Current projects

IDA

Intelligent Data Recording in the Automotive Field

ITraCS

Interaction-based Trajectory Prediction for Collision Avoidance in Safety Systems

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

Completed projects

HySLEUS

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

SUP

Reliable Crash Prediction

Publications

  • 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.