The safety of autonomous driving systems (ADSs) is crucial for the implementation of autonomous vehicles (AVs). Therefore, ADSs must undergo thorough evaluation before being released and deployed publicly. Generating diverse, safety-critical test scenarios is a key task for ADS testing. Existing methods for generating ADS test scenarios include reproducing real-world traffic accidents or using search-based techniques. However, the accident-based scenario often fails to uncover safety violations in ADSs due to the gap between human driving and ADSs. The search-based approach tends to produce scenarios with high similarity because of the limitations of the search algorithm. To address these issues, this study proposes LEADE, a road network modeling-based safety-critical scenario generation and adaptive evolution method for ADSs. Specifically, it constructs abstract scenarios from user test requirements and generates concrete scenarios through road network modeling. LEADE then employs an improved adaptive evolutionary search to generate diverse safety-critical scenarios for testing the ADS. LEADE is implemented and evaluated on an industrial-grade full-stack ADS platform, Baidu Apollo. Experimental results demonstrate that LEADE can effectively and efficiently generate safety-critical scenarios and expose 10 diverse safety violations of Apollo. LEADE outperforms two state-of-the-art search-based ADS testing techniques by identifying 4 new types of safety-critical scenarios on the same roads.