arXiv：cs.LG· Efe \c{C}ang{\i}r{\i}l{\i}, Murat Kurt·· 4 小时前AI 评分29
TRACK：模拟赛车游戏中的遥测分析与驾驶指导工具包
TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games
AI 导读
研究提出 TRACK 框架，将模拟赛车单次驾驶记录表示为速度、刹车、策略、一致性四维行为空间中的紧凑几何指纹，并用无监督聚类按相似度分组。框架在 Assetto Corsa Gym（ACGym）数据集上开发，每个聚类结果均与随机基线校准，无法区分时明确说明。研究提示弯道类型沿未参与定义的 behavioral 维度存在差异，换车后仅速度和一致性可迁移，刹车与策略维度的可重复性未能得到证明。
正文
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.
| Comments: | 29 pages, 9 figures, 8 tables |
| Subjects: | Graphics (cs.GR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| MSC classes: | 68-XX |
| ACM classes: | I.3.0; I.3.m |
| Cite as: | arXiv:2610.10061 [cs.GR] |
| (or arXiv:2610.10061v1 [cs.GR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10061 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Murat Kurt [view email]
[v1]
Wed, 7 Oct 2026 13:29:45 UTC (4,008 KB)
来源：arXiv：cs.LG · arxiv.org