Decision-Making Based on Reinforcement Learning and Model Predictive Control Considering Space Generation for Highway On-Ramp Merging

Hikaru Kimura, Masaki Takahashi, Kazuhiro Nishiwaki, Masahiro Iezawa

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Reducing traffic accidents pertaining to autonomous vehicles has garnered attention. Merging on a highway is one of the most challenging problems that must be addressed for the realization of autonomous vehicles. It is difficult because an agent must decide where to merge in a complex and ever-changing environment. Merging with congested highway traffic involves significant interaction with vehicles in the main lane. If there is no space for the autonomous vehicle to merge, it needs to work on vehicles in the main lane to create space and subsequently decide to merge or not. Reinforcement learning (RL) is a promising method for solving decision-making problems. However, it is difficult to guarantee the safety of the controller obtained using RL. Therefore, we propose a combined method in which decision-making is performed by RL and vehicle control by model predictive control (MPC) to ensure safety. The performance of the proposed system is tested by simulations. The proposed system made appropriate decisions according to the situation, and by controlling the vehicle in consideration of collision avoidance constraints, it showed a high merge success rate evenin a crowded situation.

Original languageEnglish
Pages (from-to)241-246
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number27
DOIs
Publication statusPublished - 2022 Sept 1
Event9th IFAC Symposium on Mechatronic Systems, MECHATRONICS 2022 - Los Angeles, United States
Duration: 2022 Sept 62022 Sept 9

Keywords

  • Advanced driver assistance system
  • Autonomous Vehicles
  • Decision making
  • Merging
  • Model predictive control
  • Reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering

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