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Team MIT Urban Challenge Technical Report

Research and Teaching Output of the MIT Community

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dc.contributor.advisor John Leonard en_US Leonard, John en_US Barrett, David en_US How, Jonathan en_US Teller, Seth en_US Antone, Matt en_US Campbell, Stefan en_US Epstein, Alex en_US Fiore, Gaston en_US Fletcher, Luke en_US Frazzoli, Emilio en_US Huang, Albert en_US Jones, Troy en_US Koch, Olivier en_US Kuwata, Yoshiaki en_US Mahelona, Keoni en_US Moore, David en_US Moyer, Katy en_US Olson, Edwin en_US Peters, Steven en_US Sanders, Chris en_US Teo, Justin en_US Walter, Matthew en_US
dc.contributor.other Robotics, Vision & Sensor Networks en_US 2007-12-17T13:50:57Z 2007-12-17T13:50:57Z 2007-12-14 en_US
dc.identifier.other MIT-CSAIL-TR-2007-058 en_US
dc.description.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. en_US
dc.format.extent 26 p. en_US
dc.relation Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory en_US
dc.relation en_US
dc.subject autonomous vehicle en_US
dc.subject robotics en_US
dc.subject DARPA Grand Challenge en_US
dc.subject path planning en_US
dc.subject machine perception en_US
dc.subject tracking en_US
dc.title Team MIT Urban Challenge Technical Report en_US

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