in 2013 and 2015, respectively. He is currently a Marie Skłodowska-Curie research fellow. His research topic is the development of driver distraction detection and human-machine interface evaluation methods for multi-actuated ground vehicles. His research interests include human-machine interface, fuzzy systems, machine learning, vehicle safety, ADAS. A novel robust driver distraction detection and evaluation algorithm based on machine learning and fuzzy theory combination is developed. Driver-in-the-loop experiment with Skoda Auto facilities (advance testing of driver decoy workshop) and in cooperation with IPG Automotive GmbH (Secondment 1; 3 month). Publications: “Driver distraction while performing a secondary task evaluation approach based on fuzzy expert system”, 10th Graz Symposium 2017. “A Method of Driver Distraction Evaluation Using Fuzzy Logic”, IEEE ICAT 2017. “Design of Regenerative Anti−lock Braking System Controller for 4 In−Wheel−Motor Drive Electric Vehicle with Road Surface Estimation”, TBD “Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic”, TBD “Driver performance prediction by solving regression analysis”, TBD “Induced by In-Vehicle Information System Driver Distraction Detection and Evaluation Method”, TBD “Driver performance prediction with regression analysis”, TBD Interdisciplinary Training Network in Multi-Actuated Ground Vehicles DRIVER ASSISTANCE MEASURE FOR LOW-EMISSION MAGV https://iteam-project.net CONTACT INFO FUTURE STEPS OUTCOMES PROGRESS ACHIEVED ESR 08: Andrei Aksjonov, MSc. ŠKODA Auto a.s. | Concept Development | Volkswagen Group Tř. Václava Klementa 869, 293 01 Mladá Boleslav II, Czech Republic +420 734 249 830 | andrei.aksjonov@skoda-auto.cz ACKNOWLEDGEMENT This Research receives funding from the European Community Horizon 2020 Framework Programme under grant agreement No. 675999. · Development method of testing and assessment the secondary task impact to the safe vehicle operation, which is exploited for different human-machine interaction technologies comparison and as a benchmark for safe and clear in-vehicle information system design with minimal driver’s burden; · Development software tool for on-board simulation driver decoy on in-vehicle secondary task execution; · Verification simulation results with respect to correctness and completeness on the advanced driving simulator based on a real vehicle platform with identical control elements like steering wheel, pedals, 3D scene and consistent road topology with real test field. Host institution: ŠKODA Auto a.s. Primary supervisor: Pavel Nedoma, PhD Secondary supervisor: Zdeněk Franc, MSc Planned secondments: IPG Automotive GmbH (3 month); Università degli studi di Pavia (2 month) MSc. Andrei Aksjonov OBJECTIVES AND WORK PLAN Andrei Aksjonov received his BSc and MSc in electrical drives and power electronics with automation specialization from Tallinn University of Technology The method capable not only to detect driver distraction induced by secondary activity, but also to evaluate its impact on traffic safety is developed. The algorithm operates as follows: vDesign each individual driver normal driving model using machine learning (ANFIS, ANN, ED, k-NN); vPredict driver behavior and compare it with driver distraction lap; vEvaluate (fuzzy logic) level of driver distraction based on vehicle dynamic performance. Predictor dx dv Vl r dvp dxp dvr dxr DD        dv dv dv dv dv dv dv p p p r , ,0        dx dx dx dx dx dx dx p p p r , ,0 Driver Vehicle Environment Dynamics Performance Distraction detection Secondary task Actuation Performance prediction Road Elaboration of the driver advisory functions related to the vehicle and traffic safety improvement, emission reduction and energy-efficiency increase. These functions will be realized in human-machine interface (HMI) systems with visual and haptic channels supporting interactions between the driver and on-board systems of Multi-Actuated G