@inproceedings{GoldhammerHubertKoehleretal.2014, author = {Goldhammer, Michael and Hubert, Andreas and K{\"o}hler, Sebastian and Zindler, Klaus and Brunsmann, Ulrich and Doll, Konrad and Sick, Bernhard}, title = {Analysis on Termination of Pedestrians' Gait at Urban Intersections}, series = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, publisher = {IEEE}, address = {Qingdao, China}, doi = {10.1109/ITSC.2014.6957947}, pages = {1758 -- 1763}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {en} } @article{BieshaarZernetschHubertetal.2018, author = {Bieshaar, Maarten and Zernetsch, Stefan and Hubert, Andreas and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble}, series = {IEEE Transactions on Intelligent Vehicles}, volume = {3}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {4}, pages = {534 -- 544}, year = {2018}, abstract = {In the future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this paper, we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision-level cooperation. We introduce a novel method based on a three-dimensional convolutional neural network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{HubertZernetschDolletal.2017, author = {Hubert, Andreas and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Cyclists starting behavior at intersections}, series = {2017 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Los Angeles, CA, USA}, doi = {10.1109/IVS.2017.7995856}, pages = {1071 -- 1077}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} }