Diagnosing the Sim-to-Real Gap in Latency-Aware Nonlinear Predictive Control: An Experimental Evaluation and Refinement Study on an Autonomous Vehicle

Abstract

This paper presents the design, implementation, and Sim-to-Real evaluation of a Nonlinear Model Predictive Control (NMPC) strategy for autonomous urban shuttles and last-mile transport, focusing on bridging the reality gap. A decoupled NMPC architecture was developed for a by-wire Toyota Prius, explicitly compensating for a measured 100 ms steering actuator delay. The system was first evaluated in the CARLA simulation environment and subsequently deployed on the physical vehicle under matching operational conditions. Real-world trials confirmed consistent transfer of the core control logic, evidenced by a high correlation in steering commands (PearsonCorrelationCoefficient>0.99). However, a systematic Cross-Track Error (CTE) offset (averaging ≈0.20m) was observed. An in-depth gap diagnosis suggests that this tracking discrepancy was primarily driven by real-world yaw angle sensor bias and variability rather than fundamental flaws in the NMPC algorithm itself. Building on this diagnosis, controller refinements—including the use of a path-derived yaw input and an alternative Δu formulation providing integral action—were designed and evaluated within the simulation environment (with physical deployment remaining as future work). In CARLA, these refinements significantly improved tracking accuracy, reducing mean absolute CTE from 0.045m to 0.007m. Finally, a systematic parameter sensitivity analysis in CARLA provides practical tuning guidelines. Overall, the study highlights how high-fidelity simulation can support controller development and diagnostic root cause isolation, while emphasizing the critical interplay between control design, state estimation fidelity, and hardware characteristics in Sim-to-Real deployment.