DOI QR코드

DOI QR Code

V2V based Cut-In Vehicle Yield Algorithm for Congested Traffic Autonomous Driving

혼잡 교통류에서의 V2V 기반 Cut-In 차량 양보 거동 계획 알고리즘

  • 김창희 (서울대학교 기계항공공학부) ;
  • 채흥석 (서울대학교 기계항공공학부) ;
  • 윤영민 (서울대학교 기계항공공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2020.11.09
  • Accepted : 2021.09.07
  • Published : 2022.06.30

Abstract

This paper presents motion planning algorithm that yields to intervening side lane vehicles in a congested traffic flow based on vehicle to vehicle (V2V) communication. Autonomous driving in dense traffic situation requires advanced driving performance in terms of vehicle interaction and risk mitigation. One of the most important functions necessary for congested traffic autonomous driving is to predict the lane change intention of the side lane target vehicle. However, implementing this function by using only environmental sensors has limitations. In this study, V2V communication is used to overcome the limitations and determine the intention of cut-in vehicles. Lane change intention of the intervening side lane vehicle is inferred by its longitudinal speed, steering angle, and turn signal light information received by the on-board-unit (OBU). Once the yield decision is made, the subject vehicle decelerates to generate sufficient clearance for the target vehicle to enter. Validation of the algorithm was conducted with actual autonomous test vehicles.

Keywords

Acknowledgement

본 연구는 국토교통부 및 국토교통과학기술 진흥원의 2018년 교통물류연구사업(18TLRP-B146733-01, 자율주행기반 대중교통시스템 실증 연구)의 지원을 받아 연구되었음을 밝히며, 이에 감사드립니다.

References

  1. Nagai, M., 2014, "Research into ADAS with Autonomous Driving Intelligence for Future Innovation", 5th International Munich Chassis Symposium 2014, pp. 779~793.
  2. Darbha, S., Konduri, S., Pagilla, P. R., 2018, "Benefits of V2V Communication for Autonomous and Connected Vehicles", IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1954~1963.
  3. Jung, C., Lee, D., Lee, S., Shim, D. H., 2020, V2X - Communication - Aided Autonomous Driving: System Design and Experimental Validation, Sensors, 20(10), 2903. https://doi.org/10.3390/s20102903
  4. Hobert, L., Festag, A., Llatser, I., Altomare, L., Visintainer, F., Kovacs, A., 2015, Enhancements of V2X communication in support of cooperative autonomous driving, IEEE communications magazine, Vol. 53, No. 12, pp. 64~70. https://doi.org/10.1109/MCOM.2015.7355568
  5. Chae, H., Yi, K., 2020. Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles. IEEE Access, Vol. 8, pp. 51363~51376. https://doi.org/10.1109/access.2020.2980391
  6. Chae, H., Jeong, Y., Kim, S., Lee, H., Park, J., Yi, K., 2018, Design and vehicle implementation of autonomous lane change algorithm based on probabilistic prediction. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2845~2852.
  7. Chae, H. S., Lee, M. S. and Yi, K. S., 2017, "Probabilistic prediction based automated driving motion planning algorithm for lane change", 2017 17th International Conference.