With the development of highly automated vehicles a situation recognition of the traffic environment is of considerable interest. In particular challenging traffic situations i.e. cut-in scenarios have to be detected early and reliably. Here an environment perception is presented as a base for behavior control, which offers a consistent environment sensing and a situation awareness regarding lane change maneuvers of the surrounding traffic. For this a probabilistic approach based on Bayesian networks is applied to predict lane change maneuvers considering human driving behavior and traffic rules as well as the measurable object and lane states. The approach is applied to the Temporary Autopilot in the EU funded project HAVEit.
A probabilistic lane change prediction module for highly automated driving
Eine probabilistisches Modell zur Vorhersage des Spurwechsels bei hoch automatisiertem Fahren
2010
6 Seiten, 9 Bilder, 11 Quellen
(nicht paginiert)
Aufsatz (Konferenz)
Datenträger
Englisch
A probabilistic lane change prediction module for highly automated driving
Kraftfahrwesen | 2010
|Probabilistic time-to-lane-change prediction on highways
IEEE | 2017
|Development and test of a lane change prediction algorithm for automated driving
Springer Verlag | 2017
|