论文标题
识别现场数据中的方案，以验证高度自动化的驾驶系统
Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems
论文作者
论文摘要
基于方案的方法来验证高度自动化驾驶功能的方法是基于使用软件式仿真的搜索驾驶方案的安全至关重要特征。此搜索需要有关现实世界流量中场景的形状和概率的信息。这项工作的范围是开发一种方法，该方法可以识别现场数据中重新定义的逻辑驾驶场景，以便随后派生此信息。更确切地说，以流量情景为例开发，实施和验证了一种合适的方法。提出的方法基于场景的定性建模，可以在抽象的字段数据中检测到。通过使用由域模型代表的本体学的通用元素来实现抽象。就给定的应用程序讨论和具体讨论并具体讨论了已经发表的此类抽象的方法。通过检查第一组测试数据，可以证明开发的方法是识别进一步驾驶方案的合适方法。
Scenario-based approaches for the validation of highly automated driving functions are based on the search for safety-critical characteristics of driving scenarios using software-in-the-loop simulations. This search requires information about the shape and probability of scenarios in real-world traffic. The scope of this work is to develop a method that identifies redefined logical driving scenarios in field data, so that this information can be derived subsequently. More precisely, a suitable approach is developed, implemented and validated using a traffic scenario as an example. The presented methodology is based on qualitative modelling of scenarios, which can be detected in abstracted field data. The abstraction is achieved by using universal elements of an ontology represented by a domain model. Already published approaches for such an abstraction are discussed and concretised with regard to the given application. By examining a first set of test data, it is shown that the developed method is a suitable approach for the identification of further driving scenarios.
