<p>Simulated scenario-based test on Highly Automated Vehicles (HAVs) has been widely-received to ensure HAVs’ safety as a solution to the problem brought on by mileage-based road testing. An appropriate scenario library is an important guarantee for the reliability of test results and test efficiency. Most scenario datasets opened to public contain redundant scenarios, making the required testing resource intractable. Testers need to design condensed scenario libraries based on existing datasets to expedite testing. The difficulty lies in how to measure the similarity of scenarios and the reliability of test results. In response to these problems, a framework for establishing and validation of the condensed scenario library based on double-C (i.e., Conciseness and Consistence) principle is proposed. A Deep Temporal Clustering (DTC) algorithm, which reduces the dimensionality of scenario data using an autoencoder, is developed and combined with screening criteria to ensure the conciseness of the scenario library. In the case of the HighD dataset, all typical scenarios are found and the number of test-worthy scenarios is reduced by 59%. The OnSite Autonomous Driving Algorithm Challenge was hosted to verify the consistency of the scenario library in terms of test results, based on which 312 Planning and Control (PNC) algorithms were collected. A condensed scenario library is established based on the OnSite scenario library. There is no discernible change in the scenario library's difficulty level, with no significant difference in SUTs’ scores or the score difference between SUTs. It is proved that the condensation method ensures conciseness and consistency of the testing scenario library.</p>