CAPE: Control Algorithm Performance Evaluation under Learned Vehicle Dynamics Models

Abstract

We propose the Control Algorithm Performance Evaluation (CAPE) framework, a systematic methodology for benchmarking racing controllers under our proposed learned enhanced physics model (EPM). The proposed framework enables cross-controller comparison by evaluating five closed-loop control architectures. We further compare our proposed EPM with two state-of-the-art learned vehicle dynamics models: Deep Pacejka Model (DPM) and Deep-learning Dynamics Model (DDM). Closed-loop experiments show that across all models and controllers, the proposed EPM achieves best average lap times. Specifically, the Adaptive NMPC with EPM achieves a time of 5.82 s, compared with 12.99 s for DPM and 8.80 s for DDM, while simultaneously producing substantially lower longitudinal and lateral tracking errors under identical controller configurations. We further evaluate all three models and five controllers using a disturbance-aware simulation framework incorporating measurement noise, process disturbances, actuator delay, and parametric uncertainty. Under moderate global disturbance scaling factor (η = 1), results averaged across the five controllers show that EPM reduces a) longitudinal tracking error by 29.0% and 17.2%; b) lateral tracking error by 24.6% and 12.3%; c) while increasing average velocity magnitude by 39.9% and 3.1% relative to DPM and DDM, respectively. Overall, CAPE establishes a systematic benchmark for evaluating the performance of learned vehicle dynamics models in a closed-loop control framework and demonstrates that our proposed EPM significantly improves controller robustness and performance under realistic uncertainties.