Systematic information fusion methodology for static and dynamic obstacle detection in ITS

Environment understanding technology is vital for intelligent vehicles that are expected to automatically respond to fast changing environment and dangerous situations. To obtain perception abilities, it is expected to automatically detect static and dynamic obstacles, and estimate their related information. Conventional methods independently detect individual piece of overall information. Each process is computationally heavy and often produces noisy results without high reliability. Here we propose fusion-based and layered-based methodology to systematically detect dynamic and static obstacles and obtain their location/timing information for visible and infrared sequences. The proposed obstacle detection methodologies take advantage of connection between different information and increase the computational accuracy of obstacle information estimation, yet reduce computing time, thus improving environment understanding abilities, and driving safety.