Validation of autonomous vehicles (AVs) is a critical task in their development and for their approval on public roads. Scenario-based testing is the state-of-the-art validation method and is recommended by international automotive regulators. While simulated execution of critical scenarios is essential, it cannot yet fully replace real-world testing, which however remains tedious, time-consuming, and resource-intensive. In this work, we propose an enhanced methodology using Augmented Reality to bridge the gap, providing an intermediate testing method that enables comprehensive real-world testing with reduced cost and improved realism. To demonstrate this methodology, we conducted tests in a controlled environment using six critical scenarios selected from road crash studies.