Obstacle Avoidance Path Planning based on Output Constrained Model Predictive Control

Ji Chang Kim, Dong Sung Pae, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2850-2861
Number of pages12
JournalInternational Journal of Control, Automation and Systems
Issue number11
Publication statusPublished - 2019 Nov 1

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2019, ICROS, KIEE and Springer.


  • Comfort level
  • model predictive control
  • obstacle avoidance
  • path planning
  • vehicle dynamics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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