@inproceedings{BieshaarReitbergerZernetschetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Zernetsch, Stefan and Sick, Bernhard and Fuchs, Erich and Doll, Konrad}, title = {Detecting intentions of vulnerable road users based on collective intelligence}, series = {AAET - Automatisiertes und vernetztes Fahren}, booktitle = {AAET - Automatisiertes und vernetztes Fahren}, pages = {67 -- 87}, year = {2017}, abstract = {Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is important to detect VRUs and to predict their intentions. In this article a holistic approach for detecting intentions of VRUs by cooperative methods is presented. The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory. Vehicles equipped with sensors, data processing systems and communication abilities, referred to as intelligent vehicles, acquire and maintain a local model of their surrounding traffic environment, e.g. crossing cyclists. Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e.g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties. The resulting model allows to extend the perceptual horizon of the individual agent beyond their own sensory capabilities, enabling a longer forecast horizon. Concealments, implausibilities and inconsistencies are resolved by the collective intelligence of cooperating agents. Novel techniques of signal processing and modelling in combination with analytical and learning based approaches of pattern and activity recognition are used for detection, as well as intention prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge fusion, takes place on the level of perception and intention recognition. Based on the requirements of the cooperative approach for the communication a new strategy for an ad hoc network is proposed. The evaluation is done using real data gathered with a research vehicle, a research intersection with public traffic and mobile devices.}, subject = {Verkehrsverhalten}, language = {en} } @inproceedings{ReitbergerBieshaarZernetschetal.2018, author = {Reitberger, G{\"u}nther and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, series = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, booktitle = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, publisher = {IEEE}, address = {Maui, HI, USA}, doi = {10.1109/ITSC.2018.8569267}, year = {2018}, abstract = {In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.}, 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{EilbrechtBieshaarZernetschetal.2017, author = {Eilbrecht, Jan and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Stursberg, Olaf}, title = {Model-predictive planning for autonomous vehicles anticipating intentions of vulnerable road users by artificial neural networks}, series = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Honolulu, HI, USA}, doi = {10.1109/SSCI.2017.8285249}, pages = {1 -- 8}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarZernetschDeppingetal.2017, author = {Bieshaar, Maarten and Zernetsch, Stefan and Depping, Malte and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Intention Detection of Cyclists based on Smart Devices and Infrastructure}, series = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, booktitle = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Yokohama, Japan}, doi = {10.1109/ITSC.2017.8317691}, pages = {1 -- 8}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarReitbergerKressetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Fuchs, Erich and Sick, Bernhard}, title = {Highly Automated Learning for Improved Active Safety of Vulnerable Road Users}, series = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, volume = {2017}, booktitle = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, address = {M{\"u}nchen}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarZernetschRiepeetal.2021, author = {Bieshaar, Maarten and Zernetsch, Stefan and Riepe, Katharina and Doll, Konrad and Sick, Bernhard}, title = {Cyclist Motion State Forecasting - Going beyond Detection}, series = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, doi = {10.1109/SSCI50451.2021.9660151}, pages = {1 -- 8}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{SchneegansEilbrechtZernetschetal.2021, author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard}, title = {Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, doi = {10.1109/IVWorkshops54471.2021.9669208}, pages = {272 -- 279}, year = {2021}, subject = {Fahrerassistenzsystem}, language = {en} }