发表机构
Ege University; International Computer Institute, Ege University（爱琴海大学; 爱琴海大学国际计算机研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TRACK框架，利用四维行为空间和无监督聚类分析模拟赛车驾驶表现，并针对参考智能体归一化，为个性化改进建议奠定分析基础。
AI 中文摘要
本文介绍了TRACK（基于遥测的赛车分析与教练套件），这是一个用于分析模拟赛车驾驶表现并刻画个体驾驶员在方向盘后行为特征的框架。我们报告了该框架及其局限性：我们将每个聚类结果与一个零模型进行校准，当聚类结果无法与随机性区分时，我们会明确说明。我们并不局限于为驾驶员打分或将其归入预设标签，而是将每次记录会话表示为四维行为空间（速度、制动、策略和一致性）中的一个紧凑几何形状，并通过无监督聚类根据这些指纹的相似性进行分组。随着时间的推移，我们在开源的Assetto Corsa Gym（ACGym）数据集上开发并完善了这一框架。我们的研究表明，弯道类型在某个未用于定义它们的行为维度上存在差异。研究还表明，当车辆发生变化时，在受限人群中只有速度和一致性得以延续，而制动或策略指标的可重复性未能得到证实。随着可用遥测范围的扩大，聚类分离度变得不那么明显。在可重复性得到证实之前，基于制动和策略维度的分组不能将车辆视为可互换的，这会将本已较小的样本进一步分割成更小的单元。此外，驾驶员的分组是否能在不同弯道类型之间延续也不明确。我们还针对一个强化学习参考智能体对每个指标进行了归一化处理。该参考不依赖于样本，因此当样本变化时，尺度不会随之改变。我们打算将这些结果作为个性化改进建议系统的分析基础。样本规模较小。当样本定义更广泛时，跨车辆的结果会发生变化。这些结果是初步的。
英文摘要
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.
Comments29 pages, 9 figures, 8 tables
