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Distance Determination for an Automobile Environment

Shane Tuohy. Distance Determination for an Automobile Environment. Introduction. In 2008, rear end collisions accounted for almost 25% of all injuries sustained in road traffic accidents on Irish roads [ RSA Road Collision Factbook 2008]

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Distance Determination for an Automobile Environment

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  1. Shane Tuohy Distance Determination for an Automobile Environment

  2. Introduction • In 2008, rear end collisions accounted for almost 25% of all injuries sustained in road traffic accidents on Irish roads [RSA Road Collision Factbook 2008] • Effective distance determination can go a long way to reducing injuries

  3. Current Systems • Mercedes Pre-Safe • Audi Pre-Sense Plus • Toyota Pre-Collision System • All are RADAR systems • Expensive • Cannot detect humans, animals • Susceptible to interference

  4. System Overview • Front facing standard optical camera • Cheap • Many uses • Simple to install

  5. OpenCV • Begun by Intel, currently maintained by community, under stewardship of Willow Garage • Extensive library of Computer Vision functions • C, C++, Python, Java • No need to continually ‘reinvent the wheel’

  6. System Overview

  7. System Overview

  8. Image Processing Steps

  9. Thresholding • Remove road surface and highlight objects • Sample road surface in front of vehicle • Remove pixels ±35 of sampled value • Apply binary threshold

  10. Image Processing Steps

  11. The Problem • Distance in image does not change linearly as vehicle changes position

  12. The Solution • Inverse Perspective Mapping

  13. Inverse Perspective Mapping • Geometric transform which allows us to remove perspective effect

  14. Image Processing Steps

  15. Distance Determination • All road pixels are zero • Analyze area in front of car • Find first non zero pixels • Translate to distance using scaling factor

  16. Accurate Distance Calibration • How can we know this ‘scaling factor’? • Need to calibrate for particular camera setup • Can be done once for given environment and parameters • Lay 1m object on road surface • Use chessboard pattern of known size • Roughly calculated for project testing

  17. System Overview

  18. Information Overlay • Provide graphical feedback to user

  19. Project Milestones • Threshold to remove road surface. Generate transformation matrix • Transform image to IPM view • Distance determination • Graphics overlay • Modify algorithm for use on a real time video stream

  20. Conclusion • Further work possible • Improve thresholding for different road conditions • Improve performance of IPM algorithm • Automatic calibration implementation • Paper submitted to ISSC 2010, awaiting review • S. Tuohy, D. O Cualain, M. Glavin, E. Jones:“Distance Determination for an Automobile Environment using Inverse Perspective Mapping in OpenCV” • Successful implementation of proposed algorithm

  21. Demonstration

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