@unpublished{KalyanasundaramChandraSekaranStaeuberetal.2025, author = {Kalyanasundaram, Abinav and Chandra Sekaran, Karthikeyan and St{\"a}uber, Philipp and Lange, Michael and Utschick, Wolfgang and Botsch, Michael}, title = {Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2504.06105}, year = {2025}, abstract = {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.}, language = {en} } @inproceedings{deCandidoKollerGallitzetal.2020, author = {de Candido, Oliver and Koller, Michael and Gallitz, Oliver and Melz, Ron and Botsch, Michael and Utschick, Wolfgang}, title = {Towards feature validation in time to lane change classification using deep neural networks}, booktitle = {2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4149-7}, doi = {https://doi.org/10.1109/ITSC45102.2020.9294555}, year = {2020}, language = {en} } @inproceedings{BotschHuberBalasubramanianetal.2023, author = {Botsch, Michael and Huber, Werner and Balasubramanian, Lakshman and Flores Fernandez, Alberto and Geisler, Markus and Gudera, Christian and Morales Gomez, Mauricio Rene and Riegl, Peter and S{\´a}nchez Morales, Eduardo and Chandra Sekaran, Karthikeyan and Weinzierl, Michael}, title = {Data Collection and Safety Use Cases in Smart Infrastructures}, booktitle = {15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-0112-2}, doi = {https://doi.org/10.1145/3581961.3609858}, pages = {333 -- 336}, year = {2023}, language = {en} } @article{SanchezMoralesDauthHuberetal.2021, author = {S{\´a}nchez Morales, Eduardo and Dauth, Julian and Huber, Bertold and Garc{\´i}a Higuera, Andr{\´e}s and Botsch, Michael}, title = {High precision outdoor and indoor reference state estimation for testing autonomous vehicles}, volume = {21}, pages = {1131}, journal = {Sensors}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s21041131}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{WurstBalasubramanianBotschetal.2022, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8821-1}, doi = {https://doi.org/10.1109/IV51971.2022.9827187}, pages = {484 -- 491}, year = {2022}, language = {en} } @article{FloresFernandezSanchezMoralesBotschetal.2022, author = {Flores Fern{\´a}ndez, Alberto and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Facchi, Christian and Garc{\´i}a Higuera, Andr{\´e}s}, title = {Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics}, volume = {23}, pages = {159}, journal = {Sensors}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s23010159}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{RossbergNeumeierHasirliogluetal.2025, author = {Roßberg, Niklas and Neumeier, Marion and Hasirlioglu, Sinan and Bouzouraa, Mohamed Essayed and Botsch, Michael}, title = {Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-3803-3}, doi = {https://doi.org/10.1109/IV64158.2025.11097767}, pages = {1787 -- 1794}, year = {2025}, language = {en} } @inproceedings{ElterDirndorferBotschetal.2022, author = {Elter, Tim and Dirndorfer, Tobias and Botsch, Michael and Utschick, Wolfgang}, title = {Interaction-aware Prediction of Occupancy Regions based on a POMDP Framework}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922127}, pages = {980 -- 987}, year = {2022}, language = {en} } @inproceedings{RieglChandraSekaranBotsch2025, author = {Riegl, Peter and Chandra Sekaran, Karthikeyan and Botsch, Michael}, title = {Generation of realistic traffic scenarios for virtual and real test drives based on a hybrid machine learning framework}, booktitle = {2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-8727-8}, doi = {https://doi.org/10.1109/ICVES61986.2024.10927934}, year = {2025}, language = {en} } @unpublished{NeumeierTollkuehnDornetal.2023, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Gradient Derivation for Learnable Parameters in Graph Attention Networks}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2304.10939}, year = {2023}, language = {en} } @unpublished{KruberWurstBotsch2020, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael}, title = {An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2004.02121}, year = {2020}, language = {en} } @inproceedings{BalasubramanianWurstBotschetal.2021, author = {Balasubramanian, Lakshman and Wurst, Jonas and Botsch, Michael and Deng, Ke}, title = {Traffic scenario clustering by iterative optimisation of self-supervised networks using a random forest activation pattern similarity}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9575615}, pages = {682 -- 689}, year = {2021}, language = {en} } @inproceedings{BalasubramanianKruberBotschetal.2021, author = {Balasubramanian, Lakshman and Kruber, Friedrich and Botsch, Michael and Deng, Ke}, title = {Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9575433}, pages = {674 -- 681}, year = {2021}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2021, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Utschick, Wolfgang}, title = {Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture}, booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9142-3}, doi = {https://doi.org/10.1109/ITSC48978.2021.9564555}, pages = {493 -- 499}, year = {2021}, language = {en} } @inproceedings{SanchezMoralesKruberBotschetal.2021, author = {S{\´a}nchez Morales, Eduardo and Kruber, Friedrich and Botsch, Michael and Huber, Bertold and Garc{\´i}a Higuera, Andres}, title = {Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304705}, pages = {2081 -- 2088}, year = {2021}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2019, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Utschick, Wolfgang}, title = {Interpretable feature generation using deep neural networks and its application to lane change detection}, booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-7024-8}, doi = {https://doi.org/10.1109/ITSC.2019.8917524}, pages = {3405 -- 3411}, year = {2019}, language = {en} } @inproceedings{BalasubramanianWurstEgolfetal.2022, author = {Balasubramanian, Lakshman and Wurst, Jonas and Egolf, Robin and Botsch, Michael and Utschick, Wolfgang and Deng, Ke}, title = {ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922453}, pages = {1471 -- 1478}, year = {2022}, language = {en} } @inproceedings{NeumeierTollkuehnBotschetal.2022, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Botsch, Michael and Utschick, Wolfgang}, title = {A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922419}, pages = {687 -- 694}, year = {2022}, language = {en} } @article{KruberSanchezMoralesEgolfetal.2022, author = {Kruber, Friedrich and S{\´a}nchez Morales, Eduardo and Egolf, Robin and Wurst, Jonas and Chakraborty, Samarjit and Botsch, Michael}, title = {Micro- and Macroscopic Road Traffic Analysis using Drone Image Data}, volume = {8}, journal = {Leibniz Transactions on Embedded Systems}, number = {1}, publisher = {Schloss Dagstuhl}, address = {Wadern}, issn = {2199-2002}, doi = {https://doi.org/10.4230/LITES.8.1.2}, pages = {02:1 -- 02:27}, year = {2022}, abstract = {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.}, language = {en} } @book{BotschUtschick2020, author = {Botsch, Michael and Utschick, Wolfgang}, title = {Fahrzeugsicherheit und automatisiertes Fahren}, subtitle = {Methoden der Signalverarbeitung und des maschinellen Lernens}, publisher = {Hanser}, address = {M{\"u}nchen}, isbn = {978-3-446-46804-7}, doi = {https://doi.org/10.3139/9783446468047}, pages = {448}, year = {2020}, language = {de} } @article{FloresFernandezWurstSanchezMoralesetal.2022, author = {Flores Fern{\´a}ndez, Alberto and Wurst, Jonas and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Facchi, Christian and Garc{\´i}a Higuera, Andr{\´e}s}, title = {Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction}, volume = {22}, pages = {4498}, journal = {Sensors}, number = {12}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s22124498}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{NeumeierTollkuehnDornetal.2023, author = {Neumeier, Marion and Tollk{\"u}hn, Andreas and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications}, booktitle = {IEEE IV 2023 Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, issn = {2642-7214}, doi = {https://doi.org/10.1109/IV55152.2023.10186536}, year = {2023}, language = {en} } @inproceedings{ChandraSekaranBalasubramanianBotschetal.2023, author = {Chandra Sekaran, Karthikeyan and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors}, booktitle = {IEEE IV 2023 Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, issn = {2642-7214}, doi = {https://doi.org/10.1109/IV55152.2023.10186527}, year = {2023}, language = {en} } @article{BalasubramanianWurstBotschetal.2023, author = {Balasubramanian, Lakshman and Wurst, Jonas and Botsch, Michael and Deng, Ke}, title = {Open-World Learning for Traffic Scenarios Categorisation}, volume = {8}, journal = {IEEE Transactions on Intelligent Vehicles}, number = {5}, publisher = {IEEE}, address = {Piscataway}, issn = {2379-8904}, doi = {https://doi.org/10.1109/TIV.2023.3260270}, pages = {3506 -- 3521}, year = {2023}, language = {en} } @inproceedings{GallitzdeCandidoBotschetal.2020, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Melz, Ron and Utschick, Wolfgang}, title = {Interpretable Machine Learning Structure for an Early Prediction of Lane Changes}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-61609-0}, doi = {https://doi.org/10.1007/978-3-030-61609-0_27}, pages = {337 -- 349}, year = {2020}, language = {en} } @article{ChaulwarAlHashimiBotschetal.2021, author = {Chaulwar, Amit and Al-Hashimi, Hussein and Botsch, Michael and Utschick, Wolfgang}, title = {Sampling Algorithms Combination with Machine Learning for Effcient Safe Trajectory Planning}, volume = {11}, journal = {International Journal of Machine Learning and Computing}, number = {1}, publisher = {IACSIT Press}, address = {Singapur}, issn = {2010-3700}, doi = {https://doi.org/10.18178/ijmlc.2021.11.1.1007}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{NeumeierBotschTollkuehnetal.2021, author = {Neumeier, Marion and Botsch, Michael and Tollk{\"u}hn, Andreas and Berberich, Thomas}, title = {Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space}, booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9142-3}, doi = {https://doi.org/10.1109/ITSC48978.2021.9565120}, pages = {820 -- 827}, year = {2021}, language = {en} } @inbook{KruberWurstBotschetal.2023, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael and Chakraborty, Samarjit}, title = {Unsupervised Random Forest Learning for Traffic Scenario Categorization}, booktitle = {Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems}, editor = {Kukkala, Vipin Kumar and Pasricha, Sudeep}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-28016-0}, doi = {https://doi.org/10.1007/978-3-031-28016-0_20}, pages = {565 -- 590}, year = {2023}, language = {en} } @inproceedings{BalasubramanianWurstEgolfetal.2024, author = {Balasubramanian, Lakshman and Wurst, Jonas and Egolf, Robin and Botsch, Michael and Utschick, Wolfgang and Deng, Ke}, title = {SceneDiffusion: Conditioned Latent Diffusion Models for Traffic Scene Prediction}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422482}, pages = {3914 -- 3921}, year = {2024}, language = {en} } @unpublished{NeumeierDornBotschetal.2024, author = {Neumeier, Marion and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2405.14384}, year = {2024}, language = {en} } @inproceedings{FertigBalasubramanianBotsch2024, author = {Fertig, Alexander and Balasubramanian, Lakshman and Botsch, Michael}, title = {Clustering and Anomaly Detection in Embedding Spaces for the Validation of Automotive Sensors}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588817}, pages = {1076 -- 1083}, year = {2024}, language = {en} } @inproceedings{ChandraSekaranBalasubramanianBotschetal.2024, author = {Chandra Sekaran, Karthikeyan and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588872}, pages = {1643 -- 1650}, year = {2024}, language = {en} } @inproceedings{NeumeierDornBotschetal.2024, author = {Neumeier, Marion and Dorn, Sebastian and Botsch, Michael and Utschick, Wolfgang}, title = {Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422530}, pages = {1172 -- 1179}, year = {2024}, language = {en} } @unpublished{ChandraSekaranGeislerRoessleetal.2025, author = {Chandra Sekaran, Karthikeyan and Geisler, Markus and R{\"o}ßle, Dominik and Mohan, Adithya and Cremers, Daniel and Utschick, Wolfgang and Botsch, Michael and Huber, Werner and Sch{\"o}n, Torsten}, title = {UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2510.23478}, year = {2025}, language = {en} } @inproceedings{FertigBalasubramanianBotsch2025, author = {Fertig, Alexander and Balasubramanian, Lakshman and Botsch, Michael}, title = {Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-3803-3}, doi = {https://doi.org/10.1109/IV64158.2025.11097759}, pages = {38 -- 44}, year = {2025}, language = {en} } @unpublished{FertigBalasubramanianBotsch2025, author = {Fertig, Alexander and Balasubramanian, Lakshman and Botsch, Michael}, title = {Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2501.03666}, year = {2025}, language = {en} } @inproceedings{ElterDirndorferBotschetal.2025, author = {Elter, Tim and Dirndorfer, Tobias and Botsch, Michael and Utschick, Wolfgang}, title = {Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-3803-3}, doi = {https://doi.org/10.1109/IV64158.2025.11097498}, pages = {663 -- 670}, year = {2025}, language = {en} } @inproceedings{KruberSanchezMoralesChakrabortyetal.2021, author = {Kruber, Friedrich and S{\´a}nchez Morales, Eduardo and Chakraborty, Samarjit and Botsch, Michael}, title = {Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304794}, pages = {2089 -- 2096}, year = {2021}, language = {en} } @unpublished{KruberSanchezMoralesChakrabortyetal.2020, author = {Kruber, Friedrich and S{\´a}nchez Morales, Eduardo and Chakraborty, Samarjit and Botsch, Michael}, title = {Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2004.08206}, year = {2020}, language = {en} } @inproceedings{NadarajanBotschSardina2024, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Continuous Probabilistic Motion Prediction based on Latent Space Interpolation}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422685}, pages = {3796 -- 3803}, year = {2024}, language = {en} } @inproceedings{WurstFloresFernandezBotschetal.2021, author = {Wurst, Jonas and Flores Fernandez, Alberto and Botsch, Michael and Utschick, Wolfgang}, title = {An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304733}, pages = {1436 -- 1443}, year = {2021}, language = {en} } @unpublished{WurstFloresFernandezBotschetal.2020, author = {Wurst, Jonas and Flores Fernandez, Alberto and Botsch, Michael and Utschick, Wolfgang}, title = {An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.13288}, year = {2020}, language = {en} } @inproceedings{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9575730}, pages = {1304 -- 1311}, year = {2021}, language = {en} } @unpublished{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2105.01924}, year = {2021}, language = {en} }