Reliable and efficient perception and reasoning in dynamic and densely cluttered environments is still a major challenge for driver assistance systems. Most of today’s systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However, these approaches usually fail in more complex environments featuring a large variety of potential obstacles, as it is usually the case in urban driving situations.
In this work, we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian Occupancy Filtering (BOF), it basically combines a 4-dimensional occupancy grid representation of the obstacle state-space with Bayesian filtering techniques.
Key words: Multi-target tracking, bayesian state estimation, occupancy grid, Bayesian Occupency Filters, Bayesian robotics, probabilistic robotics