Multi-objects association in perception of dynamical situation

TL;DR

This paper introduces a multi-object association algorithm for intelligent vehicle perception systems that handles uncertainty and ambiguity using belief theory and fuzzy mathematics, improving environment mapping and object tracking.

Contribution

It develops a novel multi-object association method incorporating belief theory and fuzzy mathematics to manage uncertain sensor data in dynamic environments.

Findings

Effective handling of uncertain and ambiguous sensor data.

Improved object tracking accuracy in dynamic environments.

Robust environment mapping for intelligent vehicles.

Abstract

In current perception systems applied to the rebuilding of the environment for intelligent vehicles, the part reserved to object association for the tracking is increasingly significant. This allows firstly to follow the objects temporal evolution and secondly to increase the reliability of environment perception. We propose in this communication the development of a multi-objects association algorithm with ambiguity removal entering into the design of such a dynamic perception system for intelligent vehicles. This algorithm uses the belief theory and data modelling with fuzzy mathematics in order to be able to handle inaccurate as well as uncertain information due to imperfect sensors. These theories also allow the fusion of numerical as well as symbolic data. We develop in this article the problem of matching between known and perceived objects. This makes it possible to update a dynamic environment map for a vehicle. The belief theory will enable us to quantify the belief in the association of each perceived object with each known object. Conflicts can appear in the case of object appearance or disappearance, or in the case of a confused situation or bad perception. These conflicts are removed or solved using an assignment algorithm, giving a solution called the " best " and so ensuring the tracking of some objects present in our environment.