DOI QR코드

DOI QR Code

Automated Driving Lane Change Algorithm Based on Robust Model Predictive Control for Merge Situations on Highway Intersections

고속도로 합류점 주행을 위한 강건 모델 예측 기법 기반 자율주행 차선 변경 알고리즘 개발

  • Chae, Heongseok (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.) ;
  • Jeong, Yonghwan (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.) ;
  • Min, Kyongchan (Korea Automobile Testing & Research Institute, Korea Transportation Safety Authority) ;
  • Lee, Myungsu (Korea Automobile Testing & Research Institute, Korea Transportation Safety Authority) ;
  • Yi, Kyongsu (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.)
  • 채흥석 (서울대학교 기계항공공학부) ;
  • 정용환 (서울대학교 기계항공공학부) ;
  • 민경찬 (교통안전공단 자동차안전연구원) ;
  • 이명수 (교통안전공단 자동차안전연구원) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2016.07.28
  • Accepted : 2017.03.15
  • Published : 2017.07.01

Abstract

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.

Keywords

Highway Automated Driving;Merge Situation on Highway;Automated Driving Control Algorithm;Merge Mode Decision;Safe Driving Envelope Decision;Model Predictive Control

Acknowledgement

Supported by : 국토교통과학기술진흥원

References

  1. Jeong, Y., 2015, "An Evaluation Scenario of Safety Performance for Extraordinary Service Permission of Autonomous Vehicle," KASA Spring Conference Proceedings.
  2. Bishop, R., 2000, "A Survey of Intelligent Vehicle Applications Worldwide," IEEE Intelligent Vehicles Symposium.
  3. Kim, B., Yi, K., Jeong, C. and Kim, J., 2013, "Probabilistic States Prediction Algorithm using Multisensor Fusion and Application to Smart Cruise Control Systems," IEEE Intelligent Vehicles Symposium.
  4. Seo, J. and Yi, K., 2015, "Robust Mode Predictive Control for Lane Change of Automated Driving Vehicles," SAE Technical Paper, No. 2015-01-0317.
  5. Mayne, D., Seron, M. and Rakovic, S., 2005, "Robust Model Predictive Control of Constrained Linear Systems with Bounded Disturbances," Automatica, 41:219-224. https://doi.org/10.1016/j.automatica.2004.08.019
  6. Li, S., Li, K., Rajamani, R. and Wang, J., 2011, "Model Predictive Multi-Objective Vehicluar Adaptive Cruise Control," IEEE Transactions on Control System Technology, 19(3).
  7. Rajamani, R., 2006, Vehicle Dynamics and Control, New York, Springer.
  8. Moon, S. and Yi, K., 2009, "Design, Tuning, and Evaluation of a Full-range Adaptive Cruise Control System with Collision Avoidance," Control Engineering Practice, Vol. 17, No. 4, pp. 442-455. https://doi.org/10.1016/j.conengprac.2008.09.006
  9. Kim, D., Yoon, J. and Yi, K., 2013, "Steering and Cruise Control for Autonomous Driving in Stop-and- Go Traffic Situations," KSME Spring Conference Proceedings.