发表机构
UCF ECE Department（中佛罗里达大学电子与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自主赛车中模拟与现实的差距问题，引入三层视角分析不匹配传播，提出诊断指标、缓解策略和基准测试指南，为可部署自主赛车系统提供故障机制及设计原则。
AI 中文摘要
自主赛车在高速、稳定性裕度小和严格实时约束的极端运行条件下暴露出模拟与现实的差距。虽然模拟对开发不可或缺，但在模拟中表现良好的控制器在物理平台上常因动力学失配、估计延迟和执行层延迟的相互作用而突然性能下降。本文将自主赛车中的模拟到现实转换视为一个全栈实时系统问题。引入结构化三层视角分析不匹配如何通过闭环反馈传播和放大。提出超越标称圈速的诊断指标，从面向部署的角度合成缓解策略，概述基准测试指南。该框架阐明跨层故障机制并为接近动态极限运行的可部署自主赛车系统提供实用设计原则。
英文摘要
Autonomous racing exposes the sim-to-real gap under extreme operating conditions characterized by high speed, tight stability margins, and stringent real-time constraints. Although simulation is indispensable for development, controllers that perform well in simulation often degrade abruptly on physical platforms due to interacting effects of dynamics mismatch, estimation delay, and execution-layer latency. This paper frames sim-to-real transfer in autonomous racing as a full-stack, real-time systems problem. We introduce a structured three-layer perspective (Physical/Cyber/Execution) to analyze how mismatches propagate and amplify through closed-loop feedback. We present diagnostic metrics beyond nominal lap time, including performance flip, stability-oriented measures, sensitivity to delay and noise, and latency distribution characterization. Mitigation strategies are synthesized from a deployment-oriented viewpoint, emphasizing execution-aware and delay-aware design. Finally, we outline benchmarking guidelines that enable reproducible and fair sim-to-real evaluation under compute and timing constraints. The resulting framework clarifies cross-layer failure mechanisms and provides practical design principles for deployable autonomous racing systems operating near dynamic limits.
CommentsAccepted for presentation at the 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall). 6 pages, 2 figures, 3 tables
