Abstract
- The rapidly growing market of eSports has become a lucrative industry. Sim racing is one of the traditional sports that gain increasing popularity in eSports. With raising profits, lucrative sponsoring contracts, and competitive price money, incentives for fraud are also on the upraise. This is further facilitated by the COVID-19 pandemic, which led to the substitution of live eSport events by virtual formats. Particularly, in sim racing it becomes increasingly challenging to verify the contestants identity in virtual events. Here, we propose and evaluate a generic workflow to identify personal driving style by transforming raw racing data (including measurements extracted from the simulation software and connected simulator hardware) into a comparable representation (fingerprint). As data base we used an extensive collection of telemetry data recorded from the racing simulation Assetto Corsa on 3 immersive motion simulators. The set contains over 2,000 laps of seven player recorded in weekly sessions. The experiments demonstrate the feasibility to distinguish players using the proposed method on different tracks, car models, and motion simulators. Due to the extensive experiments, we could achieve a driver separation score of up to 89%. The raw data and the raw results are made publicly available on Github.