2.5D Evidential Grids for Dynamic Object Detection

Perception is a crucial and challenging part of Intelligent Transportation Systems. One of the main issues is to keep up with the moving objects in complex and dynamic environments. This paper proposes a method for dynamic object detection using Evidential 2.5D Occupancy Grids. The approach is based on a map representation for occupation modeling and navigable area definition. At each time step, a local grid is derived from the sensor data. Belief Theory is then retained to perform a grid fusion over time in order to keep track of the moving objects in the grid. The description of the dynamic behavior of objects in a scene is related to the conflict issued after the temporal fusion. Finally, the construction of the objects themselves is realized with a segmentation based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN). In order to validate the efficiency of the proposed approach, experimental results are provided based on the KITTI dataset. Performances are evaluated through comparison with the ground truth.