Information Maps: A Practical Approach to Position Dependent Parameterization

In this contribution a practical approach to determine and store position dependent parameters is presented. These parameters can be obtained, among others, using experimental results or expert knowledge and are stored in 'Information Maps'. Each Information Map can be interpreted as a kind of static grid map and the framework allows to link different maps hierarchically. The Information Maps can be local or global, with static and dynamic information in it. One application of Information Maps is the representation of position dependent characteristics of a sensor. Thus, for instance, it is feasible to store arbitrary attributes of a sensor's preprocessing in an Information Map and utilize them by simply taking the map value at the current position. This procedure is much more efficient than using the attributes of the sensor itself. Some examples where and how Information Maps can be used are presented in this publication. The Information Map is meant to be a simple and practical approach to the problem of position dependent parameterization in all kind of algorithms when the analytical description is not possible or can not be implemented efficiently.

[1]  Klaus C. J. Dietmayer,et al.  Using grid maps to reduce the number of false positive measurements in advanced driver assistance systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[2]  Conrad Sanderson,et al.  Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments , 2010 .

[3]  Klaus C. J. Dietmayer,et al.  Pedestrian tracking using Random Finite Sets , 2011, 14th International Conference on Information Fusion.

[4]  Klaus C. J. Dietmayer,et al.  Multi-object tracking at intersections using the cardinalized probability hypothesis density filter , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[5]  Klaus C. J. Dietmayer,et al.  A sensor independent probabilistic fusion system for driver assistance systems , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[6]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[7]  Roland Chapuis,et al.  Dealing with occlusions with multi targets tracking algorithms for the real road context , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[8]  Klaus C. J. Dietmayer,et al.  Cooperative multi sensor network for traffic safety applications at intersections , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[9]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.