, 2009. FUSION'09. 12th International Conference on. IEEE, 2009.

Rasouli, Amir, Iuliia Kotseruba, and John K. Tsotsos. "Agreeing to cross: How drivers and pedestrians communicate." Intelligent Vehicles Symposium (IV), 2017 IEEE. IEEE, 2017.

Quintero, Raúl, et al. "Pedestrian intention recognition by means of a Hidden Markov Model and body language." Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on. IEEE, 2017.

Chaturvedi, Anil, Paul E. Green, and J. Douglas Caroll. "K-modes clustering." Journal of Classification 18.1 (2001): 35-55.

Huang, Zhexue. "Extensions to the k-means algorithm for clustering large data sets with categorical values." Data mining and knowledge discovery 2.3 (1998): 283-304.

Heinze, Clint. Modelling Intention Recognition For Intelligent Agent Systems. No. Dsto-Rr-0286. Defence Science And Technology Organisation Salisbury (Australia) Systems Sciences Lab, 2004.

Downloads

Published

2018-10-31

How to Cite

Kong, J., Kang, J., Lee, J., & Sohn, M. (2018). Pedestrians’ Intention Recognition Method using Hidden Semi-Markov Model: The Case of Crossing the Crosswalk. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(4), 95–100. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4791

Issue

Vol. 10 No. 4: October - December 2018

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.