The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high costs in real-world settings and limited immersion in numerical simulations. To address these challenges and facilitate testing in mixed traffic scenarios involving both human-driven vehicles (HDVs) and AVs, we propose a testing and evaluation approach using a driving simulator. Our methodology comprises three fundamental steps. First, we systematically classify scenario elements by drawing insights from the scenario generation logic of the driving simulator. Second, we establish an interactive traffic scenario that allows human drivers to manipulate vehicles within the simulator while AVs execute their decision and planning algorithms. Third, we introduce an evaluation method based on this testing approach, validated through a case study focused on car-following models. The experimental results confirm the efficiency of the simulation-based testing method and demonstrate how car-following efficiency and comfort decline with increased speeds. The proposed approach offers a cost-effective and comprehensive solution for testing, considering human driver behavior, making it a promising method for evaluating AVs in mixed traffic scenarios.

Simulation platforms are often evaluated as isolated teaching tools, although classroom outcomes reflect coordinated technical and instructional elements. This study evaluated a research-team-developed CARLA–SUMO platform as part of a bounded socio-technical instructional system for teaching ramp-merging and weaving analysis. A class-level controlled quasi-experiment involved 174 undergraduates from four intact classes, with 87 students in each instructional configuration. The evaluation combined configuration-level comparisons of formal knowledge, scenario-based analytical performance, and learning-experience profiles; within-system relational analyses in the simulation-supported group; and scenario- and question-level diagnostics. The simulation-supported configuration showed higher post-instruction knowledge ($d=0.425$) and scenario-based analytical performance ($d=0.783$), together with descriptively more positive learning-experience profiles. Perceived learning support was positively associated with cognitive engagement and with learning satisfaction and behavioral intention, while cognitive engagement provided the clearest positive information about scenario performance. Question-level diagnostics identified interacting-behavior identification in Scenario 2 as the clearest candidate for targeted redesign because it combined relatively low absolute performance with a small between-configuration difference. These findings illustrate a three-level approach linking system outputs, within-system relationships, and assessment-informed redesign.