ARGO

Architectural issues on vision-based automatic vehicle guidance: The experience of the ARGO project This paper discusses the main architectural issues of a challenging application of real-time image processing: the vision-based automatic guidance of road vehicles. Two algorithms for lane detection and obstacle localization, currently implemented on the ARGO autonomous vehicle developed at the University of Parma, are used as examples to compare two different computing engines -- a massively parallel special-purpose SIMD architecture and a general-purpose system -- while future trends in this field are proposed, based on the experience of the ARGO project.

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References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Harmouche, Mohamed; Laghrouche, Salah; Chitour, Yacine: Target point-based path-following controller for a car-type vehicle using bounded controls (2015)
  2. Marino, Riccardo; Scalzi, Stefano; Netto, Mariana: Integrated driver and active steering control for vision-based lane keeping (2012)
  3. Kim, Jonghwan; Lee, Chung-Hee; Lim, Young-Chul; Kwon, Soon: Stereo vision-based improving cascade classifier learning for vehicle detection (2011) ioport
  4. Toulminet, Gwenaëlle; Bertozzi, Massimo; Mousset, Stéphane; Bensrhair, Abdelaziz; Broggi, Alberto: Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis. (2006) ioport
  5. Huang, Yingping; Fu, Shan; Thompson, Chris: Stereovision-based object segmentation for automotive applications (2005)
  6. Consolini, Luca; Piazzi, Aurelio; Tosques, Mario: Path following of car-like vehicles using dynamic inversion (2003)
  7. Heimes, F.; Nagel, H.-H.: Towards active machine-vision-based driver assistance for urban areas (2002)
  8. Broggi, Alberto; Bertozzi, Massimo; Fascioli, Alessandra: Architectural issues on vision-based automatic vehicle guidance: The experience of the ARGO project (2000)