TY - CHAP A1 - Schneegans, Jan A1 - Eilbrecht, Jan A1 - Zernetsch, Stefan A1 - Bieshaar, Maarten A1 - Doll, Konrad A1 - Stursberg, Olaf A1 - Sick, Bernhard T1 - Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) KW - Fahrerassistenzsystem KW - Radfahrer KW - Autonomes Fahrzeug Y1 - 2021 U6 - https://doi.org/10.1109/IVWorkshops54471.2021.9669208 SP - 272 EP - 279 PB - 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 - TY - CHAP A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Riepe, Katharina A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Cyclist Motion State Forecasting - Going beyond Detection T2 - 2021 IEEE Symposium Series on Computational Intelligence (SSCI) KW - Autonomes Fahrzeug KW - Radfahrer Y1 - 2021 U6 - https://doi.org/10.1109/SSCI50451.2021.9660151 SP - 1 EP - 8 PB - IEEE 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 - 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 - Kreß, Viktor A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks T2 - ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges N2 - In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods. KW - 3D human trajectory forecast KW - Vulnerable road users KW - 3D body pose KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2020 SN - 978-3-030-68763-2 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-68763-2_5 SP - 57 EP - 71 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Hubert, Andreas A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Cyclists starting behavior at intersections T2 - 2017 IEEE Intelligent Vehicles Symposium (IV) KW - Fahrerassistenzsystem KW - Radfahrer Y1 - 2017 U6 - https://doi.org/10.1109/IVS.2017.7995856 SP - 1071 EP - 1077 PB - IEEE CY - Los Angeles, CA, USA ER - TY - CHAP A1 - Eilbrecht, Jan A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Stursberg, Olaf T1 - Model-predictive planning for autonomous vehicles anticipating intentions of vulnerable road users by artificial neural networks T2 - 2017 IEEE Symposium Series on Computational Intelligence (SSCI) KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer KW - Autonomes Fahrzeug Y1 - 2017 U6 - https://doi.org/10.1109/SSCI.2017.8285249 SP - 1 EP - 8 PB - IEEE CY - Honolulu, HI, USA ER - TY - CHAP A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Depping, Malte A1 - Sick, Bernhard A1 - Doll, Konrad T1 - Cooperative Starting Intention Detection of Cyclists based on Smart Devices and Infrastructure T2 - 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) KW - Fahrerassistenzsystem KW - Radfahrer Y1 - 2017 U6 - https://doi.org/10.1109/ITSC.2017.8317691 SP - 1 EP - 8 PB - IEEE CY - Yokohama, Japan ER - TY - JOUR A1 - Goldhammer, Michael A1 - Köhler, Sebastian A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Dietmayer, Klaus T1 - Intentions of Vulnerable Road Users – Detection and Forecasting by Means of Machine Learning JF - IEEE Transactions on Intelligent Transportation Systems KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2019 UR - https://doi.org/10.1109/TITS.2019.2923319 U6 - https://doi.org/10.1109/TITS.2019.2923319 VL - 2020 IS - 21 / 7 SP - 3035 EP - 3045 ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Kreß, Viktor A1 - Sick, Bernhard A1 - Doll, Konrad T1 - Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network T2 - 2018 IEEE Intelligent Vehicles Symposium (IV) N2 - In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7%. The ResNet approach achieved an F 1- score of 100% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time. KW - Fahrerassistenzsystem KW - Radfahrer KW - Fußgänger Y1 - 2018 U6 - https://doi.org/10.1109/IVS.2018.8500428 PB - IEEE CY - Changshu, China ER - TY - CHAP A1 - Kreß, Viktor A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Trajectory Forecast of Vulnerable Road Users T2 - IEEE Symposium Series on Computational Intelligence (SSCI) N2 - In this article, we investigate the use of 3D human poses for trajectory forecasting of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic. The forecast is based on past movements of the respective VRU and an important aspect in driver assistance systems and autonomous driving, which both could increase VRU safety. The 3D poses represent the entire body posture of the VRUs and can therefore provide important indicators for trajectory forecasting. In particular, we investigate the influence of different joint combinations and input sequence lengths of past movements on the accuracy of trajectory forecasts for pedestrians and cyclists. In addition, we divide VRU movements into the motion types wait, start, move, and stop and evaluate the results separately for each of them. Comparing it to an existing, solely head based trajectory forecast, we show the advantages of using 3D poses. With an input sequence length of 1.0 s, the forecasting error is reduced by 17.9 % for starting, 8.18 % for moving, and 11.0 % for stopping cyclists. For pedestrians, the error is reduced by 6.93 %, 2.73 %, and 5.02 %, respectively. With shorter input sequences, the improvements over the solely head based method remain for cyclists and even increase for pedestrians. KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2019 U6 - https://doi.org/10.1109/SSCI44817.2019.9003023 VL - 2019 SP - 1200 EP - 1207 PB - IEEE CY - Xiamen, China ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Human Pose Estimation in Real Traffic Scenes T2 - 2018 IEEE Symposium Series on Computational Intelligence (SSCI) KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2018 U6 - https://doi.org/10.1109/SSCI.2018.8628660 SP - 518 EP - 523 PB - IEEE CY - Bangalore, India ER -