Abstract
This paper intends to focus on vehicles steering by analyzing the trajectory. The target values we need to measure are the vehicle’s speed and turning angle. These two values are needed to be quantified to certain levels. To create the Hidden Markov Model, HMM learning algorithm and the two values above are used. In HMM, turning left, going straight and turning right are the hidden states and data from the video are used to compute the parameters. HMM can be used to analyze vehicles’ driving and to predicate the probable steering in time. The experimental results show that in the case of getting good vehicle trajectory, it is pretty suitable to use HMM to predicate vehicle behavior.
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References
Li, Z.-M.: Traffic Engineering. China Communications Press, Beijing (2002)
Yi, S.: Video-based Traffic Incident Detection Method Research. Southeast University, Nanjing (2007)
Shunsuke, K., Masao, S.: Classification of Traffic Events based on the Spatio-Temporal MRF Model and the Bayesian Network. In: Proc. of the 9th World Congress on ITS, Chicago (2002)
Shunsuke, K., Katsushi, I., Masao, S.: Event Recognitions from Traffic Images based on Spatio-Temporal Markov Random Field Model. In: Proc. of the 8th World Congress on Intelligent Transport Systems, pp. 1–12 (2001)
Kamijo, S., Matsushita, Y., Ikeuchi, K., Sakauchi, M.: Traffic Monitoring and Accident Detection at Intersections. IEEE Trans. on Intelligent Transportation Systems 1(2), 108–118 (2000)
Saunier, N.: Automated Road Safety Analysis Using Video Data. In: Proc of the 86th Annual Meeting of the Transportation Research Board (2007)
Saunier, N., Sayed, T.: A Feature-based Tracking Algorithm for Vehicles in Intersections. In: Proc. of the 3rd Canadian Conference on Computer and Robot Vision (2006)
Saunier, N., Sayed, T.: Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis. In: Proc. of International Joint Conference on Neural Networks, pp. 4123–4138 (2006)
Bashir, F.I., Khokhar, A.A., Schonfeld, D.: Object Trajectory- Based Activity Classification and Recognition Using Hidden Markov Models. IEEE Trans. on Image Processing 16(7), 1912–1919 (2007)
Wang, H.-Q., Peng, J.-X., Yu, Q.-Z.: A Precise Eye Localization via a Dynamic Probability Distribution HMM Model. Journal of Image and Graphics 11(1), 26–32 (2006)
Feng, C.-J.: Application on Faults Diagnosis of Rotating Machine in Hidden Markov Models. Zhejiang University, Hangzhou (2002)
Shen, Y.-Y., Kang, X., Liu, F.: The Fault Diagnosis of Cars Bogie Based on HMM Method and Its Application. Railway Locomotive & Car 26(4), 24–26 (2006)
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Wu, J., Cui, Zm., Zhao, Pp., Chen, Jm. (2012). Traffic Vehicle Behavior Prediction Using Hidden Markov Models. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_48
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DOI: https://doi.org/10.1007/978-3-642-33478-8_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
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