@unpublished{KruberWurstBotsch2020, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael}, title = {An Unsupervised Random Forest Clustering Technique for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{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{KruberWurstBotsch2018, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael}, title = {An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization}, booktitle = {2018 IEEE Intelligent Transportation Systems Conference}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-0323-5}, doi = {https://doi.org/10.1109/ITSC.2018.8569682}, pages = {2811 -- 2818}, year = {2018}, 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} } @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} } @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} } @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{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{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} }