TY - CHAP A1 - Kress, Viktor A1 - Schreck, Steven A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks T2 - 2020 IEEE Symposium Series on Computational Intelligence (SSCI) N2 - This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right. KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug KW - Fußgänger KW - Fahrrad Y1 - 2020 SN - 978-1-7281-2548-0 U6 - https://doi.org/10.1109/SSCI47803.2020.9308462 SP - 2723 EP - 2730 PB - IEEE CY - Canberra, Australia ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Schreck, Steven A1 - Kress, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution T2 - 2020 25th International Conference on Pattern Recognition (ICPR) N2 - In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available. KW - Fahrerassistenzsystem KW - Radfahrer Y1 - 2020 UR - https://ieeexplore.ieee.org/document/9413233 ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Reichert, Hannes A1 - Kress, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - A Holistic View on Probabilistic Trajectory Forecasting – Case Study. Cyclist Intention Detection T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) KW - Autonomes Fahrzeug KW - Radfahrer Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1109/IV51971.2022.9827220 SP - 265 EP - 272 PB - IEEE ER - TY - JOUR A1 - Kress, Viktor A1 - Jeske, Fabian A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories JF - IEEE Transactions on Intelligent Vehicles KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug Y1 - 2023 U6 - https://doi.org/10.1109/TIV.2022.3149624 SN - 2379-8904 VL - 8 IS - 3 SP - 2592 EP - 2603 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Trupp, Oliver A1 - Kress, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information T2 - 2021 IEEE International Smart Cities Conference (ISC2) KW - Autonomes Fahrzeug KW - Radfahrer Y1 - 2021 U6 - https://doi.org/10.1109/ISC253183.2021.9562857 SP - 1 EP - 7 PB - IEEE ER -