Probabilistic Tracking and Trajectory Planning for Autonomous Ground Vehicles in Urban Environments
Final rept. 26 Aug 2009-25 Aug 2014
CORNELL UNIV ITHACA NY
Pagination or Media Count:
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 trackingidentification 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.
- Statistics and Probability
- Surface Transportation and Equipment