@inproceedings{GoldhammerKoehlerDolletal.2016, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Doll, Konrad and Sick, Bernhard}, title = {Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons}, series = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, booktitle = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, publisher = {Springer International Publishing}, isbn = {978-3-319-33386-1}, pages = {259 -- 279}, year = {2016}, abstract = {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.}, subject = {Fußg{\"a}nger}, language = {en} } @inproceedings{ZernetschKohnenGoldhammeretal.2016, author = {Zernetsch, Stefan and Kohnen, Sascha and Goldhammer, Michael and Doll, Konrad and Sick, Bernhard}, title = {Trajectory prediction of cyclists using a physical model and an artificial neural network}, series = {Intelligent Vehicles Symposium (IV), 2016}, booktitle = {Intelligent Vehicles Symposium (IV), 2016}, doi = {10.1109/IVS.2016.7535484}, pages = {833 -- 838}, year = {2016}, abstract = {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.}, subject = {Kalman-Filter}, language = {en} }