Accession Number : ADA635945


Title :   Probabilistic Tracking and Trajectory Planning for Autonomous Ground Vehicles in Urban Environments


Descriptive Note : Final rept. 26 Aug 2009-25 Aug 2014


Corporate Author : CORNELL UNIV ITHACA NY


Personal Author(s) : Campbell, Mark E


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a635945.pdf


Report Date : 05 Mar 2016


Pagination or Media Count : 12


Abstract : The aim of this research is to develop a unified theory for perception and planning in autonomous ground vehicles, with a specific focus on obstacle tracking/identification and trajectory planning, so as to enable reasoned, intelligent planning rather than simple reactive planning. This final report details our contributions. First, we developed a new method for anticipating obstacle motion using Gaussian Processes in order to enable contingency planning. Second, we developed three new mapping strategies including a unified terrain model based on a Markov Random Fields; soft relative maps; and fusion of stochastic maps. Third, we developed a methodology for capturing negative information (e.g. reasoning about areas where there is no sensor data), which is then used to improve tracking of obstacles. Fourth, we developed a new smoothing method which enables real time update of complex density functions (e.g. complex maps, tracking dynamic obstacles) in the presence of sparse data. We have published /accepted 23 journal articles and conference papers.


Descriptors :   *GROUND VEHICLES , *PROBABILITY , *ROBOTS , *TRAJECTORIES , COMPUTER VISION , LEAST SQUARES METHOD , MARKOV PROCESSES , MATHEMATICAL PREDICTION , MULTISENSORS , PLANNING , REGRESSION ANALYSIS , SELF OPERATION , STATISTICAL PROCESSES , TERRAIN MODELS , URBAN AREAS , VISUAL PERCEPTION


Subject Categories : Statistics and Probability
      Cybernetics
      Surface Transportation and Equipment


Distribution Statement : APPROVED FOR PUBLIC RELEASE