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Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

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Abstract

Image processing and control technologies have been widely studied and autonomous vehicles have become an active research area. For autonomous driving, it is essential to generate a safe obstacle avoidance path considering the surrounding environment. This paper devised an algorithm based on a real-time output constrained model predictive control for obstacle avoidance path planning in high speed driving situations. The proposed algorithm was compared with the normal model predictive control algorithm by simulation, including operation times to verify robustness for high speed driving situations. We used the ISO 2631-1 comfort level standard to quantify driver comfort fo r both cases.

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Authors and Affiliations

  1. Department of Automotive Convergence, Korea University, 1 Anam-dong, Sungbuk-gu, Seoul, 02841, Korea

    Ji-Chang Kim

  2. Department of Electrical Engineering, Korea University, 1 Anam-dong, Sungbuk-gu, Seoul, 02841, Korea

    Dong-Sung Pae & Myo-Taeg Lim

Authors
  1. Ji-Chang Kim
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  2. Dong-Sung Pae
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  3. Myo-Taeg Lim
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Correspondence to Myo-Taeg Lim.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Changsun Ahn under the direction of Editor Keum-Shik Hong. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2016R1D1A1B01016071).

Ji-Chang Kim received his B.S. degree in Electrical Engineering from Korea University in 2017 and his Master degree in Automotive Convergence from Korea University in 2019. His research interests include model predictive control and vehicle dynamics.

Dong-Sung Pae received his B.S. degree in Electrical Engineering from Korea University, Seoul, Korea, in 2013, where he has been working toward a Ph.D. degree with the School of Electrical Engineering since 2013. His current research interests include computer vision, feature extractor, video stabilization, artificial intelligence, and their applications to intelligence vehicle systems.

Myo-Taeg Lim received his B.S. and M.S. degrees in Electrical Engineering from Korea University, Seoul, Korea, in 1985 and 1987, respectively. He also received his M.S. and Ph.D. degrees in Electrical Engineering from Rutgers University, NJ, USA, in 1990 and 1994, respectively. He was a Senior Research Engineer with the Samsung Advanced Institute of Technology and a Professor in the Department of Control and Instrumentation, National Changwon University, Korea. Since 1996, he has been a Professor in the School of Electrical Engineering at Korea University. His research interests include optimal and robust control, vision based motion control, and autonomous vehicles. He is the author or coauthor of more than 80 journal papers and two books (Optimal Control of Singularly Perturbed Linear Systems and Application: High-Accuracy Techniques, Control Engineering Series, Marcel Dekker, New York, 2001; Optimal Control: Weakly Coupled Systems and Applications, Automation and Control Engineering Series, CRC Press, New York, 2009). Dr. Lim currently serves as an Editor for International Journal of Control, Automation, and Systems. He is a Fellow of the Institute of Control, Robot and Systems, and a member of the IEEE and Korea Institute of Electrical Engineers.

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Kim, JC., Pae, DS. & Lim, MT. Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control. Int. J. Control Autom. Syst. 17, 2850–2861 (2019). https://doi.org/10.1007/s12555-019-9091-y

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  • DOI: https://doi.org/10.1007/s12555-019-9091-y

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