Statistical signal processing methods have been increasingly used in vehicles since the introduction of exteroceptive sensors. The methods play a crucial role in the tracking of objects and in estimation algorithms of integrated vehicle safety functions. In order to prevent accidents in complex traffic situations involving many dynamic road users, methods of machine learning are investigated in this working group. The focus lies on methodologies that enable the use of machine learning techniques in combination with model-based approaches for safety-critical applications.
Publications
- 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. [ accepted ]
- 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. [ accepted ]
- 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 ]
- 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 ]
- 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. [arxiv pre-print]
- 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. [arxiv pre-print]
- 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. [arxiv pre-print]
- 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]
- 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. [arxiv pre-print]
- 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 link]
- 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.
- 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.
- 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.
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.
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.
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: 10.12720/jait.9.1.15-24.
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: 10.12720/jait.9.1.1-9.
A. Chaulwar, M. Botsch, and W. Utschick, “Generation of Reference Trajectories for Safe Trajectory Planning”, International Conference on Artificial Neural Networks, 2018.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- P. Nadarajan; M. Botsch, “Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning”, IEEE Intelligent Vehicles Symposium, 2016.
- 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.
- G. Notomista und M. Botsch, "Maneuver Segmentation for Autonomous Parking Based on Ensemble Learning", International Joint Conference on Neural Networks, 2015.