An online evolving method named evolving Finite State Machine (e-FSM) can determine unknown states (situations) and identify transitions. At the moment, it is similar to Markov Chain, but its structure evolves over time. This approach enables controllers to recognize unexpected situations and learn optimal decisions over time. Also, the e-FSM is fully explainable, while Deep Neural Network is not.
Driving intention is recognized by determining and estimating a driving characteristic, and lane-change behaviors are predicted by training a neural-network based predictor
Published in Conference on Biologically Inspired Cognitive Architectures BICA, 2011
Recommended citation: Chen, J., Demski, A., Han, T., Morency, L. P., Pynadath, D. V., Rafidi, N., & Rosenbloom, P. S. (2011, October). Fusing Symbolic and Decision-Theoretic Problem Solving+ Perception in a Graphical Cognitive Architecture. In BICA (pp. 64-72).
Published in IEEE International Conference on Robotics and Biomimetics, 2012
Recommended citation: Han, T., Ranasinghe, N., Barrios, L., & Shen, W. M. (2012, December). An online gait adaptation with superbot in sloped terrains. In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1256-1261). IEEE.
Published in Proceedings of the 12th International Conference on Cognitive Modeling, 2013
Recommended citation: Rosenbloom, P. S., Demski, A., Han, T., & Ustun, V. (2013). Learning via gradient descent in Sigma. In Proceedings of the 12th International Conference on Cognitive Modeling (Vol. 94).
Published in 17th International IEEE Conference on Intelligent Transportation Systems, 2014
Recommended citation: Han, T., Kim, Y., & Kim, K. (2014, October). Lane detection & localization for UGV in urban environment. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 590-596).
Published in In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019
Recommended citation: Han, T., Jing, J., & Özgüner, Ü. (2019, June). Driving Intention Recognition and Lane Change Prediction on the Highway. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 957-962).
Published in arXiv preprint arXiv:1908.10823, 2019
Recommended citation: Han, T., Filev, D., & Ozguner, U. (2019). An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks. arXiv preprint arXiv:1908.10823.
Published in arXiv preprint arXiv:2006.08092, Accepted in IFAC 2020 WC, 2020
Recommended citation: Han, T., Nageshrao, S., Filev, D., & Ozguner, U. (2020). An online evolving framework for advancing reinforcement-learning based automated vehicle control. arXiv preprint arXiv:2006.08092.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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