A generic map based environment representation for driver assistance systems applied to detect convoy tracks

Future Advanced Driver Assistance Systems (ADAS) require generic map based representations to process environment information measured by different sensors. In contrast to today's widespread grid based maps, such environment representations have to be a compact, scalable and easy-to-interpret. In order to fulfill these requirements, we propose a new generic map representation, the two dimensional interval map. One example of information that can be represented in this data structure are convoy tracks. Convoy tracks describe the common motion of vehicles driving in convoys. These tracks can serve as an additional, independent input parameter for longitudinal and lateral control in highly automated ADAS. Experimental results show the general ability of the new data structure to provide sufficiently precise results at low computation time and memory consumption. The developed methodology to detect convoy tracks is capable of extracting multiple common convoy attributes even in complex scenarios with lane change maneuvers.

[1]  Klaus C. J. Dietmayer,et al.  Generic grid mapping for road course estimation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  Thierry Fraichard,et al.  Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

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

[4]  Myung Jin Chung,et al.  Stereo-vision based free space and obstacle detection with structural and traversability analysis using probabilistic volume polar grid map , 2011, 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM).

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

[6]  Markus Maurer,et al.  Situation aspect modelling and classification using the Scenario Based Random Forest algorithm for convoy merging situations , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[7]  Jürgen Dickmann,et al.  Dynamic level of detail 3D occupancy grids for automotive use , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[8]  Jizhong Xiao,et al.  Multi-volume occupancy grids: An efficient probabilistic 3D mapping model for micro aerial vehicles , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Evangeline Pollard,et al.  Convoy detection processing by using the hybrid algorithm (GMCPHD/VS-IMMC-MHT) and Dynamic Bayesian Networks , 2009, 2009 12th International Conference on Information Fusion.

[10]  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.

[11]  Wolfgang Koch,et al.  Road-map assisted convoy track maintenance using random matrices , 2008, 2008 11th International Conference on Information Fusion.

[12]  Kai Homeier,et al.  RoadGraph - Graph based environmental modelling and function independent situation analysis for driver assistance systems , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[13]  Oliver E. Drummond,et al.  A bibliography of cluster (group) tracking , 2004, SPIE Defense + Commercial Sensing.

[14]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization, Mapping and Moving Object Tracking , 2007, Int. J. Robotics Res..