This thesis explores enhancing track generalization in motorsport driver models through image-based feature sets, drawing inspiration from autonomous driving applications in urban settings. Traditional motorsport models often rely on numeric features, which excel on known tracks but face limita- tions when adapting to new, unseen environments. To address this, I introduce a CNN-based model that integrates bird’s- eye-view images with vehicle states and path-planning data, allowing a more holistic perception of track layouts and sur- roundings. Through open-loop evaluations on unseen tracks, the proposed model demonstrates superior generalization, achieving significantly lower RMSE compared to boundary point-based models, with improvements observed across steering, braking, and acceleration actions. Additionally, I apply novelty detection using Mahalanobis Distance to isolate Out-of-Distribution(OoD) scenarios, providing a precise measure of the generalization gap. This work establishes a baseline for image feature design in motorsport driver modeling, emphasizing the role of spatial and contextual information in achieving adaptable and high- performance autonomous racing agents.