A situation-adaptive lane-keeping support system: overview of the SAFELANE approach

  • Authors:

  • Angelos Amditis;Matthaios Bimpas;George Thomaidis;Manolis Tsogas;Mariana Netto;Saïd Mammar;Achim Beutner;Nikolaus Möhler;Tom Wirthgen;Stephan Zipser;Aria Etemad;Mauro Da Lio;Renzo Cicilloni

  • Affiliations:

  • Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Vehicle-Infrastructure-Driver Interactions Research Laboratory, Laboratoire Central des Ponts et Chaussées, Institut National de Recherche sur les Transports et Leur Sécurité, Versa ...;Informatique, Biologie Intégrative et Systèmes Complexes, Centre National de la Recherche Scientifique, University of Evry, Evry, France;Volvo Technology Corporation, Gothenburg, Sweden;Fraunhofer Institute for Transportation and Infrastructure Systems, Dresden, Germany;Fraunhofer Institute for Transportation and Infrastructure Systems, Dresden, Germany;Fraunhofer Institute for Transportation and Infrastructure Systems, Dresden, Germany;Ford Research Centre, Ford Forschungszentrum Aachen GmbH, Aachen, Germany;Department of Mechanical and Structural Engineering, University of Trento, Trento, Italy;Centro Richerche Fiat, Turin, Italy

  • Venue:
  • IEEE Transactions on Intelligent Transportation Systems
  • Year:
  • 2010

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Abstract

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.