智能动力平台多型复杂气象模拟仿真测试框架

A Simulation Testing Framework for Intelligent Power Platform under Complex and Diverse Weather Conditions

针对智能动力平台在恶劣复杂气象条件下的仿真测试评价问题,提出了一种基于知识图谱的典型气象-交通要素数字化表征方法,并设计了智能动力平台多型复杂气象模拟仿真框架。首先分析了智能动力平台测试评价的现状和挑战,介绍了现有的智能控制仿真平台及其局限性。然后利用知识图谱结构化和语义化的特性,系统地划分和描述了复杂气象条件和交通场景,为仿真测试提供了数据支撑。最后采用“同图式”的方式,有效地融合和呈现了不同气象条件和交通场景,为智能动力平台在各种复杂情况下的性能评估提供了支撑平台。

The problem of autonomous vehicle testing and evaluating under complex and adverse weather conditions is addressed,and a method based on knowledge graph is proposed to represent and quantify the typical weather-traffic elements,as well as a simulation framework to model and reproduce the complex weather scenarios.The current situation and challenges of autonomous vehicle testing and evaluation are analyzed,the existing simulation platforms and their limitations are introduced.The structural and semantic features of knowledge graph are used to systematically divide and describe the complex weather conditions and traffic scenarios,providing data support for the simulation tests.Finally,a"same-picture"method is adopted to effectively fuse and present different weather conditions and traffic scenarios,providing a platform for the performance evaluation of autonomous vehicles under various complex situations.