Path planning based on obstacle-dependent gaussian model predictive control for autonomous driving

Dong Sung Pae, Geon Hee Kim, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Path planning research plays a vital role in terms of safety and comfort in autonomous driving systems. This paper focuses on safe driving and comfort riding through path planning in autonomous driving applications and proposes autonomous driving path planning through an optimal controller integrating obstacle-dependent Gaussian (ODG) and model prediction control (MPC). The ODG algorithm integrates the information from the sensors and calculates the risk factors in the driving environment. The MPC function finds vehicle control signals close to the objective function under limited conditions, such as the structural shape of the vehicle and road driving conditions. The proposed method provides safe control and minimizes vehicle shaking due to the tendency to respond to avoid obstacles quickly. We conducted an experiment using mobile robots, similar to an actual vehicle, to verify the proposed algorithm performance. The experimental results show that the average safety metric is 72.34%, a higher ISO-2631 comport score than others, while the average processing time is approximately 14.2 ms/frame.

Original languageEnglish
Article number3703
JournalApplied Sciences (Switzerland)
Volume11
Issue number8
DOIs
Publication statusPublished - 2021

Keywords

  • Comfort level
  • Model predictive control
  • Obstacle avoidance
  • Path planning
  • Vehicle dynamics

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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