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 -