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 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>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}
  }
  

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