@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doi = {https://doi.org/10.1109/IJCNN.2017.7965995}, pages = {1244 -- 1251}, year = {2017}, language = {en} } @unpublished{NadarajanBotschSardina2017, author = {Nadarajan, Parthasarathy and Botsch, Michael and Sardina, Sebastian}, title = {Predicted-occupancy grids for vehicle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= {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2512.12901}, year = {2017}, language = {en} } @unpublished{NadarajanBotsch2016, author = {Nadarajan, Parthasarathy and Botsch, Michael}, title = {Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety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{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doi = {https://doi.org/10.1109/IVS.2019.8814015}, pages = {2164 -- 2171}, year = {2019}, language = {en} } @unpublished{SanchezMoralesMembarthGaulletal.2020, author = {S{\´a}nchez Morales, Eduardo and Membarth, Richard and Gaull, Andreas and Slusallek, Philipp and Dirndorfer,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} }