University of Twente Student Theses


Enhancement of Formula One Driver Performance by Group Driven Process Learning

Moghaddar, A.A. (2022) Enhancement of Formula One Driver Performance by Group Driven Process Learning.

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Abstract:Throughout the different Formula One seasons, it is seen that younger, less experienced, drivers lack pace and performance compared to their peers. Training these drivers requires time, which, due to the regulations, teams do not have. A leading misconception that affects the level of confidence and learning abilities of the driver, is that the less experienced drivers are expected to perform at the same level as experienced drivers. This thesis has the aim of analyzing the abilities and performances of both drivers within a Formula One team to redesign and redefine the driver training method. The focus of change and improvement is to provide drivers with real-time insights and feedback on their performance during a simulator training session based on the data harvested from both drivers. By using a combination of the fundamental principles of process mining and statistical analysis, data markers are created on the track. Accordingly, an advice marker is created with which the real-time telemetry data is compared. Based on the differences in telemetry, visual feedback is provided to the driver. Throughout the research, this method of training has proven to be promising, drivers showed a significant increase in their overall performance and simultaneously showed an increase in car control and confidence. Nonetheless, due to the limitations, a statistical backbone is missing and more testing needs to be done to guarantee a consistent outcome. For the performance assessment, a methodology process is developed to generalize this training method among other teams and drivers.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science, 57 mining engineering, 58 process technology
Programme:Creative Technology BSc (50447)
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