Latest revision as of 21:39, 3 February 2021

Abstract

Reasoning about the driver intent is fundamental both to advanced driver assistance systems as well as to highly automated driving. In contrast to the vast majority of preceding work, we investigate an architecture that can deal with arbitrary combinations of subsequent maneuvers as well as a varying set of available features. Detailed parametric models are given for the indicator, velocity and gaze direction features, all of which are parametrized from the results of extensive user studies. Evaluation is carried out for continuous right-turn prediction on a separate data set. Assuming conditional independence between the individual feature likelihoods, we investigate the contribution of each feature to the overall classification result separately. In particular, the approach is shown to work well even when faced with implausible observations of the indicator feature.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/itsc.2013.6728244

• http://www.mrt.kit.edu/z/publ/download/2013/LiebnerRuhhammerKlannerStiller2013itsc.pdf

• http://www.mrt.kit.edu/z/publ/download/2013/LiebnerRuhhammerKlannerStiller2013itsc.pdf,

https://dblp.uni-trier.de/db/conf/itsc/itsc2013.html#LiebnerRKS13,

http://dx.doi.org/10.1109/ITSC.2013.6728244,

https://dx.doi.org/10.1109/ITSC.2013.6728244,

https://ieeexplore.ieee.org/document/6728244,

https://www.researchgate.net/profile/Martin_Liebner/publication/260656722_Generic_driver_intent_inference_based_on_parametric_models/links/0f317531ee672ad118000000.pdf,

https://trid.trb.org/view/1352713,

https://academic.microsoft.com/#/detail/2053624032

• http://xplorestaging.ieee.org/ielx7/6712176/6728201/06728244.pdf?arnumber=6728244,

http://dx.doi.org/10.1109/itsc.2013.6728244