Lehrstuhl SRS - Team

Kurzvita / CV

2020 Dr.-Ing. Mechanical Engineering,
Faculty of Engineering,
University of Duisburg-Essen, Germany
2014M.Sc. in Mechanical Engineering/Mechatronics,
Faculty of Engineering,
University of Duisburg-Essen, Germany
2009B.Sc. in Transportation,
School of Automotive Engineering,
Harbin Institute of Technology, China

Veröffentlichungen / Publications

  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: How to evaluate classifier performance in the presence of additional effects: A new POD-based approach allowing certification of machine learning approaches. Machine Learning with Applications (Elsevier), Volume 7, 15 March, 2022.
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  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: Evaluating Machine Learning-Based Classification Approaches: A New Method for Comparing Classifiers Applied to Human Driver Prediction Intentions. IEEE Access, Vol. 10, 2022, pp. 62429-62439.
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  • Deng, Q.; Saleh, M.; Tanshi, F.; Söffker, D.: Online Intention Recognition Applied to Real Simulated Driving Maneuvers. IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA 2020), British Columbia, Canada, 2020, pp. 1-6.
  • Deng, Q.; Söffker, D.: A Review of the current HMM-based Approaches of Driving Behaviors Recognition and Prediction. IEEE Transactions on Intelligent Vehicles (TIV), 2020, accepted.
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  • Deng, Q.; Wang, J.; Hillebrand, K.; Benjamin, C.R.; Söffker, D.: Prediction performance of lane changing behaviors: a study of combining environmental and eye-tracking data in a driving simulator. IEEE Transactions on Intelligent Transportation Systems (ITS), Vol. 21, No.8, 2019, pp. 3561-3570.
  • Ameyaw, D. A.; Deng, Q.; Söffker, D.: Probability of Detection (POD)-based metric for evaluation of Classifiers used in Driving Behavior Prediction. Proceedings of the Annual Conference of the PHM Society, 11(1) , Scottsdale, Arizona, USA, 2019.
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  • Deng, Q.; Söffker, D.: Multi-Level HMMs-based Cognitive modeling for Human Driving Intentions Recognition. 2019 KS Workshop, Duisburg, Germany, März 26-28, 2019.
  • Deng, Q.; Söffker, D.: Modeling and Prediction of Human Behaviors based on Driving Data using Multi-Layer HMMs. IEEE Transactions on Intelligent Transportation Systems Conference (ITSC 2019), Auckland, New Zealand, 2019, pp. 2014-2019.
  • Deng, Q.; Söffker, D.: Classifying Human Behaviors: Improving Training of Conventional Algorithms. IEEE Transactions on Intelligent Transportation Systems Conference (ITSC 2019), Auckland, New Zealand, 2019, pp. 1060-1065.
  • Deng, Q.; Wang, J.; Söffker, D.: Prediction of human driver behaviors based on an improved HMM approach. 2018 IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China, 2018, pp. 2066-2071.
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  • Deng, Q.; Söffker, D.: Improved driving behaviors prediction based on Fuzzy Logic-Hidden Markov Model (FL-HMM). 2018 IEEE Intelligent Vehicles Symposium, Changshu, Suzhou, China, 2018, pp. 2003-2008.
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  • Deng, Q.; Söffker, D.: Improved human driving behaviors prediction based on Fuzzy Logic-Hidden Markov Model. 7. Interdisziplinärer Workshop Kognitive Systeme: Mensch, Teams, Systeme und Automaten, Braunschweig, Germany, 2018.
  • Deng, Q.; Wang, J.; Söffker, D.: Defining Feature Properties for Optimal HMM-based Situation Recognition for Human Drivers. Kognitive Systeme: Mensch, Teams, Systeme und Automaten, Neubiberg bei München, Germany, 2017.
  • Muthig, O.; Wang, J.; Deng, Q.; Söffker, D.: Integrating situated human interaction modeling and stochastic state automata for improved technical situation awareness. IFAC-PapersOnLine, Vol. 48(1), 2015, pp. 87-92.
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