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Map-Aided Evidential Grids for Driving Scene Understanding

Abstract : Evidential grids have recently shown interesting properties for mobile object perception since the Dempster– Shafer framework allow them to handle efficiently partial information which is a frequent situation when driving in complex urban areas. This article deals with a lidar perception scheme that is enhanced by geo-referenced maps used as an additional source of information in a multi-grid fusion frame-work. The paper presents the key stages of such a data fusion process. An adaptation of the conjunctive combination rule is presented to refine the analysis of the conflicting information. The method relies on temporal accumulation to make the distinction between stationary and moving objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the state of the occupied cells by differentiating moving objects, parked cars, urban infrastructure and buildings. Another output of this approach is the capability to separate the navigable space from the non-navigable one. Experiments carried out on real traffic conditions with an equipped car illustrate the performance of such an approach.
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Contributor : Marek Kurdej Connect in order to contact the contributor
Submitted on : Monday, February 2, 2015 - 10:25:16 PM
Last modification on : Tuesday, November 16, 2021 - 4:30:36 AM
Long-term archiving on: : Sunday, May 3, 2015 - 11:30:23 AM

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Marek Kurdej, Julien Moras, Philippe Bonnifait, Véronique Cherfaoui. Map-Aided Evidential Grids for Driving Scene Understanding. IEEE Intelligent Transportation Systems Magazine, IEEE, 2015, pp.30-41. ⟨10.1109/MITS.2014.2352371⟩. ⟨hal-01112443⟩

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