Advances in the field of environment perception for automated agents have resulted in an ongoing increase in generated sensor data. The available computational resources to process these data are bound to become insufficient for real-time applications. Reducing the amount of data to be processed by identifying the most relevant data based on the agents' situation, often referred to as situation-awareness, has gained increasing research interest, and the importance of complementary approaches is expected to increase further in the near future. In this work, we extend the applicability range of our recently introduced concept for situation-aware environment perception to the decentralized automation architecture of the UNICARagil project. Considering the specific driving capabilities of the vehicle and using real-world data on target hardware in a post-processing manner, we provide an estimate for the daily reduction in power consumption that accumulates to 36.2%. While achieving these promising results, we additionally show the need to consider scalability in data processing in the design of software modules as well as in the design of functional systems if the benefits of situation-awareness shall be leveraged optimally.
翻译:由于自动化剂环境认识领域的进展,生成的感官数据不断增多;处理这些数据的现有计算资源必然不足以实时应用;根据物剂的状况确定最相关的数据(通常称为对情况的认识),减少需要处理的数据数量已引起越来越多的研究兴趣,补充方法的重要性预期在不久的将来会进一步增加;在这项工作中,我们将我们最近引进的环境认识概念的应用范围扩大到UNICARagil项目分散的自动化结构;考虑到车辆的具体驱动能力,并以后处理方式在目标硬件上使用真实世界数据,我们估计每日耗电量将累积到36.2%;在取得这些有希望的结果的同时,我们还表明,如果能够最佳地利用情况认识的好处,就需要考虑在设计软件模块时以及在设计功能系统时,在数据处理方面考虑可扩缩性。