Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6904389 | Applied Soft Computing | 2017 | 44 Pages |
Abstract
Automatic scene understanding from multimodal data is a key task in the design of fully autonomous vehicles. The theory of belief functions has proved effective for fusing information from several sensors at the superpixel level. Here, we propose a novel framework, called evidential grammars, which extends stochastic grammars by replacing probabilities by belief functions. This framework allows us to fuse local information with prior and contextual information, also modeled as belief functions. The use of belief functions in a compositional model is shown to allow for better representation of the uncertainty on the priors and for greater flexibility of the model. The relevance of our approach is demonstrated on multi-modal traffic scene data from the KITTI benchmark suite.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Jean-Baptiste Bordes, Franck Davoine, Philippe Xu, Thierry DenÅux,