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A highly automated vehicle (HAV) is a safety-critical system. Therefore, a verification and validation (V&V) process that rigorously evaluates the safety of HAVs is necessary before their mass deployment on public roads. This dissertation will present the methodology and implementation procedure of a scenario-based evaluation framework for HAVs. First, an evaluation framework for reactive scenarios is proposed, where the risk level of test cases could be objectively categorized in advance. The pedestrian crossing scenario is used as a case study. We first build a statistical model for the pedestrian scenario based on naturalistic data. Next, reachability analysis is applied to partition the scenario testing space into different risk level sets, which are then combined with importance sampling to generate test cases efficiently and realistically. The proposed method achieves unbiased crash rate estimation in an accelerated fashion, while all the test cases are feasible and have controlled risk levels. Then, a novel evaluation framework for interactive scenarios is proposed, including highway merging and roundabout entering. Instead of assuming that the primary other vehicle (POV) takes predetermined maneuvers, we model the POVs as game-theoretic agents. To capture a wide variety of interactions between the POV and the vehicle under test (VUT), we use level-k game theory and the social value orientation (SVO) concept to model the POV, and generate a diverse library of POV policies using reinforcement learning. On the other hand, an adaptive test case generation method is developed based on adaptive sampling, stochastic optimization and …