Telemetry-based optimisation for user training in racing simulators

Abstract

Motorsports, like all major forms of sporting activities, require training and dedication to master, supplemented by hours of rote learning and mentoring by experts. This study explores the question of whether a serious game is a powerful enough pedagogical tool to be gainfully employed in the training of race drivers. A system of heuristics is proposed for a novel telemetry-based feedback model for contextual real-time suggestions. The model has been integrated into a racing simulation game and a study of its performance reported here. The study consists of 27 participants, partitioned into two groups, to provide a control for the experiment. Two questionnaires have been used to acquire demographic information about the participants and help control for factors such as experience. Quantitative results show that there is an improvement for the group using the feedback system, although this improvement dissipates when the feedback is disabled again for the feedback group. Analysis of the initial results are encouraging, with the model showing promise. Additionally, the lack of cognitive retention on behalf of the participants when feedback was disabled merits further investigation and future work.