Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.10061

TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games

Efe Çangırılı·Murat Kurt
View original source ↗Read source document ↗Export metadata ↓

Abstract

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 the wheel. We report this framework together with its limitations: we calibrate each clustering result against a null, and when one does not separate from chance, we say so. Instead of restricting ourselves to scoring drivers or sorting them into preset labels, we represent each recording session as a compact geometry in a four-dimensional behavioral space (speed, braking, strategy, and consistency), and we group these fingerprints by their similarity using unsupervised clustering. Over time, we have developed and refined this framework on the open Assetto Corsa Gym (ACGym) dataset. Our study suggests that corner types differ along a behavioral dimension that was not used to define them. It also suggests that when the car changes, only speed and consistency carry over in the restricted population, while repeatability could not be shown there for any of the braking or strategy measures. Cluster separation becomes less distinct as the range of available telemetry widens. Until that repeatability is shown, grouping on the braking and strategy dimensions cannot treat the car as interchangeable, which divides an already small sample into smaller cells. It is also not clear whether a driver's grouping carries over from one corner type to the next. We also normalize each metric against a reinforcement-learning reference agent. The reference does not depend on the sample, so the scale does not shift when the sample does. We intend these results as an analytical foundation for a personalized improvement suggestion system. The sample is small. The cross-car result changes when the sample is defined more broadly. These outcomes are preliminary.

Explore related subjects

cs.GRcs.AIcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Efe Çangırılı, Murat Kurt. 2026-10-07. TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games. https://arxiv.org/abs/2610.10061

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editability

Sketch constraints specify geometric conditions for constructing and modifying parametric CAD models. We study whether predicted constraints allow a given history to reproduce the required initial model and support prescribed dimensional edits. We introduce HistCAD, an executable representation and dataset whose Academic and Industrial collections contain 180,495 parametric construction histories with entity-referenced sketch constraints and retained feature operations. Predictors receive these histories with the geometry and feature definitions retained and explicit sketch constraints removed. They generate constraints for every sketch without seeing the edit request. The benchmark compares models built with alternative constraint sets for the same history under the same dimensional edit. An edit succeeds when the model reproduces the required initial geometry, reaches the target value, preserves specified relations and unedited dimensions in the target sketch, and rebuilds through the complete history. A predictor trained on both collections and supplied with descriptions of the input histories achieves overall edit success of 52.4% on Academic and 29.0% on Industrial. Models retaining only endpoint-connectivity constraints in the target sketch and the history's constraints elsewhere can reach the target and rebuild while failing preservation. For all-sketch predictions, we retain the target-sketch prediction and restore the history's constraints in other sketches. More models then reproduce the required initial geometry, and some of these newly matched models complete the edit. HistCAD connects constraint learning to the construction and revision of parametric CAD models.

cs.GR↗
arXiv2026

Event-T2M: Event-level Conditioning for Complex Text-to-Motion Synthesis

Text-to-motion generation has advanced with diffusion models, yet existing systems often collapse complex multi-action prompts into a single embedding, leading to omissions, reordering, or unnatural transitions. In this work, we shift perspective by introducing a principled definition of an event as the smallest semantically self-contained action or state change in a text prompt that can be temporally aligned with a motion segment. Building on this definition, we propose Event-T2M, a diffusion-based framework that decomposes prompts into events, encodes each with a motion-aware retrieval model, and integrates them through event-based cross-attention in Conformer blocks. Existing benchmarks mix simple and multi-event prompts, making it unclear whether models that succeed on single actions generalize to multi-action cases. To address this, we construct HumanML3D-E, the first benchmark stratified by event count. Experiments on HumanML3D, KIT-ML, and HumanML3D-E show that Event-T2M matches state-of-the-art baselines on standard tests while outperforming them as event complexity increases. Human studies validate the plausibility of our event definition, the reliability of HumanML3D-E, and the superiority of Event-T2M in generating multi-event motions that preserve order and naturalness close to ground-truth. These results establish event-level conditioning as a generalizable principle for advancing text-to-motion generation beyond single-action prompts.

cs.GR↗
arXiv2026

SECA: Strict-Elastic Compact Activation for Differentiable Elastoplastic Deformation

Return mapping keeps an exact elastic domain but switches its response tangent at yield, while smoothing laws with a sub-yield tail admit plastic flow below the threshold. We present SECA, a Strict-Elastic Compact Activation method for differentiable elastoplastic simulation. SECA prescribes a bounded $C^2$ activation over a finite positive-overstress interval and integrates it into a Perzyna-type flow law, so the implicit update returns exactly zero plastic increment for sub-yield trials while smoothing the onset. For scalar and proportional isotropic $J_2$ updates we derive an exact decomposition of the plastic-increment discrepancy into finite-width and viscosity contributions, and an explicit algebraic budget for the transition width. The update is embedded in finite-deformation loading histories with contact and complete unloading, and differentiated to released equilibria. Controlled indentation results stay closely matched. In the tested coarse cube-indentation run, where the transition is crossed without intermediate increments, SECA reduces whole-run Newton iterations by 47% and backtracking halvings by 93% against return mapping. A four-method comparison evaluates released-shape error and computational cost across loading partitions, and on a contact scene the path derivative drives a bounded control task to its target in three iterations. Further examples show elastic recovery and residual deformation under compression, tension and bending.

cs.GR↗