Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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Vehicle State Estimation Robust to Wheel Slip Using Extended Kalman Filter

휠 슬립에 강건한 확장칼만필터 기반 차량 상태 추정

  • 전명근 (서울대학교 기계공학부) ;
  • 조아라 (서울대학교 기계공학부) ;
  • 이경수 (서울대학교 기계공학부)
  • Received : 2022.06.07
  • Accepted : 2022.09.13
  • Published : 2022.12.31
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Abstract

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

Keywords

Acknowledgement

본 논문은 국토교통부/국토교통과학기술진흥원(과제번호 21AMDP-C162182-01)"의 지원을 받아 수행하였습니다.

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