TY - CHAP A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Bauer, Sebastian A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Dietmayer, Klaus T1 - Early Detection of the Pedestrian’s Intention to Cross the Street T2 - 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA. KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 UR - http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Koehler12-EDO.pdf U6 - https://doi.org/10.1109/ITSC.2012.6338797 SP - 1759 EP - 1764 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 - Goldhammer, Michael A1 - Brunsmann, Ulrich A1 - Doll, Konrad T1 - Verkehrssicherheitsforschung: Bildverabeitung an intelligenten Kreuzungen T2 - Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November KW - Bildverarbeitung KW - Verkehrssicherheit Y1 - 2012 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Strigel, Elias A1 - Meissner, Daniel A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Dietmayer, Klaus T1 - Cooperative Multi Sensor Network for Traffic Safety Applications at Intersections T2 - 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA. KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 U6 - https://doi.org/10.1109/ITSC.2012.6338672 SP - 1178 EP - 1183 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 - 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 - 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 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Dietmayer, Klaus T1 - Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle T2 - Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on N2 - We present a method to detect starting, stopping and bending in intentions of pedestrians from a moving vehicle based on stereo-vision. The method focuses on urban scenarios where these pedestrian movements are common and may result in critical situations. Pedestrian intentions are determined by means of an image-based motion contour histogram of oriented gradient descriptor. It is based on silhouettes gathered from stereo data and does not require any compensation of appearance changes resulting from the ego-motion of a vehicle. Nevertheless, it covers small movements indicating a pedestrian’s intention. A linear support vector machine with probabilistic estimates is used for classification. We evaluated our method on the publicly available Daimler Pedestrian Path Prediction Benchmark Dataset containing detections of a stateof-the-art pedestrian detector. We detect a pedestrian’s stopping intention from 125 ms to 500 ms before standing still within an accuracy range of 80% to 100%. Bending in is detected from 320 ms to 570 ms after a first visible lateral body movement in the same accuracy range. The intention to cross the road from standing still (starting) is detected 250 ms after the first visible motion and, therefore, within the first step with an accuracy of 100%. KW - Image Motion Analysis KW - Stereo Image Processing KW - Pedestrian Safety KW - Bildfolgenanalyse Y1 - 2015 U6 - https://doi.org/10.1109/ITSC.2015.374 SP - 2317 EP - 2322 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Camera Based Pedestrian Path Prediction by Means of Polynomial Least-squares Approximation and Multilayer Perceptron Neural Networks T2 - SAI Intelligent Systems Conference (IntelliSys), 2015 N2 - This paper provides a method to forecast pedestrian trajectories by means of polynomial least-squares approximation and multilayer perceptron artificial neural networks for traffic safety applications. The approach uses camera based head tracking as input data to predict a continuous trajectory for a 2.5 s future time horizon. Training and test is performed using 1075 recorded tracks of uninstructed pedestrians in common public traffic situations, including many challenging scenarios like starting, stopping and bending in. The neural network approach has the ability to handle these scenes by learning a single implicit movement model independent of a specific motion type. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients, high independence of input data, e.g., sample rate, and additional noise resistance. Our test results show 24% lower prediction errors for starting scenes and 29% for stopping scenes in comparison to a constant velocity Kalman filter. Approaches using MLP without polynomial input and the usage of Support Vector KW - Artificial Neural Networks KW - Polynomial Approximation KW - Path Prediction KW - Intelligent Vehicles KW - Neuronales Netz KW - Polynomapproximation Y1 - 2015 U6 - https://doi.org/10.1109/IntelliSys.2015.7361171 SP - 390 EP - 399 PB - IEEE ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Kohnen, Sascha A1 - Goldhammer, Michael A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Trajectory prediction of cyclists using a physical model and an artificial neural network T2 - Intelligent Vehicles Symposium (IV), 2016 N2 - This article presents two methods for predicting the trajectories of cyclists at an intersection and compares them to a Kalman Filter (KF) approach. The first method uses a physical model of cyclists to predict their future position. The second method is based on a polynomial least-squares approximation in combination with a multilayer perceptron artificial neural network and is able to predict the future position of cyclists independent of their motion type such as “Starting”, “Stopping”, “Waiting” or “Passing”. To evaluate the performance of the methods, 566 tracks (394 for training, 172 for testing) of uninstructed cyclists were recorded at a public intersection using a wide angle stereo camera system and laser scanners. Using the tracks as input data, the future trajectory was predicted for a time horizon of 2.5 s. For starting motions the prediction using the physical model leads to 27% more accurate positions than the KF approach for a forecast horizon of 2.5 s. The neural network shows a 34% more accurate result for starting and stopping motions and a similar result for waiting and passing motions. KW - Kalman-Filter KW - Prognose Y1 - 2016 U6 - https://doi.org/10.1109/IVS.2016.7535484 SP - 833 EP - 838 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 - 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 -