Autonomous driving functions (ADFs) in public traffic have to comply with complex system requirements that are based on knowledge of experts from different disciplines, e.g., lawyers, safety experts, psychologists. In this paper, we present a research preview regarding the validation of ADFs with respect to such requirements. We investigate the suitability of Traffic Sequence Charts (TSCs) for the formalization of such requirements and present a concept for monitoring system compliance during validation runs. We find TSCs, with their intuitive visual syntax over symbols from the traffic domain, to be a promising choice for the collaborative formalization of such requirements. For an example TSC, we describe the construction of a runtime monitor according to our novel concept that exploits the separation of spatial and temporal aspects in TSCs, and successfully apply the monitor on exemplary runs. The monitor continuously provides verdicts at runtime, which is particularly beneficial in ADF validation, where validation runs are expensive. The next open research questions concern the generalization of our monitor construction, the identification of the limits of TSC monitorability, and the investigation of the monitor's performance in practical applications. Perspectively, TSC runtime monitoring could provide a useful technique in other emerging application areas such as AI training, safeguarding ADFs during operation, and gathering meaningful traffic data in the field.
翻译:公共交通中的自主驾驶功能(ADFs)必须符合基于不同学科的专家,例如律师、安全专家、心理学家等专家知识的复杂系统要求。我们在本文件中介绍了对ADF要求的验证的研究预览;我们调查了交通序列图是否适合将此类要求正规化,并提出了在验证运行期间监测系统遵守情况的概念;我们发现,TSC及其直观的视觉合成对交通领域标志的视觉合成,是合作规范此类要求的有希望的选择。例如,TSC,我们描述根据我们的新概念建造运行时间监测器,利用TSC的空间和时间方面的分离,成功地将监测器用于模拟运行。监测器不断在运行时做出判断,这对ADF的验证过程特别有益,在验证运行过程中,我们监测器的常规构造、确定TSC可监测的限度,以及调查在实际应用中监测运行的运行情况,例如AISC的操作中,可提供有意义的数据操作。