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Team MIT Urban Challenge Technical Report

Author(s)
Leonard, John; Barrett, David; How, Jonathan; Teller, Seth; Antone, Matt; Campbell, Stefan; Epstein, Alex; Fiore, Gaston; Fletcher, Luke; Frazzoli, Emilio; Huang, Albert; Jones, Troy; Koch, Olivier; Kuwata, Yoshiaki; Mahelona, Keoni; Moore, David; Moyer, Katy; Olson, Edwin; Peters, Steven; Sanders, Chris; Teo, Justin; Walter, Matthew; ... Show more Show less
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DownloadMIT-CSAIL-TR-2007-058.pdf (12.57Mb)
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MIT-CSAIL-TR-2007-058.ps (504.9Mb)
Other Contributors
Robotics, Vision & Sensor Networks
Advisor
John Leonard
Metadata
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Abstract
This technical report describes Team MIT’s approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios.
Date issued
2007-12-14
URI
http://hdl.handle.net/1721.1/39822
Other identifiers
MIT-CSAIL-TR-2007-058
Keywords
autonomous vehicle, robotics, DARPA Grand Challenge, path planning, machine perception, tracking

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