Vehicle-Following Model Using Virtual Piecewise Spline Tow Bar
Publication: Journal of Transportation Engineering
Volume 142, Issue 11
Abstract
Vehicle following has always been a hotspot of research in the transportation system. It can be used in many applications, such as unmanned ground vehicle (UGV) queue marching, traffic congestion relief, automatic driving systems, etc. Trajectory tracking has been the main consideration in previous studies, but the research about following distance is rare. In this paper, a novel vehicle-following model is proposed for independent vehicle following. In the proposed model, multisensor information is fused to promote the detection accuracy. Moreover, a virtual piecewise spline tow-bar is built between the leader vehicle and the follower vehicle. According to the state of virtual tow-bar, the following speed and heading can be well controlled, to keep a reasonable gap between two vehicles. The proposed vehicle-following model is designed for low speed and minor vehicle spacing case. By using the proposed vehicle-following model, the performance of multple-vehicle automatic cruise can be improved, resulting in a steady traffic flow on an express way. Then the stop-and-go waves of congestion can probably be relieved. Simulation and experiment are conducted to validate the efficiency and robustness of the proposed vehicle-following model.
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Acknowledgments
This work is supported by International Scientific and Technological Cooperation Projects of China (Grant No. 2015DFG12650) and National Nature Science Foundation of China (Grant No. 61573048).
References
Baumberg, A. (2000). “Reliable feature matching across widely separated views.” Proc., Computer Vision and Pattern Recognition (CVPR), 2000, IEEE Conf., Vol. 1, IEEE, New York, 774–781.
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). “Speeded-up robust features (SURF).” Comput. Vision Image Understanding, 110(3), 346–359.
Borodani, P. (2000). “Full automatic control for trucks in a tow-bar system.” Proc., Advanced Vehicle Control (AVEC) 2000, IEEE, New York, 131–138.
Caraffi, C., Vojir, T., Trefny, J., Sochman, J., and Matas, J. (2012). “A system for real-time detection and tracking of vehicles from a single car-mounted camera.” Proc., Intelligent Transportation Systems (ITSC), 2012 15th Int. IEEE Conf., IEEE, New York, 975–982.
Comaniciu, D., Ramesh, V., and Meer, P. (2003). “Kernel-based object tracking.” Pattern Anal. Mach. Intell., IEEE Trans., 25(5), 564–577.
Dalal, N. and Triggs, B. (2005). “Histograms of oriented gradients for human detection.” Proc., IEEE Computer Vision and Pattern Recognition (CVPR), 2005, IEEE Computer Society Conf., Vol. 1, IEEE, New York, 886–893.
Gao, T., and Koller, D. (2011). “Discriminative learning of relaxed hierarchy for large-scale visual recognition.” Proc., Computer Vision (ICCV), 2011 IEEE Int. Conf., IEEE, New York, 2072–2079.
Huang, C. J., et al. (2014). “Application of cellular automata and type-2 fuzzy logic to dynamic vehicle path planning.” Appl. Soft Comput., 19, 333–342.
Jazayeri, A., Cai, H., Zheng, J. Y., and Tuceryan, M. (2011). “Vehicle detection and tracking in car video based on motion model.” Intell. Trans. Syst. IEEE Trans., 12(2), 583–595.
Kalal, Z., Mikolajczyk, K., and Matas, J. (2012). “Tracking-learning-detection.” Pattern Anal. Mach. Intell. IEEE Trans., 34(7), 1409–1422.
Kalogiratou, Z., and Simos, T. E. (2003). “Newton–Cotes formulae for long-time integration.” J. Comput. Appl. Math., 158(1), 75–82.
Lowe, D. G. (2004). “Distinctive image features from scale-invariant keypoints.” Int. J. Comput. Vision, 60(2), 91–110.
Ng, T. C., Guzman, J. I., and Adams, M. D. (2005). “Autonomous vehicle-following systems: A virtual trailer link model.” Proc., Intelligent Robots and Systems, (IROS 2005), IEEE/RSJ Int. Conf., IEEE, New York, 3057–3062.
Nouveliere, L. (2007). “Experimental vehicle longitudinal control using a second order sliding mode technique.” Control Eng. Pract., 15(8), 943–954.
Pan, Z., Li, J. Q., Hu, K. M., and Zhu, H. (2015). “Intelligent vehicle path planning based on improved artificial potential field method.” Appl. Mech. Mater., 742, 349–354.
Pandey, G., McBride, J., Savarese, S., and Eustice, R. (2010). “Extrinsic calibration of a 3D laser scanner and an omnidirectional camera.” Proc., 7th Symp. on Intelligent Autonomous Vehicles (IFAC), Vol. 7, IEEE, New York.
Ravi Kantha, A. R., and Reddy, Y. N. (2005). “Cubic spline for a class of singular two-point boundary value problems.” Appl. Math. Comput., 170(2), 733–740.
Sochman, J., and Matas, J. (2005). “Waldboost-learning for time constrained sequential detection.” Proc., IEEE Computer Vision and Pattern Recognition (CVPR), 2005, IEEE Computer Society Conf., Vol. 2, IEEE, New York, 150–156.
Sun, Z., Bebis, G., and Miller, R. (2006). “On-road vehicle detection: A review.” Pattern Anal. Mach. Intell. IEEE Trans., 28(5), 694–711.
Vale, C., and Calcada, R. (2014). “A dynamic vehicle-track interaction model for predicting the track degradation process.” J. Infrastruct. Syst., 04014016.
Zhang, Q., and Pless, R. (2004). “Extrinsic calibration of a camera and laser range finder (improves camera calibration).” Proc., Intelligent Robots and System (IROS), 2004 IEEE/RSJ Int. Conf., Vol. 3, IEEE, New York, 2301–2306.
Information & Authors
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Published In
Journal of Transportation Engineering
Volume 142 • Issue 11 • November 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Jun 21, 2015
Accepted: Apr 25, 2016
Published online: Jun 22, 2016
Published in print: Nov 1, 2016
Discussion open until: Nov 22, 2016
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