Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2207.01896
