TY - THES A1 - Kreß, Viktor T1 - Posenbasierte Intentionserkennung von ungeschützten Verkehrsteilnehmern aus einem Fahrzeug KW - Fahrerassistenzsystem KW - Fußgänger KW - Künstliche Intelligenz Y1 - 2022 ER - TY - JOUR A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Huber, Bertold ED - WILEY-VCH, Verlag T1 - Sicher unterwegs - Fortschritte beim aktiven Fußgängerschutz JF - messtec drives Automation KW - aktiver Fußgängerschutz, automatische Brems- und Ausweichmanöver KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2017 UR - https://www.wileyindustrynews.com/restricted-files/161702 VL - 25 IS - 03 SP - 82 EP - 82 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 - 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 - GEN A1 - Doll, Konrad A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Brunsmann, Ulrich T1 - Pedestrian Movement Modelling and Trajectory Prediction at Urban Intersections T2 - International IEEE Conference on Intelligent Transportation Systems (ITSC 2015) N2 - Vortrag KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2015 CY - Las Palmas de Gran Canaria, Spain 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 - 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 - TY - THES A1 - Goldhammer, Michael T1 - Selbstlernende Algorithmen zur videobasierten Absichtserkennung von Fußgängern KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2016 U6 - https://doi.org/10.19211/KUP9783737602532 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons T2 - Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015 N2 - We present an approach for predicting continuous pedestrian trajectories over a time horizon of 2.5 s by means of polynomial least squares approximation and multilayer perceptron (MLP) artificial neural networks. The training data are gathered from 1075 real urban traffic scenes with uninstructed pedestrians including starting, stopping, walking and bending in. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients. It is independent of sensor parameters such as cycle time and robust regarding noise. Approximation and prediction can be performed very efficiently. It only takes 35 ms on an Intel Core i7 CPU. Test results show 28% lower prediction errors for starting scenes and 32% for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well. KW - Fußgänger KW - Verkehrsverhalten KW - Prognose Y1 - 2016 SN - 978-3-319-33386-1 SP - 259 EP - 279 PB - Springer International Publishing ER - TY - CHAP A1 - Hahnle, Michael A1 - Saxen, Frerk A1 - Doll, Konrad T1 - Erkennung von Fußgängern in Echtzeit auf FPGAs T2 - 49. MPC-Workshop, Mannheim, Deutschland KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1868-9221 SP - 57 EP - 65 ER - TY - CHAP A1 - Hahnle, Michael A1 - Saxen, Frerk A1 - Hisung, Matthias A1 - Brunsmann, Ulrich A1 - Doll, Konrad T1 - FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images T2 - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 978-0-7695-4990-3 U6 - https://doi.org/10.1109/CVPRW.2013.95 SN - 2160-7508 SP - 629 EP - 635 PB - IEEE ER - TY - CHAP A1 - Goldhammer, Michael A1 - Gerhard, Matthias A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Brunsmann, Ulrich T1 - Early Prediction of a Pedestrian's Trajectory at Intersections T2 - 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 978-1-4799-2914-6 U6 - https://doi.org/10.1109/ITSC.2013.6728239 SP - 237 EP - 242 PB - IEEE ER - TY - JOUR A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Bauer, Sebastian A1 - Zecha, Stephan A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Dietmayer, Klaus T1 - Stationary Detection of the Pedestrian's Intention at Intersections JF - IEEE Intelligent Transportation Systems Magazine KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 1939-1390 VL - 2013 IS - 5 SP - 87 EP - 99 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Gensler, Andre A1 - Sick, Bernhard T1 - Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks T2 - 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 SN - 978-1-4799-5208-3 U6 - https://doi.org/10.1109/ICPR.2014.704 SN - 1051-4651 SP - 4110 EP - 4115 PB - IEEE ER - TY - CHAP A1 - Goldhammer, Michael A1 - Hubert, Andreas A1 - Köhler, Sebastian A1 - Zindler, Klaus A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Analysis on Termination of Pedestrians‘ Gait at Urban Intersections T2 - Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957947 SP - 1758 EP - 1763 PB - IEEE CY - Qingdao, China ER - TY - CHAP A1 - Hahn, Stefan A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Ein neues Regelungskonzept zur Funktionsprüfung vorausschauender Fahrzeugsicherheits- und Fußgängerschutzsysteme im automatisierten Fahrversuch T2 - 7. Tagung Fahrerassistenz KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2015 VL - 2015 ER - TY - RPRT A1 - Zindler, Klaus A1 - Hahn, Stefan A1 - Heinlein, Sven A1 - Geiß, Niklas T1 - Regelung von Testfahrzeugen und Testvorrichtungen zur standardisierten Funktionsüberprüfung vorausschauender Fahrzeugsicherheits- und Fußgängerschutzsysteme KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 ER - TY - JOUR A1 - Heinlein, Sven A1 - Hahn, Stefan A1 - Zindler, Klaus T1 - Control methods for automated testing of preventive pedestrian protection systems JF - International Journal of Vehicle Systems Modelling and Testing KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2015 VL - 10 IS - 2 SP - 127 EP - 147 PB - Inderscience Enterprises ER - TY - JOUR A1 - Blank, Andreas A1 - Hahn, Stefan A1 - Heinlein, Sven A1 - Zindler, Klaus T1 - Development of a test rig for the precise positioning of a pedestrian dummy in driving tests JF - International Journal of Vehicle Safety KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2015 VL - 2015 IS - 2 SP - 144 EP - 164 PB - Inderscience Enterprises ER - TY - JOUR A1 - Blank, Andreas A1 - Zindler, Klaus T1 - Vorsicht, Auto - Lineareinheiten bewegen Fußgängerattrappen JF - mda - messtec drives Automation KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2015 VL - 23 IS - 3 SP - 28 EP - 29 PB - Wiley-VCH Verlag ER - TY - JOUR A1 - Hahn, Stefan A1 - Heinlein, Sven A1 - Zindler, Klaus T1 - Testing Methods for Forward-looking Safety Systems JF - ATZ worldwide KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2012 VL - 114 (2012) IS - 11 SP - 4 EP - 7 PB - Springer Automotive Media ER - TY - JOUR A1 - Zindler, Klaus A1 - Blank, Andreas T1 - Fußgängerattrappen mit Lineareinheiten realistisch bewegen JF - Konstruktion - Sonderausgabe "Special Antriebstechnik" KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 VL - 2014 IS - S2 SP - 44 EP - 45 PB - Springer-VDI-Verlag ER - TY - JOUR A1 - Zindler, Klaus A1 - Blank, Andreas T1 - Fahrzeugsicherheitssysteme im Test: Fußgängerattrappen realistisch mit Lineareinheiten bewegen JF - Antriebstechnik KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 VL - 2014 IS - 9 SP - 34 EP - 36 PB - Vereinigte Fachverlage ER - TY - CHAP A1 - Hahn, Stefan A1 - Zindler, Klaus T1 - Steuerungs- und Regelungskonzepte zur automatisierten Funktionsprüfung vorausschauender Fahrzeugsicherheitssysteme T2 - Dresdner Automatisierungstechnisches Kolloquium KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 ER - TY - JOUR A1 - Blank, Andreas A1 - Zindler, Klaus T1 - Fahrzeugsicherheitssysteme im Test: Mit Lineareinheiten Fußgängerattrappen automatisch führen - Realistische Bewegungen JF - KEM - Konstruktion, Entwicklung, Management KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 VL - 2014 IS - 6 SP - 20 EP - 21 PB - Kohlhammer ER - TY - CHAP A1 - Bauer, Sebastian A1 - Brunsmann, Ulrich A1 - Schlotterbeck-Macht, Stefan T1 - FPGA implementation of a HOG-based pedestrian recognition system T2 - 42. MPC-Workshop, Karlsruhe KW - Field programmable gate array KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1862-7102 VL - 2009 IS - 42 SP - 49 EP - 58 ER - TY - CHAP A1 - Hahn, Stefan A1 - Heinlein, Sven A1 - Zindler, Klaus T1 - Regelung von Testfahrzeugen und Testvorrichtungen zur Funktionsprüfung vorausschauender Fahrzeugsicherheits- und Fußgängerschutzsysteme T2 - 28. VDI/VW-Gemeinschaftstagung Fahrerassistenz und Integrierte Sicherheit, Wolfsburg, VDI-Berichte Nr. 2166 KW - Fahrzeugsicherheitssysteme KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2012 SN - 978-3-18-092166-2 SP - 367 EP - 374 PB - VDI Verlag ER -