Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Target Vehicle State Estimation Algorithm Using V2V Communication

V2V 통신을 이용한 상대 차량 상태 추정 알고리즘 개발

  • 권우진 (서울대학교 공과대학 기계공학부) ;
  • 조아라 (서울대학교 공과대학 기계공학부) ;
  • 이경수 (서울대학교 공과대학 기계공학부)
  • Received : 2021.11.16
  • Accepted : 2022.04.05
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes the development of a target vehicle state estimation algorithm using vehicle-to-vehicle (V2V) communication. Perceiving the state of the target vehicle has great importance for successful autonomous driving and has been studied using various sensors and methods for many years. V2V communication has advantage of not being constrained by surrounding circumstances relative to other sensors. In this paper, we adopt the V2V signal for estimating the target vehicle state. Since applying only the V2V signal is improper by its low frequency and latency, the signal is used as additional measured data to improve the estimation accuracy. We estimate the target vehicle state using Extended Kalman filter (EKF); a point mass model was utilized in process update to predict the state of next step. The process update is followed by measurement update when ego vehicle receives V2V information. The proposed study evaluated state estimation by comparing input V2V information in an experiment where the ego vehicle follows the target vehicle behind it.

Keywords

Acknowledgement

본 논문은 산업통상자원부 산업기술혁신사업(10079730, 자동차전용도로/도심로 자율주행 시스템 개발 및 성능평가)의 지원을 받아 수행하였습니다.

References

  1. Schoettle, B. (2017), Sensor fusion: A comparison of sensing capabilities of human drivers and highly automated vehicles, University of Michigan.
  2. Li, Q., Chen, L., Li, M., Shaw, S. L., and Nuchter, A. (2013), A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios, IEEE Transactions on Vehicular Technology, 63(2), 540~555. https://doi.org/10.1109/TVT.2013.2281199
  3. Yi, C., Zhang, K., and Peng, N. (2019), A multi-sensor fusion and object tracking algorithm for self-driving vehicles, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of automobile engineering, 233(9), 2293~2300. https://doi.org/10.1177/0954407019867492
  4. Suwandi, B., Kitasuka, T., and Aritsugi, M. (2019), Vehicle vibration error compensation on IMU-accelerometer sensor using adaptive filter and low-pass filter approaches, Journal of Information Processing, 27, 33~40. https://doi.org/10.2197/ipsjjip.27.33
  5. 김인수, 박재홍, 이은영, 이은덕, 신재곤, 김대원 (2017), V2V 기본 안전 메시지 데이터의 유효성 검증, 한국자동차안전학회지, 9(2), pp. 33~39.
  6. Simon, D. (2006), Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons.
  7. Polack, P., Altche, F., d'Andrea-Novel, B., and de La Fortelle, A. (2017, June), The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles, In 2017 IEEE intelligent vehicles symposium (IV) (pp. 812~818). IEEE.
  8. Bucy, R. S. and Joseph, P. D. (2005), Filtering for stochastic processes with applications to guidance (Vol. 326), American Mathematical Soc.