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Simplistic Intelligent Vehicle Control Using Real-Time Stereo. David Hoffert CS 766 Final Project Fall 2006. Motivation. 43,200 U.S. highways deaths in 2005 Insurance Institute for Highway Safety credits reduction solely to technology Drunken driving rates static 1994 – 2004

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Simplistic intelligent vehicle control using real time stereo l.jpg

Simplistic Intelligent Vehicle Control Using Real-Time Stereo

David Hoffert

CS 766 Final Project

Fall 2006


Motivation l.jpg
Motivation Stereo

  • 43,200 U.S. highways deaths in 2005

  • Insurance Institute for Highway Safety credits reduction solely to technology

    • Drunken driving rates static 1994 – 2004

  • Driving is purely a rule-based task

    • And computers don’t drink and drive


History l.jpg
History Stereo

  • 2005 DARPA Grand Challenge

    • 5 vehicles complete 132-mile desert race course autonomously

    • But only one uses any kind of computer vision

    • Others just laser range finders, etc.

  • 2007 DARPA Urban Challenge

    • Now traffic, street signs, pedestrians, etc.

    • Computer vision no longer not an option


Vehicular computer vision tasks l.jpg
Vehicular Computer Vision Tasks Stereo

  • Object recognition

    • Street signs, to follow the rules

  • Edge detection

    • Lane lines and the edge of the road

  • Stereo/depth mapping

    • Don’t hit the car/person/object in front of you

  • Common requirement: real-time!


Real time stereo l.jpg
Real-Time Stereo Stereo

  • Broad assumption for driving: steer away from objects that are close to you

    • Don’t even have to classify first for this

  • So all need is foreground vs. background bitmap for each pixel, but need it fast

  • Kolmogorov, Criminisi, et al, 2005


Their application background substitution l.jpg
Their Application: Background Substitution Stereo

  • Exact same concept for stereo navigation

  • Only difference is how use bitmap information


Real time stereo algorithm l.jpg
Real-Time Stereo Algorithm Stereo

  • Energy functions for stereo, color, and contrast

  • Costs associated with each state, transition

  • Dynamic Programming to find least cost path


Goals l.jpg
Goals Stereo

  • Implement Kolmogorov real-time stereo algorithm to replicate their results

  • No remote control car with webcams, just use the sample inputs from website

    • Proof of concept, gathering appropriate data is a relative waste of time for this project

  • For each frame, indicate safest driving direction, simple x-histogram of foreground


Examples l.jpg
Examples Stereo


Future extensions l.jpg
Future Extensions Stereo

  • For a toy robot, this could potentially be sufficient; on the road, unacceptable

  • Biggest missing piece: stay on road, in lane

    • Edge detection, color segmentation

  • Greater sophistication than “maximize distance from foreground”

  • Street sign recognition to obey traffic laws


Questions l.jpg
Questions Stereo


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