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-\keywordsen{Intelligent Driving \ \ Simulation Scenarios \ \ Dangerous Scenario Generation \ \ Automation Technology}
+\keywordsen{Intelligent Driving \ \ Simulation Scenarios \ \ Dangerous Scenario Generation \ \ NSGA-II Multi-objective Optimization}
 \begin{abstracten}
 
-In the development of intelligent driving systems, the generation and optimization of simulation scenarios are crucial for ensuring their safety and reliability. This project reviews the technologies for generating and optimizing dangerous simulation scenarios in intelligent driving. Firstly, based on natural driving data, representative dangerous driving scenarios are identified and extracted, providing a data foundation for the construction of simulation scenarios. Secondly, through multi-dimensional scenario automatic extraction and fusion methods, typical driving scenarios such as line-following, following, and lane-changing are identified and integrated with dynamic driving scenarios to generate more complex and realistic test scenarios. Additionally, to address the issue of insufficient dangerous scenarios in existing test scenarios, a test case generation and enhancement method based on cluster analysis and importance sampling is proposed, which effectively improves the test coverage and efficiency of dangerous scenarios. Finally, an automated simulation testing platform has been developed, enabling the rapid construction of test scenarios, joint invocation of simulation software, and fully automated execution of result analysis and report generation. These methods can significantly improve the safety testing efficiency of intelligent driving systems in simulation environments, providing strong support for the further development of intelligent driving technology.
+In the development of intelligent driving systems, the generation and optimization of simulation scenarios play a vital role in ensuring system safety and reliability. This thesis begins by extracting representative hazardous scenarios from naturalistic driving data to establish a realistic simulation foundation. Through multidimensional scenario fusion techniques, typical driving behaviors such as lane changes, car following, and adjacent vehicle cut-ins are identified and integrated with dynamic traffic elements to produce complex and realistic test cases. To address the insufficient coverage of high-risk scenarios, this work emphasizes the adoption of the NSGA-II multi-objective optimization algorithm. By applying non-dominated sorting and crowding distance mechanisms, NSGA-II balances the conflicting objectives of minimum safety distance and collision risk to extract a Pareto-optimal set of critical scenarios. Experimental comparisons demonstrate that NSGA-II outperforms random search, achieving over 30\% improvement in high-risk scenario coverage. Finally, an automated simulation testing platform is developed to support scenario generation, execution, data logging, and result evaluation, enabling a fully automated and standardized testing workflow. The proposed framework significantly enhances safety testing efficiency and effectiveness in simulated environments, providing robust technical support for autonomous driving technology development.
 
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-\end{abstracten}
+\end{abstracten}