Lehrstuhl SRS - Team

Kurzvita / CV

Veröffentlichungen / Publications

  • David, R.; Söffker, D.: A review on Machine Learning-based models for lane changing behavior prediction and recognition. Frontiers-Future Transportation, Lausanne, Switzerland, Vol. 4, 2023.
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  • David, R.; Söffker, D.: Comparison of Different Hyperparameter Optimization Methods on Driving Behavior Recognition. 13th Cognitive Situation Management (CogSIMA) conference, 2023.
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  • David, R.; Söffker, D.: A modified Hidden Markov Model (HMM)-based state machine model for driving behavior recognition: Effectiveness of features using different sub-HMMs . 13th Cognitive Situation Management (CogSIMA) conference, 2023.
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  • David, R.; Söffker, D.: A state machine-based approach for estimating the capacity loss of Lithium-Ion Batteries. 15th Annual Conference of the Prognostics and Health Management Society, 2023.
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  • David, R.; Söffker, D.: Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition. IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Salerno, Italy, 2022, pp. 16-22.
  • David, R.; Söffker, D.: Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition. The 12th IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Salerno, Italy, 2022, pp. 16-22.
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  • David, R.; Söffker,D.: A study on a HMM-based state machine approach for lane changing behavior recognition. IEEE Access, Vol. 10, 2022, pp 122954-122964.
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  • David, R.; Rothe, S.; Söffker, D.: Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach. 2021 IEEE International Conference on Intelligent Transportation ( ITSC), Indianapolis, USA, 2021.
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  • David, R.; Rothe, S.; Söffker, D.: State Machine approach for lane changingdriving behavior recognition. Automation, MDPI, Special Issue: Automation in Intelligent Transportation Systems, Vol. 1, 2020, pp. 68-79.
    • , [PDF], [Link]