TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
This paper presents TRACK (Telemetry-Based Racing Analysis and Coaching Kit), which is a framework for analyzing driving performance in sim racing and profiling how individual drivers behave behind th...
arXiv:2606. 12616v1 Announce Type: new Abstract: Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode.
arXiv:2607. 06495v1 Announce Type: cross Abstract: Live sports commentary is grounded generation under a deadline: statements concern real, named athletes, the grounding state changes every few seconds, and no reference text exists at generation time.
arXiv:2607. 26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams.
The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.
The paper proposes a method for verifying whether two gameplay replays in Counter‑Strike 2 belong to the same player by extracting a behavioral fingerprint that captures crosshair control, movement, economy, combat, and rhythm. Using a six‑fold evaluation on two datasets (Perfect and Professional), the pairwise model achieves ROC AUCs of 0.926 and 0.956, with aiming and low‑level mechanics providing the strongest identity signals. Aggregating evidence across multiple historical demos further improves account‑history AUC, reaching 0.982 on Perfect and 0.975 on Professional.