Jonas Wurst, M.Sc.

Research

• Intelligent Data Recording in the Automotive Field (IDA)

• BayWISS-Kolleg Mobility & Transport

Publications

• L. Balasubramanian, J. Wurst, R. Egolf, M. Botsch, W. Utschick, K. Deng, "ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios", IEEE International Conference on Intelligent Transportation Systems, Macau, Macau, 2022. [ accepted | arXiv ]
• A. Flores Fernández, J. Wurst, E. Sánchez Morales, M. Botsch, C. Facchi and A. García Higuera, "Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction", MDPI Journal Sensors Vol. 22 Iss. 12, 2022 [ MDPI ]
• J. Wurst, L. Balasubramanian, M. Botsch, W. Utschick, "Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios", IEEE Intelligent Vehicles Symposium, Aachen, Germany, 2022. [ IEEE | arXiv ]
• L. Balasubramanian, J. Wurst, M. Botsch and K. Deng, "Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity", IEEE Intelligent Vehicles Symposium, Nagoya, Japan, 2021. [ IEEE | arXiv ]
• J. Wurst, L. Balasubramanian, M. Botsch, W. Utschick, "Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder ", IEEE Intelligent Vehicles Symposium, Nagoya, 2021. [ IEEE | arXiv ]
• J. Wurst, A. Flores Fernández, M. Botsch and W. Utschick, "An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images", IEEE Intelligent Vehicles Symposium, Las Vegas, 2020. [ IEEE | arXiv ]
• F. Kruber, J. Wurst, E. Sánchez Morales, S. Chakraborty and M. Botsch, Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification", 30th IEEE Intelligent Vehicles Symposium, 2019. [ IEEE | arXiv ]
• F. Kruber, J. Wurst, S. Charkraborty and M. Botsch, "Highway traffic data - macroscopic, microscopic and criticality analysis for capturing relevant traffic scenarios and traffic modeling based on the highD data set", arxiv.org (open access platform), 2019. [ arXiv ]
• F. Kruber, J. Wurst, and M. Botsch, „An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization”, IEEE International Conference on Intelligent Transportation Systems, 2018. [ IEEE | arXiv ]