Volume 25 Issue 1 - Publication Date: 1 January 2006
Special Issue on the Fourth International Conference on Field and Service Robotics, 2003
Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application
C. Coué, C. Pradalier, C. Laugier, T. Fraichard, and P. Bessière INRIA, Rhône-Alpes and Gravir-CNRS
Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges 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 is usually the case in urban driving situations. In this paper, we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian occupancy filtering; it basically combines a four-dimensional occupancy grid representation of the obstacle state space with Bayesian filtering techniques.
Multimedia Key
= Video = Data = Code = Image
Example 1: Complete experimentation video for the scenario described in section IV.B. The CyCab detect and track a temporarily occluded obstacle using the BOF, while adjusting its speed to minimise collision risk. (3.8 MB)
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