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Automated Driving

机译：自动驾驶

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Using artificial intelligence and machine learning presents an exciting challenge for implementing partially or highly automated driving. The development steps toward SAE levels beyond 2+ may be taken in such a way that by iteratively adding more and more “+” to the 2, level 3 is reached one day without the user noticing. An important approach for research and development in this area is the digital twin as part of the development infrastructure. According to Siemens, this is an indispensable tool for testing and validating safety-critical self-driving functions, among other things. Looking at the current generation of vehicles with comprehensive assistance systems for semi-autonomous driving, there is still quite a way to go before they always deliver full functionality and users trust the functions in every situation. Ultimately, trust in the functions depends on the accident rate being below that of human drivers. Vector Consulting shows how development must be pursued in order to achieve this goal one day. The final element for implementing highly automated driving is the transition from development to real-world operation, states Helge Kiebach of KTI in the interview. In addition to a sufficient and economically viable software and hardware infrastructure in the vehicle, the legal framework and, moreover, the homologation of the permanent modification of the vehicles and their functions must be secured for the entire service life.
机译：利用人工智能和机器学习提出了一个令人兴奋的挑战来实现部分或高度自动驾驶。开发步骤SAE水平超出2 + 5在这样一种方式,通过迭代添加越来越多的“+”的2、3级用户没有注意到有一天。方法的研究和开发面积是数字的双胞胎的一部分发展基础设施。这是一个测试和不可或缺的工具验证安全至上的无人驾驶功能,等等。当前的和全面的车辆半自治的驾驶辅助系统,之前还有很长的路要走总是提供完整的功能和用户的信任在任何情况下的功能。在事故的功能取决于信任率低于人类司机。咨询显示必须追求发展为了实现这一目标的一天。元素实现高度自动化驾驶从发展过渡到现实世界吗操作,Helge Kiebach KTI的州面试。经济上可行的软件和硬件基础设施在车里,合法的此外,框架和的同系化反应的车辆和永久性的修改函数必须为整个服务是安全的的生活。

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