  Automation driving techniques have seen tremendous progresses these last
years, particularly due to a better perception of the environment. In order to
provide safe yet not too conservative driving in complex urban environment,
data fusion should not only consider redundant sensing to characterize the
surrounding obstacles, but also be able to describe the uncertainties and
errors beyond presence/absence (be it binary or probabilistic). This paper
introduces an enriched representation of the world, more precisely of the
potential existence of obstacles through an evidential grid map. A method to
create this representation from 2 very different sensors, laser scanner and
stereo camera, is presented along with algorithms for data fusion and temporal
updates. This work allows a better handling of the dynamic aspects of the urban
environment and a proper management of errors in order to create a more
reliable map. We use the evidential framework based on the Dempster-Shafer
theory to model the environment perception by the sensors. A new combination
operator is proposed to merge the different sensor grids considering their
distinct uncertainties. In addition, we introduce a new long-life layer with
high level states that allows the maintenance of a global map of the entire
vehicle's trajectory and distinguish between static and dynamic obstacles.
Results on a real road dataset show that the environment mapping data can be
improved by adding relevant information that could be missed without the
proposed approach.
