@inproceedings{HerrmannUtschickBotschetal.2015, author = {Herrmann, Stephan and Utschick, Wolfgang and Botsch, Michael and Keck, Frank}, title = {Supervised learning via optimal control labeling for criticality classification in vehicle active safety}, booktitle = {Proceedings: 2015 IEEE 18th International Conference on Intelligent Transportation Systems}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-4673-6596-3}, issn = {2153-0017}, doi = {https://doi.org/10.1109/ITSC.2015.328}, pages = {2024 -- 2031}, year = {2015}, language = {en} } @article{NotomistaSelvaggioSbrizzietal.2016, author = {Notomista, Gennaro and Selvaggio, Mario and Sbrizzi, Fiorentina and Di Maio, Gabriella and Grazioso, Stanislao and Botsch, Michael}, title = {A fast airplane boarding strategy using online seat assignment based on passenger classification}, volume = {2016}, journal = {Journal of Air Transport Management}, number = {53}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0969-6997}, doi = {https://doi.org/10.1016/j.jairtraman.2016.02.012}, pages = {140 -- 149}, year = {2016}, language = {en} } @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{ChaulwarBotschUtschick2016, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios}, booktitle = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5090-6167-9}, doi = {https://doi.org/10.1109/ICMLA.2016.0095}, pages = {540 -- 546}, year = {2016}, language = {en} } @inproceedings{ChaulwarBotschUtschick2017, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios}, booktitle = {2017 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-4804-5}, doi = {https://doi.org/10.1109/IVS.2017.7995735}, pages = {297 -- 303}, year = {2017}, language = {en} } @inproceedings{MuellerBotschBoehmlaenderetal.2017, author = {M{\"u}ller, Marcus and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {A Simulation Framework for Vehicle Safety Testing}, booktitle = {Aktive Sicherheit und Automatisieres Fahren : 3. Interdisziplin{\"a}rer Expertendialog (IEDAS)}, editor = {Klaffke, Werner}, publisher = {expert Verlag}, address = {Renningen}, isbn = {978-3-8169-3405-9}, pages = {147 -- 167}, year = {2017}, language = {en} } @article{MuellerBotschBoehmlaenderetal.2018, author = {M{\"u}ller, Marcus and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies}, volume = {9 (2018)}, journal = {Journal of Advances in Information Technology}, number = {1}, publisher = {Engineering and Technology Publishing}, address = {Rowland Heights (CA)}, issn = {1798-2340}, doi = {https://doi.org/10.12720/jait.9.1.15-24}, pages = {15 -- 24}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{CanasSanchezMoralesBotschetal.2018, author = {Ca{\~n}as, Valentin and S{\´a}nchez Morales, Eduardo and Botsch, Michael and Garc{\´i}a Higuera, Andres}, title = {Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles}, booktitle = {2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-3543-8}, doi = {https://doi.org/10.1109/ICVES.2018.8519492}, year = {2018}, language = {en} } @inproceedings{ChaulwarBotschUtschick2018, author = {Chaulwar, Amit and Botsch, Michael and Utschick, Wolfgang}, title = {Generation of Reference Trajectories for Safe Trajectory Planning}, booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2018 : 27th International Conference on Artificial Neural Networks,Rhodes, Greece, October 4-7, 2018 : Proceedings, Part I}, editor = {Kurkov{\´a}, Vera and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-01418-6}, doi = {https://doi.org/10.1007/978-3-030-01418-6_42}, pages = {423 -- 434}, year = {2018}, language = {en} } @inproceedings{GallitzBotschdeCandidoetal.2018, author = {Gallitz, Oliver and Botsch, Michael and de Candido, Oliver and Utschick, Wolfgang}, title = {Validation of Machine Learning Algorithms through Visualization Methods}, booktitle = {ELIV-MarketPlace 2018}, publisher = {VDI Verlag}, address = {D{\"u}sseldorf}, isbn = {978-3-18-092338-3}, doi = {https://doi.org/10.51202/9783181023389-29}, pages = {29 -- 46}, year = {2018}, language = {en} } @inproceedings{MuellerLongBotschetal.2018, author = {M{\"u}ller, Marcus and Long, Xing and Botsch, Michael and B{\"o}hml{\"a}nder, Dennis and Utschick, Wolfgang}, title = {Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model}, booktitle = {2018 IEEE Intelligent Transportation Systems Conference}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-0323-5}, doi = {https://doi.org/10.1109/ITSC.2018.8569471}, pages = {2036 -- 2043}, year = {2018}, language = {en} } @unpublished{KruberWurstBotschetal.2019, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael and Chakraborty, Samarjit}, title = {Highway traffic data: macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1903.04249}, year = {2019}, 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{KruberWurstSanchezMoralesetal.2019, author = {Kruber, Friedrich and Wurst, Jonas and S{\´a}nchez Morales, Eduardo and Chakraborty, Samarjit and Botsch, Michael}, title = {Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification}, booktitle = {2019 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-0560-4}, doi = {https://doi.org/10.1109/IVS.2019.8813994}, pages = {2463 -- 2470}, year = {2019}, 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{NadarajanBotschSardina2017, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm}, booktitle = {2017 International Joint Conference on Neural Networks (IJCNN)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-6182-2}, issn = {2161-4407}, doi = {https://doi.org/10.1109/IJCNN.2017.7965995}, pages = {1244 -- 1251}, year = {2017}, language = {en} } @unpublished{NadarajanBotschSardina2017, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2512.12901}, year = {2017}, language = {en} } @unpublished{NadarajanBotsch2016, author = {Nadarajan, Parthasarathy and Botsch, Michael}, title = {Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2512.12896}, year = {2016}, language = {en} } @inproceedings{NotomistaKammenhuberNadarajanetal.2016, author = {Notomista, Gennaro and Kammenhuber, Alexander and Nadarajan, Parthasarathy and Botsch, Michael and Selvaggio, Mario}, title = {Relative Motion Estimation Based on Sensor Eigenfusion Using a Stereoscopic Vision System and Adaptive Statistical Filtering}, booktitle = {Proceedings of ISR 2016: 47st International Symposium on Robotics}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-4231-8}, pages = {604 -- 609}, year = {2016}, 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{SanchezMoralesMembarthGaulletal.2019, author = {S{\´a}nchez Morales, Eduardo and Membarth, Richard and Gaull, Andreas and Slusallek, Philipp and Dirndorfer, Tobias and Kammenhuber, Alexander and Lauer, Christoph and Botsch, Michael}, title = {Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance}, booktitle = {2019 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-0560-4}, doi = {https://doi.org/10.1109/IVS.2019.8814015}, pages = {2164 -- 2171}, year = {2019}, language = {en} } @unpublished{SanchezMoralesMembarthGaulletal.2020, author = {S{\´a}nchez Morales, Eduardo and Membarth, Richard and Gaull, Andreas and Slusallek, Philipp and Dirndorfer, Tobias and Kammenhuber, Alexander and Lauer, Christoph and Botsch, Michael}, title = {Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.06773}, year = {2020}, language = {en} } @unpublished{SanchezMoralesBotschHuberetal.2020, author = {S{\´a}nchez Morales, Eduardo and Botsch, Michael and Huber, Bertold and Garc{\´i}a Higuera, Andr{\´e}s}, title = {High precision indoor positioning by means of LiDAR}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.06798}, year = {2020}, language = {en} } @inproceedings{SanchezMoralesBotschHuberetal.2019, author = {S{\´a}nchez Morales, Eduardo and Botsch, Michael and Huber, Bertold and Garc{\´i}a Higuera, Andr{\´e}s}, title = {High precision indoor positioning by means of LiDAR}, booktitle = {2019 DGON Inertial Sensors and Systems (ISS), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-1935-9}, doi = {https://doi.org/10.1109/ISS46986.2019.8943731}, year = {2019}, language = {en} } @article{NadarajanBotschSardina2018, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic}, volume = {9}, journal = {Journal of Advances in Information Technology}, number = {1}, publisher = {Engineering and Technology Publishing}, address = {Rowland Heights}, issn = {1798-2340}, doi = {https://doi.org/10.12720/jait.9.1.1-9}, pages = {1 -- 9}, year = {2018}, abstract = {This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future spacetime representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented.}, language = {en} } @unpublished{NadarajanBotschSardina2018, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2512.12907}, year = {2018}, language = {en} } @inproceedings{MuellerNadarajanBotschetal.2016, author = {M{\"u}ller, Marcus and Nadarajan, Parthasarathy and Botsch, Michael and Utschick, Wolfgang and B{\"o}hml{\"a}nder, Dennis and Katzenbogen, Stefan}, title = {A statistical learning approach for estimating the reliability of crash severity predictions}, booktitle = {2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-1889-5}, issn = {2153-0017}, doi = {https://doi.org/10.1109/ITSC.2016.7795911}, pages = {2199 -- 2206}, year = {2016}, language = {en} } @inproceedings{NadarajanBotsch2016, author = {Nadarajan, Parthasarathy and Botsch, Michael}, title = {Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning}, booktitle = {2016 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-1821-5}, doi = {https://doi.org/10.1109/IVS.2016.7535556}, pages = {1285 -- 1292}, year = {2016}, 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} }