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Multi-Sensor Fusion for Obstacle Detection and Recognition: A Belief-Based Approach

Abstract : This paper presents an obstacle detection and classification method for intelligent vehicles. We use both a camera and radar in a multi-sensor perception framework. Our main goal is to improve the reliability of pedestrian and vehicle recognition of the system, avoiding false alarms and reducing miss detections in an uncertain environment with imprecise models. To deal with this issue, an evidential sensor fusion is developed and implemented. Simulation results and preliminary experimental test are presented and confirm the reliability improvement.
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https://hal-uphf.archives-ouvertes.fr/hal-03383067
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Submitted on : Monday, October 18, 2021 - 2:20:17 PM
Last modification on : Wednesday, November 3, 2021 - 8:44:55 AM

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Mokhtar Bouain, Denis Berdjag, Nizar Fakhfakh, Rabie Ben Atitallah. Multi-Sensor Fusion for Obstacle Detection and Recognition: A Belief-Based Approach. 2018 21st International Conference on Information Fusion (FUSION 2018), Jul 2018, Cambridge, United Kingdom. pp.1217-1224, ⟨10.23919/ICIF.2018.8455850⟩. ⟨hal-03383067⟩

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