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Driving in Traffic: Short-Range Sensing for Urban Collision Avoidance

Chuck Thorpe, David Duggins, Jay Gowdy, Robert MacLachlan, Christoph Mertz, Mel Siegel, Arne Suppe, Chieh-Chih Wang and Teruko Yata
Conference Paper, Carnegie Mellon University, Proceedings of SPIE: Unmanned Ground Vehicle Technology IV, Vol. 4715, April, 2002

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Intelligent vehicles are beginning to appear on the market, but so far their sensing and warning functions only work on the open road. Functions such as run-off-road warning or adaptive cruise control are de-signed for the uncluttered environments of open high-ways. We are working on the much more difficult problem of sensing and driver interfaces for driving in urban ar-eas. We need to sense cars and pedestrians and curbs and fire plugs and bicycles and lamp posts; we need to predict the paths of our own vehicle and of other moving objects; and we need to decide when to issue alerts or warnings to both the driver of our own vehi-cle and (potentially) to nearby pedestrians. No single sensor is currently able to detect and track all relevant objects. We are working with radar, ladar, stereo vision, and a novel light-stripe range sensor. We have installed a subset of these sen-sors on a city bus, driving through the streets of Pitts-burgh on its normal runs. We are using different kinds of data fusion for different subsets of sensors, plus a coordinating framework for mapping objects at an abstract level.

BibTeX Reference
title = {Driving in Traffic: Short-Range Sensing for Urban Collision Avoidance},
author = {Chuck Thorpe and David Duggins and Jay Gowdy and Robert MacLachlan and Christoph Mertz and Mel Siegel and Arne Suppe and Chieh-Chih Wang and Teruko Yata},
booktitle = {Proceedings of SPIE: Unmanned Ground Vehicle Technology IV},
school = {Robotics Institute , Carnegie Mellon University},
month = {April},
year = {2002},
volume = {4715},
address = {Pittsburgh, PA},