@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doi = {https://doi.org/10.1109/IV51971.2022.9827187}, pages = {484 -- 491}, year = {2022}, language = {en} } @unpublished{KruberWurstBotsch2020, author = {Kruber, Friedrich and Wurst, Jonas and Botsch, Michael}, title = {An Unsupervised Random Forest Clustering Technique for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{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{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} } @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} } @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} } @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} } @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} } @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} } @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{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} }