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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". Talk or presentation, 21, February, 2008; Poster presented at BEARS 2008 poster session.

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.

Electronic downloads

Citation formats  
  • HTML
    Humberto Gonzalez, Esten Ingar Grøtli, Todd
    Templeton, Jan Biermeyer, Jonathan Sprinkle, Shankar Sastry.
    <a
    href="http://chess.eecs.berkeley.edu/pubs/407.html"
    ><i>Transitioning Control and Sensing Technologies
    from Fully-autonomous Driving to Driver Assistance
    Systems</i></a>, Talk or presentation,  21,
    February, 2008; Poster presented at BEARS 2008 poster
    session.
  • 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".
    Talk or presentation,  21, February, 2008; Poster presented
    at BEARS 2008 poster session.
  • BibTeX
    @presentation{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},
        day = {21},
        month = {February},
        year = {2008},
        note = {Poster presented at BEARS 2008 poster session.},
        abstract = {<p>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> <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 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.</p>
                  <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>
                  <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.</p>},
        URL = {http://chess.eecs.berkeley.edu/pubs/407.html}
    }
    

Posted by Todd Templeton on 31 Mar 2008.
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