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Transitioning Control and Sensing Technologies from Fully-autonomous Driving to Driver Assistance Systems
Humberto Gonzalez, Esten Ingar Grøtli, Todd Templeton, Jan Biermeyer, Jonathan Sprinkle, Shankar Sastry

Citation
Humberto Gonzalez, Esten Ingar Grøtli, Todd Templeton, Jan Biermeyer, Jonathan Sprinkle, Shankar Sastry. "Transitioning Control and Sensing Technologies from Fully-autonomous Driving to Driver Assistance Systems". AAET: Automation, Assistance, and Embedded Systems for Transportation, Technical University, Braunschweig, 13, February, 2008.

Abstract
Based on our experience in the DARPA Urban Challenge and on current trends in consumer automobiles, we believe that driver assistance systems can be significantly improved by new techniques in control and sensing that have been developed for fully-autonomous driving.

In particular, from the control community, real-time Model Predictive Control (MPC) can be used as the next generation of cruise control for automobiles, offering a principled method for robustly incorporating information from automobiles’ existing sensing systems, such as GPS and odometry, as well as from additional sensors that will be used in future, complimentary driver assistance systems, such as visible-light cameras, infrared (IR) cameras and laser scanners.

From the sensing community, we believe that obstacle-detection systems using passive (hence, noninterfering) and cost-effective visible-light cameras and thermal IR cameras, originally developed for fully-autonomous driving, are also a valuable addition to the driver assistance toolbox, offering the ability to warn drivers about moving or heat-producing obstacles, including pedestrians and other automobiles.

In this paper we will discuss methods, derived from fully-autonomous vehicle research, for real-time Model Predictive Control (MPC), segmentation of moving (relative to the ground) obstacles using visible-light cameras, and detection of heat-producing objects using thermal infrared (IR) cameras, as well as their application to driver assistance systems.

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Citation formats  
  • HTML
    Humberto Gonzalez, Esten Ingar Grøtli, Todd
    Templeton, Jan Biermeyer, Jonathan Sprinkle, Shankar Sastry.
    <a
    href="http://chess.eecs.berkeley.edu/pubs/602.html"
    >Transitioning Control and Sensing Technologies from
    Fully-autonomous Driving to Driver Assistance
    Systems</a>, AAET: Automation, Assistance, and
    Embedded Systems for Transportation, Technical University,
    Braunschweig, 13, February, 2008.
  • Plain text
    Humberto Gonzalez, Esten Ingar Grøtli, Todd
    Templeton, Jan Biermeyer, Jonathan Sprinkle, Shankar Sastry.
    "Transitioning Control and Sensing Technologies from
    Fully-autonomous Driving to Driver Assistance Systems".
    AAET: Automation, Assistance, and Embedded Systems for
    Transportation, Technical University, Braunschweig, 13,
    February, 2008.
  • BibTeX
    @inproceedings{GonzalezGrtliTempletonBiermeyerSprinkleSastry08_TransitioningControlSensingTechnologiesFromFullyautonomous,
        author = {Humberto Gonzalez and Esten Ingar Grøtli and Todd
                  Templeton and Jan Biermeyer and Jonathan Sprinkle
                  and Shankar Sastry},
        title = {Transitioning Control and Sensing Technologies
                  from Fully-autonomous Driving to Driver Assistance
                  Systems},
        booktitle = {AAET: Automation, Assistance, and Embedded Systems
                  for Transportation},
        organization = {Technical University, Braunschweig},
        day = {13},
        month = {February},
        year = {2008},
        abstract = {Based on our experience in the DARPA Urban
                  Challenge and on current trends in consumer
                  automobiles, we believe that driver assistance
                  systems can be significantly improved by new
                  techniques in control and sensing that have been
                  developed for fully-autonomous driving. <p>In
                  particular, from the control community, real-time
                  Model Predictive Control (MPC) can be used as the
                  next generation of cruise control for automobiles,
                  offering a principled method for robustly
                  incorporating information from automobilesâ
                  existing sensing systems, such as GPS and
                  odometry, as well as from additional sensors that
                  will be used in future, complimentary driver
                  assistance systems, such as visible-light cameras,
                  infrared (IR) cameras and laser scanners. <p>From
                  the sensing community, we believe that
                  obstacle-detection systems using passive (hence,
                  noninterfering) and cost-effective visible-light
                  cameras and thermal IR cameras, originally
                  developed for fully-autonomous driving, are also a
                  valuable addition to the driver assistance
                  toolbox, offering the ability to warn drivers
                  about moving or heat-producing obstacles,
                  including pedestrians and other automobiles. <p>In
                  this paper we will discuss methods, derived from
                  fully-autonomous vehicle research, for real-time
                  Model Predictive Control (MPC), segmentation of
                  moving (relative to the ground) obstacles using
                  visible-light cameras, and detection of
                  heat-producing objects using thermal infrared (IR)
                  cameras, as well as their application to driver
                  assistance systems. },
        URL = {http://chess.eecs.berkeley.edu/pubs/602.html}
    }
    

Posted by Christopher Brooks on 17 Jun 2009.
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