Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application
Open Access
- 1 January 2006
- journal article
- Published by SAGE Publications in The International Journal of Robotics Research
- Vol. 25 (1), 19-30
- https://doi.org/10.1177/0278364906061158
Abstract
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.Keywords
This publication has 10 references indexed in Scilit:
- Bayesian Robot ProgrammingAutonomous Robots, 2004
- Multisensor on-the-fly localization:: Precision and reliability for applicationsRobotics and Autonomous Systems, 2001
- The design and implementation of a Bayesian CAD modeler for robotic applicationsAdvanced Robotics, 2001
- Tracking Multiple Moving Objects for Real-Time Robot NavigationAutonomous Robots, 2000
- Planning and acting in partially observable stochastic domainsArtificial Intelligence, 1998
- Learning metric-topological maps for indoor mobile robot navigationArtificial Intelligence, 1998
- A formulation of multitarget tracking as an incomplete data problemIEEE Transactions on Aerospace and Electronic Systems, 1997
- The computational complexity of probabilistic inference using bayesian belief networksArtificial Intelligence, 1990
- Using occupancy grids for mobile robot perception and navigationComputer, 1989
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960