TY - INPR A1 - Kalyanasundaram, Abinav A1 - Chandra Sekaran, Karthikeyan A1 - Stäuber, Philipp A1 - Lange, Michael A1 - Utschick, Wolfgang A1 - Botsch, Michael T1 - Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation N2 - Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles. UR - https://doi.org/10.48550/arXiv.2504.06105 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2504.06105 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59578 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - de Candido, Oliver A1 - Koller, Michael A1 - Gallitz, Oliver A1 - Melz, Ron A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Towards feature validation in time to lane change classification using deep neural networks T2 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC45102.2020.9294555 KW - Feature extraction KW - Convolution KW - Computer architecture KW - Road transportation KW - Standards KW - Machine learning algorithms KW - Acceleration Y1 - 2020 UR - https://doi.org/10.1109/ITSC45102.2020.9294555 SN - 978-1-7281-4149-7 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Reichel, Michael A1 - Botsch, Michael A1 - Rauschecker, Robert A1 - Siedesberger, Karl-Heinz A1 - Maurer, Markus T1 - Situation aspect modelling and classification using the scenario based random forest algorithm for convoy merging situations T2 - 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems UR - https://doi.org/10.1109/ITSC.2010.5625213 KW - merging KW - training KW - road transportation KW - vehicles KW - vehicle dynamics KW - sensors KW - sections Y1 - 2010 UR - https://doi.org/10.1109/ITSC.2010.5625213 SN - 978-1-4244-7659-6 SN - 978-1-4244-7657-2 SN - 2153-0017 SN - 2153-0009 SP - 360 EP - 366 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Huber, Werner A1 - Balasubramanian, Lakshman A1 - Flores Fernandez, Alberto A1 - Geisler, Markus A1 - Gudera, Christian A1 - Morales Gomez, Mauricio Rene A1 - Riegl, Peter A1 - Sánchez Morales, Eduardo A1 - Chandra Sekaran, Karthikeyan A1 - Weinzierl, Michael T1 - Data Collection and Safety Use Cases in Smart Infrastructures T2 - 15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3581961.3609858 KW - smart infrastructure KW - infrared camera KW - LiDAR sensor KW - sensor data fusion KW - safety use case Y1 - 2023 UR - https://doi.org/10.1145/3581961.3609858 SN - 979-8-4007-0112-2 SP - 333 EP - 336 PB - ACM CY - New York ER - TY - JOUR A1 - Sánchez Morales, Eduardo A1 - Dauth, Julian A1 - Huber, Bertold A1 - García Higuera, Andrés A1 - Botsch, Michael T1 - High precision outdoor and indoor reference state estimation for testing autonomous vehicles JF - Sensors N2 - A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK. UR - https://doi.org/10.3390/s21041131 KW - machine learning KW - autonomous vehicles KW - Inertial Navigation System KW - Satellite Navigation KW - Real-Time Kinematic KW - indoor navigation KW - reference state Y1 - 2021 UR - https://doi.org/10.3390/s21041131 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10932 SN - 1424-8220 N1 - "This paper is an extended version of our paper published in : Sánchez Morales, E.; Botsch, M.; Huber, B.; García Higuera, A. High precision indoor positioning by means of LiDAR. In Proceedings of the 2019 DGON Inertial Sensors and Systems (ISS), Braunschweig, Germany, 10–11 September 2019." VL - 21 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Notomista, Gennaro A1 - Botsch, Michael T1 - A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking JF - Journal of Artificial Intelligence and Soft Computing Research (JAISCR) N2 - A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle–dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique- parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers. UR - https://doi.org/10.1515/jaiscr-2017-0017 KW - autonomous parking KW - ensemble learning KW - maneuver segmentation Y1 - 2017 UR - https://doi.org/10.1515/jaiscr-2017-0017 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9604 SN - 2449-6499 VL - 7 IS - 4 SP - 243 EP - 255 PB - De Gruyter Open CY - Warschau ER - TY - CHAP A1 - Dietl, Guido A1 - Botsch, Michael A1 - Dietrich, F. A. A1 - Utschick, Wolfgang T1 - Robust and reduced-rank matrix Wiener filter based on the conjugate gradient algorithm T2 - 2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications UR - https://doi.org/10.1109/SPAWC.2005.1506201 Y1 - 2005 UR - https://doi.org/10.1109/SPAWC.2005.1506201 SN - 0-7803-8867-4 SP - 555 EP - 559 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Wurst, Jonas A1 - Balasubramanian, Lakshman A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV51971.2022.9827187 KW - clustering KW - novelty detection KW - scenario-based testing KW - deep learning Y1 - 2022 UR - https://doi.org/10.1109/IV51971.2022.9827187 SN - 978-1-6654-8821-1 SP - 484 EP - 491 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics JF - Sensors N2 - A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments. UR - https://doi.org/10.3390/s23010159 KW - On-Board Diagnostics KW - Machine Learning KW - Transformer Neural Network KW - Autonomous Driving KW - ADAS KW - Inertial Navigation Systems Y1 - 2022 UR - https://doi.org/10.3390/s23010159 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30464 SN - 1424-8220 VL - 23 IS - 1 PB - MDPI CY - Basel ER - TY - CHAP A1 - Roßberg, Niklas A1 - Neumeier, Marion A1 - Hasirlioglu, Sinan A1 - Bouzouraa, Mohamed Essayed A1 - Botsch, Michael T1 - Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis T2 - 2025 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV64158.2025.11097767 Y1 - 2025 UR - https://doi.org/10.1109/IV64158.2025.11097767 SN - 979-8-3315-3803-3 SP - 1787 EP - 1794 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Elter, Tim A1 - Dirndorfer, Tobias A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922127 KW - Trajectory planning KW - Estimation KW - Collaboration KW - Markov process KW - Complexity theory KW - Reliability KW - Junctions Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922127 SN - 978-1-6654-6880-0 SP - 980 EP - 987 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Riegl, Peter A1 - Chandra Sekaran, Karthikeyan A1 - Botsch, Michael T1 - Generation of realistic traffic scenarios for virtual and real test drives based on a hybrid machine learning framework T2 - 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES61986.2024.10927934 Y1 - 2025 UR - https://doi.org/10.1109/ICVES61986.2024.10927934 SN - 979-8-3503-8727-8 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Neumeier, Marion A1 - Tollkühn, Andreas A1 - Dorn, Sebastian A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Gradient Derivation for Learnable Parameters in Graph Attention Networks UR - https://doi.org/10.48550/arXiv.2304.10939 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2304.10939 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Kruber, Friedrich A1 - Wurst, Jonas A1 - Botsch, Michael T1 - An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization UR - https://doi.org/10.48550/arXiv.2004.02121 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2004.02121 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Balasubramanian, Lakshman A1 - Wurst, Jonas A1 - Botsch, Michael A1 - Deng, Ke T1 - Traffic scenario clustering by iterative optimisation of self-supervised networks using a random forest activation pattern similarity T2 - 2021 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV48863.2021.9575615 Y1 - 2021 UR - https://doi.org/10.1109/IV48863.2021.9575615 SN - 978-1-7281-5394-0 SP - 682 EP - 689 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Balasubramanian, Lakshman A1 - Kruber, Friedrich A1 - Botsch, Michael A1 - Deng, Ke T1 - Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios T2 - 2021 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV48863.2021.9575433 Y1 - 2021 UR - https://doi.org/10.1109/IV48863.2021.9575433 SN - 978-1-7281-5394-0 SP - 674 EP - 681 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gallitz, Oliver A1 - de Candido, Oliver A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture T2 - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC48978.2021.9564555 Y1 - 2021 UR - https://doi.org/10.1109/ITSC48978.2021.9564555 SN - 978-1-7281-9142-3 SP - 493 EP - 499 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kruber, Friedrich A1 - Wurst, Jonas A1 - Botsch, Michael T1 - An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization T2 - 2018 IEEE Intelligent Transportation Systems Conference UR - https://doi.org/10.1109/ITSC.2018.8569682 Y1 - 2018 UR - https://doi.org/10.1109/ITSC.2018.8569682 SN - 978-1-7281-0323-5 SP - 2811 EP - 2818 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Kruber, Friedrich A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andres T1 - Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery T2 - 2020 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV47402.2020.9304705 Y1 - 2021 UR - https://doi.org/10.1109/IV47402.2020.9304705 SN - 978-1-7281-6673-5 SP - 2081 EP - 2088 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Chaulwar, Amit A1 - Al-Hashimi, Hussein A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Efficient hybrid machine learning algorithm for trajectory planning in critical traffic-scenarios T2 - The 4th International Conference on Intelligent Transportation Engineering, ICITE 2019 UR - https://doi.org/10.1109/ICITE.2019.8880266 KW - Trajectory Planning KW - Hybrid Machine Learning KW - Embedded Implementation Y1 - 2019 UR - https://doi.org/10.1109/ICITE.2019.8880266 SN - 978-1-7281-4553-2 SP - 196 EP - 202 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Huber, Bertold A1 - García Higuera, Andrés T1 - High precision indoor navigation for autonomous vehicles T2 - 2019 International Conference on Indoor Positioning and Indoor Navigation UR - https://doi.org/10.1109/IPIN.2019.8911780 KW - Sensors KW - Automobiles KW - State estimation KW - Autonomous vehicles KW - Testing KW - Receivers KW - Discrete Fourier transforms Y1 - 2019 UR - https://doi.org/10.1109/IPIN.2019.8911780 SN - 978-1-7281-1788-1 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gallitz, Oliver A1 - de Candido, Oliver A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Interpretable feature generation using deep neural networks and its application to lane change detection T2 - 2019 IEEE Intelligent Transportation Systems Conference (ITSC) UR - https://doi.org/10.1109/ITSC.2019.8917524 KW - Time series analysis KW - Kernel KW - Convolution KW - Feature extraction KW - Finite impulse response filters KW - Neural networks KW - Heating systems Y1 - 2019 UR - https://doi.org/10.1109/ITSC.2019.8917524 SN - 978-1-5386-7024-8 SP - 3405 EP - 3411 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Dietl, Guido A1 - Utschick, Wolfgang T1 - Iterative Multi-User Detection Using Reduced-Complexity Equalization T2 - TURBO – CODING – 2006: 4th International Symposium on Turbo Codes & Related Topics, 6th International ITG-Conference on Source and Channel Coding KW - Decoding Y1 - 2006 UR - https://www.vde-verlag.de/proceedings-de/442947088.html SN - 978-3-8007-2947-0 PB - VDE CY - Berlin ER - TY - CHAP A1 - Balasubramanian, Lakshman A1 - Wurst, Jonas A1 - Egolf, Robin A1 - Botsch, Michael A1 - Utschick, Wolfgang A1 - Deng, Ke T1 - ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922453 KW - Representation learning KW - Visualization KW - Codes KW - Self-supervised learning KW - Prediction methods KW - Distortion KW - Stability analysis Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922453 SN - 978-1-6654-6880-0 SP - 1471 EP - 1478 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Neumeier, Marion A1 - Tollkühn, Andreas A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922419 KW - Road transportation KW - Computational modeling KW - Computer architecture KW - Predictive models KW - Network architecture KW - Trajectory KW - Decoding Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922419 SN - 978-1-6654-6880-0 SP - 687 EP - 694 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Kruber, Friedrich A1 - Sánchez Morales, Eduardo A1 - Egolf, Robin A1 - Wurst, Jonas A1 - Chakraborty, Samarjit A1 - Botsch, Michael T1 - Micro- and Macroscopic Road Traffic Analysis using Drone Image Data JF - Leibniz Transactions on Embedded Systems N2 - The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis. In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro- and microscopic traffic analysis. UR - https://doi.org/10.4230/LITES.8.1.2 KW - traffic data analysis KW - trajectory data KW - drone image data Y1 - 2022 UR - https://doi.org/10.4230/LITES.8.1.2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29704 SN - 2199-2002 VL - 8 IS - 1 SP - 02:1 EP - 02:27 PB - Schloss Dagstuhl CY - Wadern ER - TY - BOOK A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Fahrzeugsicherheit und automatisiertes Fahren BT - Methoden der Signalverarbeitung und des maschinellen Lernens UR - https://doi.org/10.3139/9783446468047 Y1 - 2020 UR - https://doi.org/10.3139/9783446468047 SN - 978-3-446-46804-7 SN - 978-3-446-45326-5 PB - Hanser CY - München ER - TY - CHAP A1 - Botsch, Michael A1 - Nossek, Josef A. T1 - Feature Selection for Change Detection in Multivariate Time-Series T2 - 2007 IEEE Symposium on Computational Intelligence and Data Mining UR - https://doi.org/10.1109/CIDM.2007.368929 KW - machine learning KW - sequences KW - circuit theory KW - signal processing KW - radio frequency KW - signal processing algorithms KW - stochastic processes KW - feature extraction KW - computational intelligence KW - data mining Y1 - 2007 UR - https://doi.org/10.1109/CIDM.2007.368929 SN - 1-4244-0705-2 SP - 590 EP - 597 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Botsch, Michael A1 - Nossek, Josef A. T1 - Construction of interpretable Radial Basis Function classifiers based on the Random Forest kernel T2 - The 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008) UR - https://doi.org/10.1109/IJCNN.2008.4633793 KW - Radio frequency KW - Classification algorithms KW - Kernel KW - Support vector machine classification KW - Training KW - Classification tree analysis KW - Artificial neural networks Y1 - 2008 UR - https://doi.org/10.1109/IJCNN.2008.4633793 SN - 978-1-4244-1820-6 SN - 2161-4393 SN - 2161-4407 SP - 220 EP - 227 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Bergmiller, Peter A1 - Botsch, Michael A1 - Speth, Johannes A1 - Hofmann, Ulrich T1 - Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers T2 - 2008 IEEE Intelligent Vehicles Symposium UR - https://doi.org/10.1109/IVS.2008.4621273 KW - driver information systems KW - expert systems KW - image classification KW - radial basis function networks Y1 - 2008 UR - https://doi.org/10.1109/IVS.2008.4621273 SN - 978-1-4244-2568-6 SN - 1931-0587 SP - 226 EP - 233 PB - IEEE CY - Piscataway ER - TY - BOOK A1 - Botsch, Michael T1 - Machine Learning Techniques for Time Series Classification Y1 - 2009 UR - https://cuvillier.de/de/shop/publications/1092-machine-learning-techniques-for-time-series-classification SN - 978-3-86727-950-5 SN - 978-3-7369-2950-0 N1 - Dissertation, Technische Universität München, 2009 PB - Cuvillier Verlag CY - Göttingen ER - TY - CHAP A1 - Botsch, Michael A1 - Lauer, Christoph T1 - Complexity reduction using the Random Forest classifier in a collision detection algorithm T2 - 2010 IEEE Intelligent Vehicles Symposium UR - https://doi.org/10.1109/IVS.2010.5548044 Y1 - 2010 UR - https://doi.org/10.1109/IVS.2010.5548044 SN - 978-1-4244-7868-2 SN - 978-1-4244-7866-8 SN - 1931-0587 SP - 1228 EP - 1235 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Dirndorfer, Tobias A1 - Botsch, Michael A1 - Knoll, Alois T1 - Model-based analysis of sensor-noise in predictive passive safety algorithms T2 - The 22nd ESV Conference Proceedings Y1 - 2011 UR - https://www-esv.nhtsa.dot.gov/Proceedings/22/isv7/main.htm PB - NHTSA CY - Washington, D.C. ER - TY - CHAP A1 - Notomista, Gennaro A1 - Botsch, Michael T1 - Maneuver segmentation for autonomous parking based on ensemble learning T2 - 2015 International Joint Conference on Neural Networks (IJCNN) UR - https://doi.org/10.1109/IJCNN.2015.7280546 KW - vehicles KW - microcontrollers KW - simultaneous localization and mapping KW - Jacobian matrices KW - covariance matrices Y1 - 2015 UR - https://doi.org/10.1109/IJCNN.2015.7280546 SN - 978-1-4799-1960-4 SN - 2161-4407 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Herrmann, Stephan A1 - Utschick, Wolfgang A1 - Botsch, Michael A1 - Keck, Frank T1 - Supervised learning via optimal control labeling for criticality classification in vehicle active safety T2 - Proceedings: 2015 IEEE 18th International Conference on Intelligent Transportation Systems UR - https://doi.org/10.1109/ITSC.2015.328 KW - vehicles KW - acceleration KW - trajectory KW - collision avoidance KW - tires KW - force KW - optimal control Y1 - 2015 UR - https://doi.org/10.1109/ITSC.2015.328 SN - 978-1-4673-6596-3 SN - 2153-0017 SP - 2024 EP - 2031 PB - IEEE CY - Los Alamitos ER - TY - JOUR A1 - Notomista, Gennaro A1 - Selvaggio, Mario A1 - Sbrizzi, Fiorentina A1 - Di Maio, Gabriella A1 - Grazioso, Stanislao A1 - Botsch, Michael T1 - A fast airplane boarding strategy using online seat assignment based on passenger classification JF - Journal of Air Transport Management UR - https://doi.org/10.1016/j.jairtraman.2016.02.012 KW - Turnaround time KW - Boarding KW - Seat allocation algorithm KW - Agility coefficient KW - Cost saving KW - State feedback Y1 - 2016 UR - https://doi.org/10.1016/j.jairtraman.2016.02.012 SN - 0969-6997 VL - 2016 IS - 53 SP - 140 EP - 149 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Flores Fernández, Alberto A1 - Wurst, Jonas A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - Facchi, Christian A1 - García Higuera, Andrés T1 - Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction JF - Sensors N2 - The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source. UR - https://doi.org/10.3390/s22124498 KW - PROMOTING KW - automated driving systems KW - autonomous vehicles KW - multi-modal KW - motion prediction KW - route prediction KW - machine learning KW - real traffic data Y1 - 2022 UR - https://doi.org/10.3390/s22124498 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24341 SN - 1424-8220 VL - 22 IS - 12 PB - MDPI CY - Basel ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios T2 - 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) UR - https://doi.org/10.1109/ICMLA.2016.0095 KW - Hybrid learning algorithms KW - Trajectory Planning in Road Traffic KW - 3D-ConvNets Y1 - 2016 UR - https://doi.org/10.1109/ICMLA.2016.0095 SN - 978-1-5090-6167-9 SP - 540 EP - 546 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios T2 - 2017 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IVS.2017.7995735 KW - Acceleration KW - Trajectory KW - Heuristic algorithms KW - Vehicle dynamics KW - Planning KW - Prediction algorithms KW - Roads Y1 - 2017 UR - https://doi.org/10.1109/IVS.2017.7995735 SN - 978-1-5090-4804-5 SP - 297 EP - 303 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang ED - Klaffke, Werner T1 - A Simulation Framework for Vehicle Safety Testing T2 - Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplinärer Expertendialog (IEDAS) T2 - Ein Simulationsframework für die Absicherung von Fahrzeugsicherheitsfunktionen Y1 - 2017 SN - 978-3-8169-3405-9 SP - 147 EP - 167 PB - expert Verlag CY - Renningen ER - TY - JOUR A1 - Müller, Marcus A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies JF - Journal of Advances in Information Technology N2 - In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 103 times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken. UR - https://doi.org/10.12720/jait.9.1.15-24 KW - crash severity KW - vehicle safety KW - reliable prediction KW - machine learning Y1 - 2018 UR - https://doi.org/10.12720/jait.9.1.15-24 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-24644 SN - 1798-2340 VL - 9 (2018) IS - 1 SP - 15 EP - 24 PB - Engineering and Technology Publishing CY - Rowland Heights (CA) ER - TY - CHAP A1 - Cañas, Valentin A1 - Sánchez Morales, Eduardo A1 - Botsch, Michael A1 - García Higuera, Andres T1 - Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles T2 - 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES) UR - https://doi.org/10.1109/ICVES.2018.8519492 KW - Vehicle safety KW - wireless communication KW - autonomous driving KW - Intelligent Transport Systems Y1 - 2018 UR - https://doi.org/10.1109/ICVES.2018.8519492 SN - 978-1-5386-3543-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Chaulwar, Amit A1 - Botsch, Michael A1 - Utschick, Wolfgang ED - Kurková, Vera ED - Manolopoulos, Yannis ED - Hammer, Barbara ED - Iliadis, Lazaros ED - Maglogiannis, Ilias T1 - Generation of Reference Trajectories for Safe Trajectory Planning T2 - Artificial Neural Networks and Machine Learning – ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4–7, 2018 : Proceedings, Part I UR - https://doi.org/10.1007/978-3-030-01418-6_42 KW - Safe trajectory planning KW - Hybrid machine learning KW - Variational autoencoder Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-01418-6_42 SN - 978-3-030-01418-6 SN - 978-3-030-01417-9 SP - 423 EP - 434 PB - Springer CY - Cham ER - TY - CHAP A1 - Gallitz, Oliver A1 - Botsch, Michael A1 - de Candido, Oliver A1 - Utschick, Wolfgang T1 - Validation of Machine Learning Algorithms through Visualization Methods T2 - ELIV-MarketPlace 2018 UR - https://doi.org/10.51202/9783181023389-29 Y1 - 2018 UR - https://doi.org/10.51202/9783181023389-29 SN - 978-3-18-092338-3 SN - 978-3-18-102338-9 SP - 29 EP - 46 PB - VDI Verlag CY - Düsseldorf ER - TY - CHAP A1 - Müller, Marcus A1 - Long, Xing A1 - Botsch, Michael A1 - Böhmländer, Dennis A1 - Utschick, Wolfgang T1 - Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model T2 - 2018 IEEE Intelligent Transportation Systems Conference UR - https://doi.org/10.1109/ITSC.2018.8569471 KW - computer crashes KW - vehicle crash testing KW - machine learning KW - two dimensional displays KW - forestry KW - accidents KW - acceleration Y1 - 2018 UR - https://doi.org/10.1109/ITSC.2018.8569471 SN - 978-1-7281-0323-5 SP - 2036 EP - 2043 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Kruber, Friedrich A1 - Wurst, Jonas A1 - Botsch, Michael A1 - Chakraborty, Samarjit T1 - Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set UR - https://doi.org/10.48550/arXiv.1903.04249 KW - Time-To-Collision KW - Time-Headway KW - Risk Perception KW - traffic stream KW - traffic density KW - traffic flow rate KW - driver behavior KW - traffic simulation KW - highway traffic KW - highD Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1903.04249 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Neumeier, Marion A1 - Tollkühn, Andreas A1 - Dorn, Sebastian A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications T2 - IEEE IV 2023 Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186536 KW - Training KW - Representation learning KW - Adaptation models KW - Correlation KW - Intelligent vehicles KW - Automotive applications KW - Predictive models Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186536 SN - 979-8-3503-4691-6 SN - 2642-7214 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Chandra Sekaran, Karthikeyan A1 - Balasubramanian, Lakshman A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors T2 - IEEE IV 2023 Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186527 KW - Measurement KW - Head KW - Urban areas KW - Object detection KW - Thermal sensors KW - Cameras KW - Sensor systems Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186527 SN - 979-8-3503-4691-6 SN - 2642-7214 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Balasubramanian, Lakshman A1 - Wurst, Jonas A1 - Botsch, Michael A1 - Deng, Ke T1 - Open-World Learning for Traffic Scenarios Categorisation JF - IEEE Transactions on Intelligent Vehicles UR - https://doi.org/10.1109/TIV.2023.3260270 KW - self-supervised learning KW - open-world learning KW - traffic scenarios Y1 - 2023 UR - https://doi.org/10.1109/TIV.2023.3260270 SN - 2379-8904 VL - 8 IS - 5 SP - 3506 EP - 3521 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gallitz, Oliver A1 - de Candido, Oliver A1 - Botsch, Michael A1 - Melz, Ron A1 - Utschick, Wolfgang T1 - Interpretable Machine Learning Structure for an Early Prediction of Lane Changes T2 - Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I UR - https://doi.org/10.1007/978-3-030-61609-0_27 KW - Interpretability KW - Early prediction KW - Autonomous driving Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-61609-0_27 SN - 978-3-030-61609-0 SN - 978-3-030-61608-3 SP - 337 EP - 349 PB - Springer CY - Cham ER - TY - JOUR A1 - Chaulwar, Amit A1 - Al-Hashimi, Hussein A1 - Botsch, Michael A1 - Utschick, Wolfgang T1 - Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning JF - International Journal of Machine Learning and Computing N2 - The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources. UR - https://doi.org/10.18178/ijmlc.2021.11.1.1007 KW - Safe trajectory planning KW - hybrid machine learning KW - collision avoidance and mitigation Y1 - 2021 UR - https://doi.org/10.18178/ijmlc.2021.11.1.1007 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14099 SN - 2010-3700 VL - 11 IS - 1 PB - IACSIT Press CY - Singapur ER -