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Driving situation recognition in the CASSICE project towards an uncertainty management

Abstract : The problem addressed in this paper concerns the recognition of real driving maneuvers using reports acquired from an equipped vehicle. The interest of process is to permit psychologists to try building a model of the driver behaviour, taking into account his real environment. We focus on the recognition of the maneuvers performed by the driver, specially the overtaking maneuver. We consider a maneuver as a sequence of events. Such a representation allows one to study the usefulness of several formal models: rule-based systems, transition graphs or Petri nets. Then, according to the inputs obtained from the system's sensors at different times, the goal is to evaluate the driver's confidence when such a maneuver is in progress. In this paper, the confidence is modeled by a distribution of mass of evidence as proposed in the Dempster-Shafer theory.
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Jean-Marc Nigro, Sophie Loriette-Rougegrez, Michèle Rombaut, Iman Jarkass. Driving situation recognition in the CASSICE project towards an uncertainty management. 2000 IEEE Intelligent Transportation Systems. Proceedings, Oct 2000, Dearborn, United States. pp.71-76, ⟨10.1109/ITSC.2000.881020⟩. ⟨hal-02868448⟩



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