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Maschinelles Lernen für Fahrzeugsicherheitssysteme

Methoden der statistischen Signalverarbeitung haben durch die Einführung vorausschauender Sensoren vermehrt Einzug in Fahrzeuge erhalten. Die Verfahren spielen eine zentrale Rolle im Tracking von Objekten und in Schätzalgorithmen von Funktionen der integralen Fahrzeugsicherheit. Um die Vermeidung von Unfällen in komplexen Verkehrssituationen, mit vielen Verkehrsteilnehmern zu gewährleisten, werden Methoden des maschinellen Lernens erforscht. Im Fokus stehen dabei Verfahren, die es ermöglichen, maschinelles Lernen in Kombination mit modellbasierten Methoden in sicherheitskritischen Anwendungen zu nutzen.

Projekte

  • DSA

    Datengetriebene Szenarioanalyse für die Validierung automatisierter Fahrfunktionen

  • ITraKS

    Interaktionsbasierte Trajektorienprädiktion zur zuverlässigen Kollisionsvermeidung

  • KIaF

    KI-Algorithmen für automatisiertes Fahren

  • IDA

    Intelligente Datenaufnahme im Bereich Automotive

  • MLPOG

    Prädizierte Belegungskarten und statistische Lernverfahren zur Planung und Validierung von Fahrzeugsicherheitssystemen

  • HySLEUS

    Hybride Statistische Lernmethoden für die Embedded-Umsetzung von Sicherheitsfunktionen im Fahrzeug

  • SUP

    Sichere Unfallprognose

  • OLAF

    Online Maschinelle Lernverfahren für Anwendungen der Fahrzeugsicherheit

  • VbD

    Absicherungsfähigkeit und Interpretation von maschinellen Lernverfahren für automatisiertes Fahren durch Entwurf

Veröffentlichungen

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 ]

2025

  • R. Egolf, A. Fertig and M. Botsch, "Conditioned Trajectory Generation for Realistic Driving Scenarios via a Hybrid Machine Learning Architecture," 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), Gold Coast, Australia, 2025. [ IEEE | DOI ]
  • A. Kalyanasundaram, K. C. Sekaran, P. Stäuber, M. Lange, W. Utschick and M. Botsch, "Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation," 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 2025. [ IEEE |  DOI ]
  • T. Elter, T. Dirndorfer, M. Botsch and W. Utschick, "Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety," 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 2025. [ IEEE |  DOI ]
  • N. Roßberg, M. Neumeier, S. Hasirlioglu, M. E. Bouzouraa and M. Botsch, "Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis," 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 2025. [ IEEE |  DOI | arXiv ]
  • A. Fertig, L. Balasubramanian and M. Botsch, "Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction," 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 2025. [ IEEE | DOI | arXiv ]
  • K. Chandra Sekaran, M. Geisler, D. Rößle, Adithya Mohan, D. Cremers, W. Utschick, M. Botsch, W. Huber, and T. Schön, "UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception", Advances in Neural Information Processing Systems (NeurIPS), volume 38. Curran Associates Inc. 2025. [ NeurIPS | arXiv | DOI ]

2024

  • P. Riegl, K. C. Sekaran and M. Botsch, "Generation of Realistic Traffic Scenarios for Virtual and Real Test Drives Based on a Hybrid Machine Learning Framework," 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Ahmedabad, India, 2024. [ IEEE |  DOI ]
  • M. Neumeier, S. Dorn, M. Botsch and W. Utschick, "Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models," 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2024. [ IEEE |  DOI ]
  • K. C. Sekaran, L. Balasubramanian, M. Botsch and W. Utschick, "Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors," 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Korea, Republic of, 2024. [ IEEE |  DOI ]
  • A. Fertig, L. Balasubramanian and M. Botsch, "Clustering and Anomaly Detection in Embedding Spaces for the Validation of Automotive Sensors," 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Korea, Republic of, 2024. [ IEEE |  DOI ]

2023

  • M. Neumeier, A. Tollkühn, S. Dorn, M. Botsch and W. Utschick, "Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications ", 35th IEEE Intelligent Vehicles Symposium, Anchorage, United States, 2023. [ IEEE |  DOI | arXiv ]
  • M. Neumeier, A. Tollkühn, S. Dorn, M. Botsch and W. Utschick, "Gradient Derivation for Learnable Parameters in Graph Attention Networks", 2023. [ DOI |arXiv ]
  • M. Neumeier, S. Dorn, M. Botsch and W. Utschick, " Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain", 26th IEEE International Conference on Intelligent Transportation Systems, Bilbao, Spain, 2023. [ IEEE |  DOI | arXiv ]
  • L. Balasubramanian, J. Wurst, R. Egolf, M. Botsch, W. Utschick and K. Deng, "SceneDiffusion: Conditioned Latent Diffusion Models for Traffic Scene Prediction," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023. [ IEEE |  DOI ]
  • P. Nadarajan, M. Botsch and S. Sardina, "Continuous Probabilistic Motion Prediction Based on Latent Space Interpolation," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023. [ IEEE |  DOI ]
  • K. C. Sekaran, L. Balasubramanian, M. Botsch and W. Utschick, "Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors," 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA, 2023. [ IEEE |  DOI ]
  • L. Balasubramanian, J. Wurst, M. Botsch and K. Deng, "Open-World Learning for Traffic Scenarios Categorisation," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 5, pp. 3506-3521, May 2023. [ IEEE |  DOI ]
  • M. Botsch, W. Huber, L. Balasubramanian, A. F. Fernández, M. Geisler, C. Gudera, M. R. Morales Gomez, P. Riegl, E. Sánchez Morales, M. Weinzierl, and K. C. Sekaran, "Data Collection and Safety Use Cases in Smart Infrastructures", In Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '23 Adjunct), Association for Computing Machinery, New York, NY, USA, 2023. [ ACM |  DOI ]
  • A. Flores Fernández, E. Sánchez Morales, M. Botsch, C. Facchi, A. García Higuera, "Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics", Sensors, 2023. [  MDPI | DOI ]
  • F. Kruber, J. Wurst, M. Botsch, S. Chakraborty, "Unsupervised Random Forest Learning for Traffic Scenario Categorization", In: Kukkala, V.K., Pasricha, S. (eds) Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems. Springer, pp. 565-590, ISBN: 978-3-031-28016-0, 2023. [ Springer | DOI ]

2022

  • T. Elter, T. Dirndorfer, M. Botsch, W. Utschick, "Interaction-aware Prediction of Occupancy Regions Based on a POMDP Framework", IEEE International Conference on Intelligent Transportation Systems, Macau, Macau, 2022. [ IEEE | DOI ]
  • M. Neumeier, A. Tollkühn, M. Botsch and W. Utschick, " A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction ", IEEE International Conference on Intelligent Transportation Systems, Macau, Macau, 2022. [ IEEE | DOI ]
  • 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. [ IEEE | DOI | arXiv ]
  • 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 |  DOI | arXiv ]
  • F. Kruber, E. Sánchez Morales, R. Egolf, J. Wurst, S. Chakraborty, M. Botsch, "Micro- and Macroscopic Road Traffic Analysis using Drone Image Data", Special Issue on Embedded Systems for Computer Vision, Leibniz Transactions on Embedded Systems, Volume 8, Issue 1, pp. 02:1-02:27, 2022. [ DOI ]
  • A. Flores Fernández, J. Wurst, E. Sánchez Morales, M. Botsch, C. Facchi, A. García Higuera, "Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction", Sensors, 2022. [ MDPI | 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 ]

2019

  • A. Chaulwar, M. Botsch, and W. Utschick, “Efficient Hybrid Machine Learning Algorithm for Trajectory Planning in Critical Traffic-Scenarios”, International Conference on Intelligent Transportation Engineering, Singapore, September 2019. [ IEEE | DOI ]
  • O. Gallitz, O. d. Candido, M. Botsch, and W. Utschick, “Interpretable Feature Generation using Deep Neural Networks and its Application to Lane Change Detection”, IEEE International Conference on Intelligent Transportation Systems, New Zealand, October 2019. [ IEEE | DOI ]
  • E. Sánchez Moraless, R. Membarth, A. Gaull, P. Slusallek, T. Dirndorfer, A. Kammenhuber, C. Lauer, M. Botsch, "Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance," 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 2019. [ IEEE | DOI ]
  • E. Sánchez Morales, M. Botsch, B. Huber and A. García Higuera, “High precision  indoor positioning by means of LiDAR”, DGON Inertial Sensors and Systems (ISS) Symposium Gyro Technology, Braunschweig, Germany, 2019. [ IEEE | DOI ]
  • E. Sánchez Morales, M. Botsch, B. Huber and A. García Higuera, “High Precision Indoor Navigation for Autonomous Vehicles", International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy, 2019. [ IEEE | DOI ]
  • F. Kruber, J. Wurst, E. S. Morales, S. Chakraborty and M. Botsch, "Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification," 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 2019. [ IEEE | DOI ]
  • 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 | 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 ]
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