TY - CHAP A1 - Ziegmann, Johannes A1 - Schmid, Michael A1 - Endisch, Christian T1 - Sensitivity Analysis for Driver Energy Prediction with Environmental Features and Naturalistic Data T2 - 2018 IEEE International Conference on Intelligent Engineering Systems (INES) UR - https://doi.org/10.1109/INES.2018.8523987 KW - Driver Energy Prediction KW - Environmental Feature Selection KW - Sensitivity Analysis KW - Feature Transformation KW - Filter and Wrapper Methods Y1 - 2018 UR - https://doi.org/10.1109/INES.2018.8523987 SN - 978-1-5386-1122-7 SP - 000199 EP - 000206 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Ziegmann, Johannes A1 - Shi, Jieqing A1 - Schnörer, Tobias A1 - Endisch, Christian T1 - Analysis of individual driver velocity prediction using data-driven driver models with environmental features T2 - 2017 IEEE Intelligent Vehicles Symposium UR - https://doi.org/10.1109/IVS.2017.7995770 KW - vehicles KW - predictive models KW - training KW - prediction algorithms KW - kernel KW - navigation KW - biological system modeling Y1 - 2017 UR - https://doi.org/10.1109/IVS.2017.7995770 SN - 978-1-5090-4804-5 SP - 517 EP - 522 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Vögele, Ulrich A1 - Ziegmann, Johannes A1 - Endisch, Christian T1 - Driver adaptive predictive velocity control T2 - 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC.2017.8317668 KW - Predictive Velocity Control KW - Longitudinal Vehicle Control KW - Driver Adpative KW - Multi-Criteria Optimization Y1 - 2017 UR - https://doi.org/10.1109/ITSC.2017.8317668 SN - 978-1-5386-1526-3 SN - 2153-0017 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Müller, Tino A1 - Ziegmann, Johannes A1 - Krüner, Simon A1 - Endisch, Christian T1 - Optimization of inverse model identification for multi-axial test rig control JF - MATEC Web of Conferences N2 - Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO) model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization. UR - https://doi.org/10.1051/matecconf/20164201002 Y1 - 2016 UR - https://doi.org/10.1051/matecconf/20164201002 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-16314 SN - 2261-236X VL - 2016 IS - 42 PB - EDP Sciences CY - Les Ulis ER - TY - CHAP A1 - Ziegmann, Johannes A1 - Denk, Florian A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - Stochastic Driver Velocity Prediction with Environmental Features on Naturalistic Driving Data T2 - 2018 IEEE Intelligent Transportation Systems Conference UR - https://doi.org/10.1109/ITSC.2018.8569767 KW - Velocity prediction KW - driver behavior modeling KW - energy prediction KW - Kalman filter KW - Particle filter KW - switching hidden Markov model Y1 - 2018 UR - https://doi.org/10.1109/ITSC.2018.8569767 SN - 978-1-7281-0323-5 SP - 1807 EP - 1814 PB - IEEE CY - Piscataway ER -