智能驾驶系统测试评价作为智能网联汽车研发的重点，需要重点关注车辆在复杂气象和复杂交通流场景中的真实性能表现。该研究提出一种基于天气复杂度和交通复杂度的复杂场景构建方法，用于满足复杂交通环境导航智能驾驶的测试需求。基于中国大型实车路试项目（China-FOT）自然驾驶数据，分析车辆速度、纵向加速度、横向加速度、横摆角速度等车辆动力学参数，通过拟合安全边界包络线构建驾驶行为风险等级，筛选提取自然驾驶危险工况，用于明确导航智能驾驶功能安全相关的基本场景类型，通过基于多动态目标物基础场景关联特征组合的交通交互行为耦合方法构建复杂场景类型；基于量化的自然天气因素，通过自然驾驶行为特征分布，构建光照因素、降雨因素、雾气因素等影响指标表征天气复杂度；基于信息熵理论，通过支持向量机方法和K-折交叉验证方法，构建相遇角度、相对距离、相对速度等复杂度参数，用于表征复杂场景的交通状态；针对复杂场景开展封闭场地实车试验，通过真实的测试性能评价参数得到测试场景的复杂度，验证复杂场景的合理性。为导航智能驾驶功能构建能够表征真实复杂交通环境的测试场景，为智能网联汽车智能驾驶系统的优化迭代提供支持。

As the focus of research and development of intelligent connected vehicles, the test and evaluation of autonomous driving systems must focus on the real performance of vehicles in complex weather and complex traffic flow scenarios. This research proposed a method for constructing complex scenarios based on weather complexity and traffic complexity to meet the testing requirements of intelligent driving systems in challenging traffic environments. Using natural driving data from China’s large-scale field operational test project (China-FOT), the study analyzed vehicle dynamics parameters such as speed, longitudinal acceleration, lateral acceleration, and yaw rate. By fitting safety boundary envelopes, driving behavior risk levels were defined, and hazardous scenarios in natural driving were identified. These scenarios help clarify the fundamental scene types related to the functional safety of navigation-based intelligent driving. A traffic interaction coupling method, incorporating multiple dynamic target features, was applied to construct complex scenario types. The quantified natural weather factors were used to construct influence indicators, such as light factor, rainfall factor, fog factor, which are employed to characterize the weather complexity through the distribution of natural driving behavior characteristics. The complexity parameters, including encounter angle, relative distance, relative speed, were constructed using the Support Vector Machines and K-fold cross validation methods to characterize the traffic state of the complex scenarios. In order to ascertain the complexity of the test scenario, a closed field vehicle test was conducted, during which the real test performance evaluation parameters were employed to verify the rationality of the complex scenario construction. This research indicates the necessity to construct a test scenario that can accurately portray the real-world complex traffic environment for the navigation pilot driving functions. This will facilitate the optimization and iteration of the autonomous driving system of intelligent connected vehicles.