Regression-based online situation recognition for vehicular traffic scenarios

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.