@article{KoehlerGoldhammerBaueretal.2013, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Bauer, Sebastian and Zecha, Stephan and Doll, Konrad and Brunsmann, Ulrich and Dietmayer, Klaus}, title = {Stationary Detection of the Pedestrian's Intention at Intersections}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2013}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {5}, issn = {1939-1390}, pages = {87 -- 99}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @article{ZindlerDollHuber2017, author = {Zindler, Klaus and Doll, Konrad and Huber, Bertold}, title = {Sicher unterwegs - Fortschritte beim aktiven Fußg{\"a}ngerschutz}, series = {messtec drives Automation}, volume = {25}, journal = {messtec drives Automation}, number = {03}, editor = {WILEY-VCH, Verlag}, pages = {82 -- 82}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {de} } @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} } @article{GoldhammerKoehlerZernetschetal.2019, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Dietmayer, Klaus}, title = {Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning}, series = {IEEE Transactions on Intelligent Transportation Systems}, volume = {2020}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {21 / 7}, doi = {10.1109/TITS.2019.2923319}, pages = {3035 -- 3045}, year = {2019}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KressJeskeZernetschetal.2023, author = {Kress, Viktor and Jeske, Fabian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories}, series = {IEEE Transactions on Intelligent Vehicles}, volume = {8}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {3}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2379-8904}, doi = {10.1109/TIV.2022.3149624}, pages = {2592 -- 2603}, year = {2023}, subject = {Fahrerassistenzsystem}, language = {en} }