Skip to content

Projects

  • DSA

    Data-Driven Scenario Analysis for the Validation of Automated Driving Functions

  • ITraCS

    Interaction-based Trajectory Prediction for Collision Avoidance in Safety Systems

  • KIaF

    KI-Algorithmen für automatisiertes Fahren

  • MLPOG

    Efficient Design and Validation of Vehicle Safety Systems based on Predicted Occupancy Grids and Machine Learning

  • OLAF

    Online Learning Methods for Vehicle Safety Applications

  • VbD

    Validation by Design

  • SUP

    Reliable Crash Prediction

  • HySLEUS

    Hybrid statistical learning methods for the embedded implementation of vehicle safety systems

Publications

2026

  • A. Fertig, K. Sekaran, L. Balasubramanian and M. Botsch, "Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings “, 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted | arXiv pre-print ]
  • K. Chandra Sekaran, A. Kalyanasundaram, M. Botsch, and W. Utschick,  "A Zero-Shot Annotation-Free Framework for Efficient Monocular 3D Object Localization in Infrastructure Camera Systems", 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted ]
  • M. Neumeier, N. Roßberg, M. Botsch, W. Utschick, "Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling", 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted | arXiv pre-print ]
  • A. Roßberg, S. Hasirlioglu, M. E. Bouzouraa, W. Utschick, M. Botsch, "Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE", 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted | arXiv pre-print ]
  • R. Egolf and M. Botsch, "Physics-Informed Generative Architecture for Realistic and Calibrated Trajectory Synthesis using Gumbel–Softmax Sampling", 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted ]
  • P. Riegl, K. Chandra Sekaran, and M. Botsch, "Metric Learning–Based Latent Space Construction for Edge-Case Detection in Multi-Agent Traffic Scenarios", 2026 IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, USA, 2026. [ accepted ]
  • A. Fertig and M. Botsch, "Run-Time Monitoring Frameworks for Validation of Autonomous Vehicles: A Focused Review of Applications", 2026 IEEE International Conference In Electronic Engineering and Information Technology (EEITE), Chania, Greece, 2026. [ accepted ]
  • A. Fertig and M. Botsch, "Monitoring automotive perception sensors using latent representations", AT - Automatisierungtechnik, vol. 74, no. 5, pp. 349-360, 2026. [ DOI ]

2021

  • M. Neumeier, A. Tollkühn, T. Berberich, and M. Botsch, "Variational autoencoder-based vehicle trajectory prediction with an interpretable latent space", 24th IEEE International Conference on Intelligent Transportation Systems, Indianapolis, United States, 2021. [ IEEE | DOI | arXiv ]
  • L. Balasubramanian, F. Kruber, M. Botsch and K. Deng, "Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios", IEEE Intelligent Vehicles Symposium, Nagoya, Japan, 2021. [ IEEE | DOI | 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 | DOI | arXiv ]
  • O. Gallitz, O. d. Candido, M. Botsch, and W. Utschick, “Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture”, IEEE International Conference on Intelligent Transportation Systems, USA, September 2021. [ IEEE | DOI ]
  • 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 | DOI | arXiv ]
  • E. Sánchez Morales, J. Dauth, B. Huber, A. García Higuera, M. Botsch, "High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles", Sensors, 2021. [ MDPI | DOI ]
  • A. Chaulwar, H. Al-Hashimi, M. Botsch, W. Utschick, "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning", International Journal of Machine Learning, Vol 11, No. 1, pp. 1-11, January 2021. [ DOI ]

2020

  • M. Botsch, W. Utschick, "Fahrzeugsicherheit und Automatisiertes Fahren: Methoden der Signalverarbeitung und des maschinellen Lernens", München, Hanser Verlag, ISBN: ISBN 978-3-446-46804-7, 2020. [ Hanser ]
  • O. Gallitz, O. d. Candido, R. Melz, M. Botsch, and W. Utschick, “Interpretable Machine Learning Structure for an Early Prediction of Lane Changes”, Artificial Neural Networks and Machine Learning – ICANN 2020, Slovakia, September 2020. [ Springer | DOI ]
  • O. d. Candido, M. Koller, O. Gallitz, R. Melz, M. Botsch, and W. Utschick, "Towards Feature Validation in Time to Lane Change Classification using Deep Neural Networks", IEEE International Conference on Intelligent Transportation Systems, Virtual Conference, 2020. [ IEEE | DOI ]
  • 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 | DOI ]
  • F. Kruber, E. Sánchez Morales, S. Chakraborty and M. Botsch, "Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles", IEEE Intelligent Vehicles Symposium, Las Vegas, 2020. [ IEEE | DOI | arXiv ]
  • E. Sánchez Morales, F. Kruber, M. Botsch, B. Huber and A. García Higuera, "Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery", IEEE Intelligent Vehicles Symposium, Las Vegas, 2020. [ IEEE | DOI ]

2018

  • F. Kruber, J. Wurst and M. Botsch, "An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018. [ IEEE | DOI ]
  • M. Müller, X. Long, M. Botsch, D. Böhmländer, and W. Utschick „Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model”, IEEE International Conference on Intelligent Transportation Systems, 2018. [ IEEE | DOI ]
  • V. Cañas, E. Sánchez, M. Botsch and A. Garcia, "Wireless Communication System for the Validation of Autonomous Driving Functions on Full-Scale Vehicles," 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain, 2018. [ IEEE | DOI ]
  • M. Müller, M. Botsch, D. Böhmländer, W. Utschick, "Machine Learning Based Prediction of Crash Severity Distributions for Mitigation Strategies", Journal of Advances in Information Technology, Vol. 9, No. 1, pp. 15-24, February 2018. [ DOI ]
  • P. Nadarajan, M. Botsch, and S. Sardina, "Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic", Journal of Advances in Information Technology, Vol. 9, No. 1, pp. 1-9, February 2018. [ DOI ]
  • O. Gallitz, O. De Candido, M. Botsch, and W. Utschick, "Validation of Machine Learning Algorithms through Visualization Methods", VDI Electronics in Vehicles (ELIV) Market-Place, Baden-Baden, Germany, 2018. [  DOI ]
  • A. Chaulwar, M. Botsch, and W. Utschick, “Generation of Reference Trajectories for Safe Trajectory Planning”, International Conference on Artificial Neural Networks, 2018. [ Springer | DOI ]

2017

  • G. Notomista, M. Botsch, "A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking", Journal of Artificial Intelligence and Soft Computing Research, Volume 7, Issue 4, pp. 243-255, 2017. [ DOI ]
  • A. Chaulwar, M. Botsch, W. Utschick, “A Machine Learning based Biased-Sampling Approach for Planning Safe Trajectories in Complex, Dynamic Traffic-Scenarios”, IEEE Intelligent Vehicles Symposium, 2017. [ IEEE | DOI ]
  • M. Müller, M. Botsch, D. Böhmländer, W. Utschick, „A Simulation Framework for Vehicle Safety Testing / Ein Simulationsframework für die Absicherung von Fahrzeugsicherheitsfunktionen“, Fachbuch/Tagung "Aktive Sicherheit und Automatisiertes Fahren", expert Verlag, pp. 135-155, ISBN: 978-3-8169-3405-9, 2017.
  • P. Nadarajan, M. Botsch, S. Sardina, “Predicted-Occupancy Grids for Vehicle Safety Applications based on Auotencoders and the Random Forest Algorithm”, International Joint Conference on Neural Networks, 2017. [ IEEE | DOI ]

2016

  • A. Chaulwar, M. Botsch, W. Utschick, “A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios”, IEEE International Conference on Machine Learning and Applications, 2016. [ IEEE | DOI ]
  • M. Müller, P. Nadarajan, M. Botsch, W. Utschick, D. Böhmländer, S. Katzenbogen, “A Statistical Learning Approach for Estimating the Reliability of Crash Severity Predictions”, IEEE Intelligent Transportation Systems Society Conference, 2016. [ IEEE | DOI ]
  • G. Notomista, A. Kammenhuber, P. Nadarajan, M. Botsch, M. Selvaggio, “Relative Motion Estimation Based on Sensor Eigenfusion Using a Stereoscopic Vision System and Adaptive Statistical Filtering”, International Symposium on Robotics, 2016. [ IEEE ]
  • P. Nadarajan; M. Botsch, “Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning”, IEEE Intelligent Vehicles Symposium, 2016. [ IEEE | DOI ]
  • Chaulwar, M. Botsch, T. Krueger und T. Miehling, “Planning of safe trajectories in dynamic multi-object traffic-scenarios”, Journal of Traffic and Logistics Engineering, Vol.4, no.2, 2016. [ JTLE ]

2015

  • G. Notomista und M. Botsch, "Maneuver Segmentation for Autonomous Parking Based on Ensemble Learning", International Joint Conference on Neural Networks, 2015. [ IEEE | DOI ]
  • S. Herrmann, W. Utschick, M. Botsch, and F. Keck, "Supervised learning via optimal control labeling for criticality classification in vehicle active safety", IEEE International Conference on Intelligent Transportation Systems, Gran Canaria, 2015. [ IEEE | DOI ]
Jump to the top of the page