Grid mapping in dynamic road environments: Classification of dynamic cell hypothesis via tracking

We propose a method capable of acquiring an occupancy grid map-based representation of the local, static driving environment around an intelligent vehicle in the presence of dynamic objects. These corrupt the representation due to violating the underlying static-world assumptions of common grid mapping algorithms and are therefore detected and filtered from the map. For this purpose, a subsequent step is suggested that identifies, clusters and merges dynamic cell hypothesis in a novel way. Thereafter, an Interacting-Multiple-Model-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) tracker is used to classify of whether cell movements behave consistently with possible movement characteristics of real dynamic objects or are just generated by noise or newly observed static environment. In opposition to many other approaches, the method explicitly combines information of newly occupied and free areas, completes the shape of only partly visible dynamic objects and uses an advanced object tracking scheme to clean the grid from dynamic object corruptions. The method is evaluated with grids generated by an automotive radar and stereo camera in real traffic environments.