Driving situation recognition with uncertainty management and rule-based systems

Jean-Marc Nigro; Sophie Loriette-Rougegrez; Michèle Rombaut

Driving situation recognition with uncertainty management and rule-based systems

Jean-Marc Nigro ¹ Sophie Loriette-Rougegrez ¹ Michèle Rombaut ^{2, 3}

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

ICD - Institut Charles Delaunay

2 GIPSA-AGPIG - AGPIG

GIPSA-DIS - Département Images et Signal

3 IUT de Grenoble

Abstract : The recognition of a temporal sequence is a complex problem, especially in the framework of driving situations. However, this recognition is essential for the development of driving assistance systems. This paper presents a rule-based system that manages the real-time measurements got from sensors of an experimental vehicle, in order to determine the current possible maneuvers worked out by the driver. The particularity of the proposed system is that it manages the inaccuracy of the data and the uncertainty of the recognition, using fuzzy subsets and beliefs on hypotheses.

Document type :

Journal articles

Domain :

Computer Science [cs] / Artificial Intelligence [cs.AI]

Complete list of metadatas

https://hal-utt.archives-ouvertes.fr/hal-02308784
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, October 8, 2019 - 5:04:46 PM
Last modification on : Thursday, October 10, 2019 - 1:03:37 AM

Driving situation recognition with uncertainty management and rule-based systems

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Driving situation recognition with uncertainty management and rule-based systems

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