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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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