Object tracking and dynamic estimation on evidential grids

Autonomous driving is one of the most challenging tasks of the automotive industry. As a subtask, the estimation of driveable and non driveable space is often solved by applying occupancy grids. The information about non driveable space can be used to improve object tracking. This paper presents an approach for object tracking and modelling in an occupancy grid map. Tracking objects on grid cells yields the advantage of a consistent environmental model on the occupancy grid map. We introduce the occupancy grid map as the only information source for the object tracking module. Taking advantage of the Dempster Shafer theory, a dynamic belief of conflicting cells can be estimated. This dynamic belief is then accumulated in a tracked object model. This is a grid based free form object model that uses detached grid cells to model vehicles in urban environment. We reduce false positives and initialization time by maintaining a dynamic belief for each object.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  Mohammad Bagher Menhaj,et al.  A Joint Probability Data Association Filter Algorithm for Multiple Robot Tracking Problems , 2008 .

[4]  Klaus C. J. Dietmayer,et al.  Simultaneous tracking and shape estimation with laser scanners , 2013, Proceedings of the 16th International Conference on Information Fusion.

[5]  Y. Bar-Shalom,et al.  The probabilistic data association filter , 2009, IEEE Control Systems.

[6]  Ulrich Hofmann,et al.  Fusion of occupancy grid mapping and model based object tracking for driver assistance systems using laser and radar sensors , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[7]  Trung-Dung Vu,et al.  Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments , 2007, 2007 IEEE Intelligent Vehicles Symposium.

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

[9]  Gaurav S. Sukhatme,et al.  Real-time Motion Tracking from a Mobile Robot , 2010, Int. J. Soc. Robotics.

[10]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[11]  Mohamed Essayed Bouzouraa.  Belegungskartenbasierte Umfeldwahrnehmung in Kombination mit objektbasierten Ansätzen für Fahrerassistenzsysteme , 2012 .

[12]  Sebastian Thrun,et al.  Model based vehicle detection and tracking for autonomous urban driving , 2009, Auton. Robots.

[13]  Jan Effertz.  Autonome Fahrzeugführung in urbaner Umgebung durch Kombination objekt- und kartenbasierter Umfeldmodelle , 2009 .

[14]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[15]  Christian Laugier,et al.  Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application , 2006, Int. J. Robotics Res..

[16]  Trung-Dung Vu,et al.  Grid-based localization and online mapping with moving objects detection and tracking: new results , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[17]  Thorsten Graf,et al.  Improved object tracking from detailed shape estimation using object local grid maps with stereo , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[18]  Véronique Berge-Cherfaoui,et al.  Moving Objects Detection by Conflict Analysis in Evidential Grids , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[19]  Ulrich Hofmann,et al.  360 Degree multi sensor fusion for static and dynamic obstacles , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[20]  K. Dietmayer,et al.  Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids , 2007, 2007 IEEE Intelligent Vehicles Symposium.