Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning

Within motorsports less experienced drivers lack pace and performance compared to their peers. Training these drivers requires time, which, due to the regulations and resources, teams often do not have. Less experienced drivers are expected to perform at the same level as experienced drivers. This paper has the aim of analyzing the abilities and performances of both drivers within a Formula One team to redesign the driver training method. The focus is to provide drivers with real-time insights and feedback on their performance during a simulator training session. By using a combination of the principles of process mining and statistical analysis, data markers are created on the track. Based on the differences in telemetry, visual feedback is provided to the driver. Throughout the research, this manner of training has proven to be promising. Drivers showed an increase in their overall performance and an increase in car control and confidence. Despite these promising results more exper iments need to be done to guarantee a consistent outcome and to prove the effectiveness of this training program. To continue developments, further research can be conducted on the topic of visualization and communication.