Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Collision detection"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Ultra Reliable Detection of Imminent Collision for Enhanced Occupant Safety
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Taghvaeeyan, Saber; Sun, Zhen; Mott, Michael; Rajamani, Rajesh
    This project focuses on the use of anisotropic magnetoresisitve (AMR) sensors for detection of an imminent unavoidable collision. An analytical formulation is developed for the variation of the magnetic field around a car as a function of position. Based on magnetic field measurements using AMR sensors, the position and velocity of any other car can be estimated and an imminent collision detected just prior to collision. The developed AMR sensor system has very high refresh rates, works at very small distances down to zero meters and is highly inexpensive. A variety of experimental results are presented to demonstrate the performance of the system for both one-dimensional and two-dimensional relative motion between cars. The second part of the project conducts simulations to show the benefits of detecting an imminent collision using the developed AMR sensors. An occupant model is developed to analyze occupant motion inside a car during a frontal collision. Analytical formulations and simulations are used to show how occupant safety can be enhanced when knowledge of an imminent collision is available.