The robustness of vision-language agents in real-world environments depends critically on the reliability of their underlying object detectors. Adversarial camouflage has emerged as a promising approach for executing multi-view attacks against these detectors, yet its effectiveness on full-scale, complex real-world objects remains largely unverified. Existing physical validations are predominantly limited to scaled models, leaving a significant gap in understanding real-world threats. Building upon prior digital simulations and scaled-model experiments, this study presents the first systematic quantitative evaluation of full-coverage adversarial camouflage applied to an actual vehicle. We transfer textures generated in the digital domain to a real vehicle and conduct extensive outdoor tests under varying lighting conditions and viewing angles, including aerial perspectives. The attack performance is benchmarked against multiple mainstream detectors. Our results reveal a discrepancy between digital and physical effectiveness. While the camouflage exhibits a measurable attack capability in the physical world, its impact is significantly attenuated by factors including texture transfer loss, environmental interference, and detector robustness. By providing empirical data and a detailed analysis of these limiting factors, this work offers actionable insights for designing more resilient vision-language perception systems against physical-world adversarial threats.