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Lane estimation for autonomous vehicles using vision and LIDAR

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dc.contributor.advisor Seth Teller. en_US
dc.contributor.author Huang, Albert Shuyu en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2010-08-26T15:19:27Z
dc.date.available 2010-08-26T15:19:27Z
dc.date.copyright 2010 en_US
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/57535
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Cataloged from student submitted PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 109-114). en_US
dc.description.abstract Autonomous ground vehicles, or self-driving cars, require a high level of situational awareness in order to operate safely and eciently in real-world conditions. A system able to quickly and reliably estimate the location of the roadway and its lanes based upon local sensor data would be a valuable asset both to fully autonomous vehicles as well as driver assistance technologies. To be most useful, the system must accommodate a variety of roadways, a range of weather and lighting conditions, and highly dynamic scenes with other vehicles and moving objects. Lane estimation can be modeled as a curve estimation problem, where sensor data provides partial and noisy observations of curves. The number of curves to estimate may be initially unknown and many of the observations may be outliers and false detections (e.g., due to tree shadows or lens are). The challenge is to detect lanes when and where they exist, and to update the lane estimates as new observations are received. This thesis describes algorithms for feature detection and curve estimation, as well as a novel curve representation that permits fast and ecient estimation while rejecting outliers. Locally observed road paint and curb features are fused together in a lane estimation framework that detects and estimates all nearby travel lanes. en_US
dc.description.abstract (cont.) The system handles roads with complex geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. Early versions of these algorithms successfully guided a fully autonomous Land Rover LR3 through the 2007 DARPA Urban Challenge, a 90km urban race course, at speeds up to 40 km/h amidst moving traffic. We evaluate these and subsequent versions with a ground truth dataset containing manually labeled lane geometries for every moment of vehicle travel in two large and diverse datasets that include more than 300,000 images and 44km of roadway. The results illustrate the capabilities of our algorithms for robust lane estimation in the face of challenging conditions and unknown roadways. en_US
dc.description.statementofresponsibility by Albert S. Huang. en_US
dc.format.extent 114 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Lane estimation for autonomous vehicles using vision and LIDAR en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 631212135 en_US


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