"Siemens 2019:实现大规模自动驾驶仿真"
版权申诉
67 浏览量
更新于2024-03-04
收藏 6.57MB PDF 举报
ehicle Co-simulations with SCALEXIO
In the document "Enabling simulation at scale", Siemens PLM Software discusses the importance of large-scale simulation for the development and testing of autonomous driving technology. The company highlights the collaboration with Microsoft to enable such simulations, using Siemens' SCALEXIO platform.
The document emphasizes the need for realistic and comprehensive simulations in order to ensure the safety and reliability of autonomous vehicles. By combining the strengths of Siemens' simulation technology with Microsoft's cloud computing capabilities, the two companies are able to create a scalable and efficient simulation environment for automotive engineers.
One key aspect of this collaboration is the ability to perform co-simulations between advanced driver assistance systems (ADAS) and autonomous driving (AD) functions using the SCALEXIO platform. This allows engineers to test and validate the interaction between different systems in a virtual environment, before moving on to physical testing on the road.
The document goes on to explain how Siemens PLM Software's simulation tools can be integrated with Microsoft's cloud services to enable simulations at a scale that was previously not possible. By leveraging the power of the cloud, engineers can run large numbers of simulations in parallel, drastically reducing the time and cost of development.
Overall, the collaboration between Siemens PLM Software and Microsoft represents a significant step forward in the field of autonomous driving technology. By enabling large-scale simulations, engineers are able to test and refine their systems more thoroughly than ever before, ultimately leading to safer and more reliable autonomous vehicles on the road.
2020-03-03 上传
2019-11-28 上传
2019-09-23 上传
2021-09-20 上传
2021-08-29 上传
2021-08-29 上传
2021-06-30 上传
2021-07-26 上传
2021-09-25 上传
samLi0620
- 粉丝: 1414
- 资源: 1万+
最新资源
- Angular程序高效加载与展示海量Excel数据技巧
- Argos客户端开发流程及Vue配置指南
- 基于源码的PHP Webshell审查工具介绍
- Mina任务部署Rpush教程与实践指南
- 密歇根大学主题新标签页壁纸与多功能扩展
- Golang编程入门:基础代码学习教程
- Aplysia吸引子分析MATLAB代码套件解读
- 程序性竞争问题解决实践指南
- lyra: Rust语言实现的特征提取POC功能
- Chrome扩展:NBA全明星新标签壁纸
- 探索通用Lisp用户空间文件系统clufs_0.7
- dheap: Haxe实现的高效D-ary堆算法
- 利用BladeRF实现简易VNA频率响应分析工具
- 深度解析Amazon SQS在C#中的应用实践
- 正义联盟计划管理系统:udemy-heroes-demo-09
- JavaScript语法jsonpointer替代实现介绍