Five existing definitions of autonomous driving test scenario were expounded, and the definitions of autonomous driving test scenario and related concepts were proposed by combining the logical relationships between test scenario, primitive scenario, and scenario elements. Three test scenario architectures of autonomous driving that are recognized in the industry were compared. From the perspective of scenario data sources, the current situation in the collection and research of traffic accident and naturalistic driving data at home and abroad was summarized. Based on the known data, expert data, test requirements, test objects, and technical characteristics of autonomous driving, the research results on the construction and automatic generation of unknown autonomous driving test scenarios were summarized. Research results show that the definition and architecture of autonomous driving test scenario are closely related to its construction and automatic generation. The autonomous driving scenario can be considered as an organic combination and comprehensive reflection of scenario elements, such as the driving environment, traffic participants, and driving behavior of autonomous vehicle. In addition to these elements, autonomous driving test scenarios should include a dynamic semantic description of initial state of the scenario, the situation of the scenario, and the impact and results at the end of the scenario. Although the existing test scenario architecture is relatively perfect, it is difficult to meet the requirements of different test objectives and test methods. Therefore, in the optimization of test scenario architecture, the design process of test scenario should be considered. However, the collection accuracy and effective characteristics of traffic accident data are not uniform, which makes it difficult to achieve the complete collection of naturalistic driving scenario data, and the collection specifications are not standardized. Therefore, the effectiveness of traffic accident and naturalistic driving data for the construction of autonomous driving test scenarios must be further demonstrated, and the autonomous driving test data are expected to become an important supplement. Improving scenario coverage and accelerating the testing process are important research goals in the construction of autonomous driving test scenarios. The in-depth application of artificial intelligence technology in the field of autonomous driving scenario generation is expected to meet complete or high coverage requirements of test scenarios. The classification of test scenarios for different levels of autonomous driving and the test scenario construction method for accelerated test of autonomous driving will be the next important research directions in test scenario construction for autonomous driving. 7 figs, 103 refs.