International audience

In self-navigation problems for autonomous vehicles, the variability of environmental conditions, complex scenes with vehicles and pedestrians, and the high-dimensional or real-time nature of tasks make segmentation challenging. Sensor fusion can representatively improve performances. Thus, this work highlights a late fusion concept used for semantic segmentation tasks in such perception systems. It is based on two approaches for merging information coming from two neural networks, one trained for camera data and one for LiDAR frames. The first approach involves fusing probabilities along with calculating partial conflicts and redistributing data. The second technique focuses on making individual decisions based on sources and fusing them later with weighted Shannon entropies. The two segmentation models are trained and evaluated on a particular KITTI semantic dataset. In the realm of multi-class segmentation tasks, the two fusion techniques are compared and evaluated with illustrative examples. Intersection over union metric and quality of decision are computed to assess the performance of each methodology.

Multiple Object Association is a considerable and challenging process in highly cluttered environments. Its aim is to thoroughly relate known objects to new detected ones which is hard in such conditions. The recurrent occlusions and pose variation of objects can raise ambiguity in decision-making. The objective of this paper is to establish a multi-feature fusion approach based on two sources describing, distinctly, the position and the motion direction of considered objects. The proposition is based on Dempster-Shafer theory to model the uncertainty and unreliability of sources and to ensure an evidential combination. Experimental results with real data from the KITTI database are presented to evaluate the proposed solution.

Multiple Object Association (MOA) is an essential process in making vehicles smarter. It consists in associating newly detected objects to previously known ones at each instant in a scene. To treat such matter, the paper proceeds by the use of the Belief Theory introduced by Dempster-Shafer. A crucial concern in this application is the data association complexity and its relative computation time which must be distinguishably minimized. For that, this paper proposes a new approach in order to fit to real-time Intelligent Transportation Systems (ITS) applications. The solution consists in a first step on selecting the sources which deliver the most pertinent information and in the second step to combine the reduced data set. The effectiveness in the association quality as well as in the complexity reduction is shown with a pedestrian tracking example from the KITTI vision benchmark.