Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Performance Analysis on the IMM-PDAF Method for Longitudinal and Lateral Maneuver Detection using Automotive Radar Measurements

차량용 레이더센서를 이용한 IMM-PDAF 기반 종-횡방향 운동상태 검출 및 추정기법에 대한 성능분석

  • Yoo, Jeongjae (Department of Automotive Engineering, Kookmin University) ;
  • Kang, Yeonsik (Department of Automotive Engineering, Kookmin University)
  • Received : 2014.11.15
  • Accepted : 2014.12.30
  • Published : 2015.03.01
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Abstract

In order to develop an active safety system which avoids or mitigates collisions with preceding vehicles such as autonomous emergency braking (AEB), accurate state estimation of the nearby vehicles is very important. In this paper, an algorithm is proposed using 3 dynamic models to better estimate the state of a vehicle which has various dynamic patterns in both longitudinal and lateral direction. In particular, the proposed algorithm is based on the Interacting Multiple Model (IMM) method which employs three different dynamic models, in cruise mode, lateral maneuver mode and longitudinal maneuver mode. In addition, a Probabilistic Data Association Filter (PDAF) is utilized as a data association algorithm which can improve the reliability of the measurement under a clutter environment. In order to verify the performance of the proposed method, it is simulated in comparison with a Kalman filter method which employs a single dynamic model. Finally, the proposed method is validated using radar data obtained from the field test in the proving ground.

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References

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