A Machine Learning Approach for Modeling and Analyzing of Driver Performance in Simulated Racing

نویسندگان

چکیده

Abstract The emerging progress of esports lacks the approaches for ensuring high-quality analytics and training in professional amateur teams. In this paper, we demonstrated application Artificial Intelligence (AI) Machine Learning (ML) approach domain, particularly simulated racing. To achieve this, gathered a variety feature-rich telemetry data from several web sources that was captured through MoTec software ACC racing game. We performed number analyses using ML algorithms to classify laps into performance levels, evaluating driving behaviors along these finally defined prediction model highlighting channels/features have significant impact on driver performance. identify optimal feature set, three selection algorithms, i.e., Support Vector (SVM), Extreme Gradient Boosting (XGBoost) Random Forest (RF) been applied where out 84 features, subset 10 features has selected as best subset. For classification, XGBoost outperformed RF SVM with highest accuracy score among other evaluated models. study highlights promising use AI categorize sim racers according their technical-tactical behaviour, enhancing knowledge know how.

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ژورنال

عنوان ژورنال: Communications in computer and information science

سال: 2023

ISSN: ['1865-0937', '1865-0929']

DOI: https://doi.org/10.1007/978-3-031-26438-2_8