Two-dimensional sensor system for automotive crash prediction

Saber Taghvaeeyan, Rajesh Rajamani

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This paper focuses on the use of magnetoresistive and sonar sensors for imminent collision detection in cars. The magnetoresistive sensors are used to measure the magnetic field from another vehicle in close proximity, to estimate relative position, velocity, and orientation of the vehicle from the measurements. First, an analytical formulation is developed for the planar variation of the magnetic field from a car as a function of 2-D position and orientation. While this relationship can be used to estimate position and orientation, a challenge is posed by the fact that the parameters in the analytical function vary with the type and model of the encountered car. Since the type of vehicle encountered is not known a priori, the parameters in the magnetic field function are unknown. The use of both sonar and magnetoresistive sensors and an adaptive estimator is shown to address this problem. While the sonar sensors do not work at very small intervehicle distance and have low refresh rates, their use during a short initial time duration leads to a reliable estimator. Experimental results are presented for both a laboratory wheeled car door and for a full-scale passenger sedan. The results show that planar position and orientation can be accurately estimated for a range of relative motions at different oblique angles.

Original languageEnglish (US)
Article number6606810
Pages (from-to)178-190
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume15
Issue number1
DOIs
StatePublished - Feb 1 2014

Keywords

  • Crash detection
  • crash sensors
  • extended Kalman filter (EKF)
  • magnetic sensors
  • sensor fusion
  • vehicle position sensors

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