@inproceedings{KressZernetschDolletal.2020, author = {Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks}, series = {ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges}, booktitle = {ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-68763-2}, doi = {https://doi.org/10.1007/978-3-030-68763-2_5}, pages = {57 -- 71}, year = {2020}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressSchreckZernetschetal.2020, author = {Kress, Viktor and Schreck, Steven and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks}, series = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Canberra, Australia}, isbn = {978-1-7281-2548-0}, doi = {10.1109/SSCI47803.2020.9308462}, pages = {2723 -- 2730}, year = {2020}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{EilbrechtBieshaarZernetschetal.2017, author = {Eilbrecht, Jan and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Stursberg, Olaf}, title = {Model-predictive planning for autonomous vehicles anticipating intentions of vulnerable road users by artificial neural networks}, series = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Honolulu, HI, USA}, doi = {10.1109/SSCI.2017.8285249}, pages = {1 -- 8}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschKressSicketal.2018, author = {Zernetsch, Stefan and Kreß, Viktor and Sick, Bernhard and Doll, Konrad}, title = {Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network}, series = {2018 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2018 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Changshu, China}, doi = {10.1109/IVS.2018.8500428}, year = {2018}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressZernetschDolletal.2019, author = {Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users}, series = {IEEE Symposium Series on Computational Intelligence (SSCI)}, volume = {2019}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Xiamen, China}, doi = {10.1109/SSCI44817.2019.9003023}, pages = {1200 -- 1207}, year = {2019}, abstract = {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.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2018, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Human Pose Estimation in Real Traffic Scenes}, series = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Bangalore, India}, doi = {10.1109/SSCI.2018.8628660}, pages = {518 -- 523}, year = {2018}, subject = {Autonomes Fahrzeug}, language = {en} } @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{HahnleSaxenDoll2013, author = {Hahnle, Michael and Saxen, Frerk and Doll, Konrad}, title = {Erkennung von Fußg{\"a}ngern in Echtzeit auf FPGAs}, series = {49. MPC-Workshop, Mannheim, Deutschland}, booktitle = {49. MPC-Workshop, Mannheim, Deutschland}, issn = {1868-9221}, pages = {57 -- 65}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{HahnleSaxenHisungetal.2013, author = {Hahnle, Michael and Saxen, Frerk and Hisung, Matthias and Brunsmann, Ulrich and Doll, Konrad}, title = {FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images}, series = {2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA}, booktitle = {2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA}, publisher = {IEEE}, isbn = {978-0-7695-4990-3}, issn = {2160-7508}, doi = {10.1109/CVPRW.2013.95}, pages = {629 -- 635}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerGerhardZernetschetal.2013, author = {Goldhammer, Michael and Gerhard, Matthias and Zernetsch, Stefan and Doll, Konrad and Brunsmann, Ulrich}, title = {Early Prediction of a Pedestrian's Trajectory at Intersections}, series = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, booktitle = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, publisher = {IEEE}, isbn = {978-1-4799-2914-6}, doi = {10.1109/ITSC.2013.6728239}, pages = {237 -- 242}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} }