Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Imitation learning with generalized task descriptions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic situation recognition for vehicular traffic scenarios
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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In this paper, we present an approach for learning generalized models for traffic situations. We formulate the problem using a dynamic Bayesian network (DBN) from which we learn the characteristic dynamics of a situation from labeled trajectories using kernel regression. For a new and unlabeled trajectory, we can then infer the corresponding situation by evaluating the data likelihood for the individual situation models. In experiments carried out on laser range data gathered on a car in real traffic and in simulation, we show that we can robustly recognize different traffic situations even from trajectories corresponding to partial situation instances.