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This research addresses the critical sim-to-real gap in autonomous racing by framing it as a full-stack real-time systems problem involving physical dynamics, cyber logic, and execution latency. The authors propose diagnostic metrics for stability-oriented measures under extreme operating conditions to ensure controllers remain robust when deployed on actual hardware rather than just performing well in simulation.
Bridging the Sim-to-Real Gap under Real-Time Constraints in Autonomous Racing
arXiv:2607.18586v1 Announce Type: new Abstract: 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.
Abstract: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.
Accepted for presentation at the 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall). 6 pages, 2 figures, 3 tables
Robotics (cs.RO)
arXiv:2607.18586 [cs.RO]
(or arXiv:2607.18586v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18586 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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