@inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Start Intention Detection of Cyclists}, publisher = {IEEE}, address = {Auckland}, doi = {10.1109/ITSC.2019.8917215}, pages = {2381 -- 2386}, year = {2019}, abstract = {In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 \%. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 \% after 2.37 s, while the same score is reached after 0.668 s using poses.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Start Intention Detection of Cyclists using an LSTM Network}, series = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, isbn = {978-3-88579-689-3}, issn = {1617-5468}, doi = {10.18420/inf2019_ws25}, pages = {219 -- 228}, year = {2019}, abstract = {In this article, we present an approach for start intention detection of cyclists based on their head trajectories. Therefore, we are using a network architecture based on Long Short-Term Memory (LSTM) cells, which is able to handle input sequences of different lengths. This is important because, for example, due to occlusions, cyclists often only become visible to approaching vehicles shortly before dangerous situations occur. Hence, the dependency of the results on the input sequence length is investigated. We use a dataset with 206 situations where cyclists were transitioning from waiting to moving that was recorded from a moving vehicle in inner-city traffic.With an input sequence length of 1.0 s we achieve an F1-score of 96.2\% on average 0.680 s after the first movement of the bicycle. We obtain similar results for sequence lengths down to 0.2 s. For shorter sequences, the results regarding the F1-score and the mean detection time deteriorate considerably.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ReitbergerBieshaarZernetschetal.2018, author = {Reitberger, G{\"u}nther and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, series = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, booktitle = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, publisher = {IEEE}, address = {Maui, HI, USA}, doi = {10.1109/ITSC.2018.8569267}, year = {2018}, abstract = {In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.}, subject = {Fahrerassistenzsystem}, 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} } @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} } @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{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{ZernetschReichertKressetal.2019, author = {Zernetsch, Stefan and Reichert, Hannes and Kreß, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, series = {2019 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2019 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Paris, France}, doi = {10.1109/IVS.2019.8814258}, year = {2019}, abstract = {In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{BieshaarZernetschHubertetal.2018, author = {Bieshaar, Maarten and Zernetsch, Stefan and Hubert, Andreas and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble}, series = {IEEE Transactions on Intelligent Vehicles}, volume = {3}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {4}, pages = {534 -- 544}, year = {2018}, abstract = {In the future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this paper, we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision-level cooperation. We introduce a novel method based on a three-dimensional convolutional neural network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.}, 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} } @article{GoldhammerKoehlerZernetschetal.2019, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Dietmayer, Klaus}, title = {Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning}, series = {IEEE Transactions on Intelligent Transportation Systems}, volume = {2020}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {21 / 7}, doi = {10.1109/TITS.2019.2923319}, pages = {3035 -- 3045}, year = {2019}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarZernetschDeppingetal.2017, author = {Bieshaar, Maarten and Zernetsch, Stefan and Depping, Malte and Sick, Bernhard and Doll, Konrad}, title = {Cooperative Starting Intention Detection of Cyclists based on Smart Devices and Infrastructure}, series = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, booktitle = {2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Yokohama, Japan}, doi = {10.1109/ITSC.2017.8317691}, pages = {1 -- 8}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BieshaarReitbergerKressetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Fuchs, Erich and Sick, Bernhard}, title = {Highly Automated Learning for Improved Active Safety of Vulnerable Road Users}, series = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, volume = {2017}, booktitle = {ACM Chapters Computer Science in Cars Symposium (CSCS-17)}, address = {M{\"u}nchen}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {en} }