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This paper addresses the significant sim-to-real gap in autonomous racing by framing it as a full-stack, real-time systems problem, highlighting the challenges posed by dynamics mismatch, estimation delay, and execution-layer latency. By introducing a structured three-layer perspective (Physical/Cyber/Execution), the authors analyze how mismatches propagate through closed-loop feedback and propose diagnostic metrics that extend beyond traditional lap time measurements. The resulting framework not only clarifies failure mechanisms but also offers practical design principles and benchmarking guidelines for developing robust autonomous racing systems under stringent real-time constraints.
Real-time constraints can amplify sim-to-real failures in autonomous racing, but a new structured framework reveals how to mitigate these issues effectively.
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.