Details

Autor(en) / Beteiligte

• Meyer-Delius, D.
• Sturm, J.
• Burgard, W.

Titel

Regression-based online situation recognition for vehicular traffic scenarios

Ist Teil von

• 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, p.1711-1716

Ort / Verlag

IEEE

Erscheinungsjahr

2009

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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.