Update abstracten.tex

critical/undergraduate/content/abstracten.tex

@@ -1,9 +1,7 @@
 %!TEX root = ../csuthesis_main.tex
-\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.
 
 \end{abstracten}

critical/undergraduate/content/abstractzh.tex

@@ -1,11 +1,10 @@
 %!TEX root = ../csuthesis_main.tex
 % 设置中文摘要
-\keywordscn{智能驾驶\quad 仿真场景\quad 危险场景生成\quad 自动化技术}
+\keywordscn{智能驾驶\quad 仿真场景\quad 危险场景生成\quad NSGA-II 多目标优化}
 %\categorycn{TP391}
 \begin{abstractzh}
 
-
-在智能驾驶系统的发展过程中，仿真场景的生成与优化是确保其安全性和可靠性的重要手段。本项目综述了智能驾驶危险仿真场景的生成和优化技术。首先，基于自然驾驶数据，识别并提取出具有代表性的危险驾驶场景，为仿真场景的构建提供了数据基础。其次，通过多维场景自动提取和融合方法，识别出典型的行车场景，如巡线、跟车、邻车切入等，并将其与动态驾驶场景进行融合，以生成更为复杂和真实的测试场景。此外，针对现有测试场景中危险场景数量不足的问题，提出了一种基于聚类分析和重要性采样的测试用例生成和增强方法，有效提高了危险场景的测试覆盖率和测试效率。最后，开发了一种自动化仿真测试平台，实现了测试场景的快速构建、仿真软件的联合调用以及结果分析与报告生成的全自动化执行。通过这些方法，能够显著提高智能驾驶系统在仿真环境中的安全测试效率，为智能驾驶技术的进一步发展提供了有力支持。
+在智能驾驶系统的发展过程中，仿真场景的生成与优化是保障其安全性和可靠性的关键技术手段。本文首先基于自然驾驶数据提取具有代表性的危险场景，构建真实有效的仿真数据基础；随后，采用多维场景融合方法识别典型行车行为（如变道、跟车、邻车切入等），并与动态交通要素结合，生成更加复杂和真实的测试场景。针对现有测试环境中高风险场景覆盖率不足的问题，本文重点引入 NSGA-II 多目标优化算法，通过非支配排序与拥挤度计算，在“最小安全距离”和“碰撞风险”两个目标之间实现 Pareto 最优平衡，有效筛选出多样且具有代表性的高危场景。实验结果表明，与传统随机搜索方法相比，NSGA-II 可将高风险场景覆盖率提高 30\% 以上。最后，设计并实现了一套自动化仿真测试平台，集成场景生成、仿真执行、数据采集与结果分析功能，实现测试流程的自动化和标准化。本文方法显著提升了智能驾驶系统在仿真环境中的安全性验证能力，为未来自动驾驶系统的开发与测试提供了有力技术支撑。
 
 
 \end{abstractzh}

critical/undergraduate/content/acknowledgements.tex

@@ -1,7 +1,7 @@
 %!TEX root = ../csuthesis_main.tex
 \begin{acknowledgements} 
-
-写到这里这篇本科论文就算完成了，回想起撰写论文这段时光心里全是感慨和感激，我要用最真诚的文字向所有帮过我的人表达深深谢意。首先要向我的论文导师表达最崇高的敬意和最诚挚的感谢，从对论文选题感到迷茫到梳理ChatScene框架设计思路，从探讨ASIL - Gen优化算法到分析实验结果时遇到困惑，老师一直凭借渊博专业知识、严谨治学态度和耐心细致指导为我拨开迷雾。每次组会的讨论内容和每份修改意见的批注都凝聚着老师的心血，老师不光教会我学术研究的方法，还以实际行动诠释了精益求精的科研精神，这些会成为我未来学习和工作的宝贵财富。感谢学院各位老师，课堂上你们生动讲解让我对自动驾驶领域有更深入理解，在论文开题、中期检查等环节你们提出的建设性意见帮我不断完善研究内容。特别感谢实验室的师兄师姐和同学们，我遇到技术难题时你们慷慨分享经验陪我调试CARLA仿真平台、优化NSGA - II算法代码，我倍感压力时你们的鼓励和陪伴让我重新找回信心，和你们一起奋斗的日子是我本科生涯最难忘的回忆。还要感谢我的家人，是你们在背后默默支持让我能心无旁骛投入论文研究，每当我因实验不顺利而焦虑时你们的理解与安慰给予我温暖和力量，你们的关爱与鼓励是我前行路上最坚实的后盾。
-最后，感谢参与的所有老师与同学，以及为论文提供数据和技术支持的机构。虽然论文仍存在不足之处，但这段经历让我收获颇丰。未来，我将带着这份感恩之心，继续在学术道路上探索前行。
-
+	
+	写到这里这篇本科论文就算完成了，回想起撰写论文这段时光心里全是感慨和感激，我要用最真诚的文字向所有帮过我的人表达深深谢意。首先要向我的论文导师表达最崇高的敬意和最诚挚的感谢，从对论文选题感到迷茫到梳理ChatScene框架设计思路，从探讨ASIL - Gen优化算法到分析实验结果时遇到困惑，老师一直凭借渊博专业知识、严谨治学态度和耐心细致指导为我拨开迷雾。每次组会的讨论内容和每份修改意见的批注都凝聚着老师的心血，老师不光教会我学术研究的方法，还以实际行动诠释了精益求精的科研精神，这些会成为我未来学习和工作的宝贵财富。感谢学院各位老师，课堂上你们生动讲解让我对自动驾驶领域有更深入理解，在论文开题、中期检查等环节你们提出的建设性意见帮我不断完善研究内容。特别感谢实验室的师兄师姐和同学们，我遇到技术难题时你们慷慨分享经验陪我调试CARLA仿真平台、优化NSGA - II算法代码，我倍感压力时你们的鼓励和陪伴让我重新找回信心，和你们一起奋斗的日子是我本科生涯最难忘的回忆。还要感谢我的家人，是你们在背后默默支持让我能心无旁骛投入论文研究，每当我因实验不顺利而焦虑时你们的理解与安慰给予我温暖和力量，你们的关爱与鼓励是我前行路上最坚实的后盾。
+	最后，感谢参与的所有老师与同学，以及为论文提供数据和技术支持的机构。虽然论文仍存在不足之处，但这段经历让我收获颇丰。未来，我将带着这份感恩之心，继续在学术道路上探索前行。
+	
 \end{acknowledgements}