解决高速自动驾驶赛车中仿真到现实的落地难题
Bridging the Sim-to-Real Gap under Real-Time Constraints in Autonomous Racing
- 从物理-网络-执行三层分析仿真到现实的误差传播机制
- 提出性能翻转、延迟敏感度等新评估指标，超越单纯圈速
- 适合追求实时性与鲁棒性的自动驾驶系统开发者
自主赛车在高速、小稳定裕度和严格实时约束下暴露了仿真到现实的差距。尽管仿真对开发至关重要，但仿真中表现良好的控制器在真实平台常因动力学不匹配、估计延迟和执行层延迟的相互作用而性能骤降。本文将自主赛车的仿真到现实迁移问题视为一个全栈实时系统问题。我们提出物理/网络/执行三层结构视角，分析误差如何通过闭环反馈传播并放大。引入超越平均圈速的诊断指标，包括性能翻转、稳定性度量、对延迟和噪声的敏感度，以及延迟分布特征。从部署角度出发，提炼出执行感知和延迟感知的设计策略。最后，提出可在计算和时序约束下实现可复现、公平评估的基准测试指南。该框架揭示了跨层失效机理，为接近动态极限运行的可部署自主赛车系统提供了实用设计原则。
原文摘要 · Abstract (English)
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.
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