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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Study on IMM-PDAF based Sensor Fusion Method for Compensating Lateral Errors of Detected Vehicles Using Radar and Vision Sensors

레이더와 비전 센서를 이용하여 선행차량의 횡방향 운동상태를 보정하기 위한 IMM-PDAF 기반 센서융합 기법 연구

  • Jang, Sung-woo (The Department of Secured Smart Electric Vehicle Engineering, Kookmin University) ;
  • Kang, Yeon-sik (Department of Automotive Engineering, Kookmin University)
  • 장성우 (국민대학교 대학원 보안-스마트 전기자동차공학과) ;
  • 강연식 (국민대학교 자동차공학과)
  • Received : 2016.04.01
  • Accepted : 2016.06.16
  • Published : 2016.08.01
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Abstract

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

Keywords

Acknowledgement

Supported by : 한국연구재단

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