This paper presents an approach for allocation of resources in an automotive service-oriented software architecture. Using mathematical optimization, we assign computational resources of an automotive compute cluster to a set of software services. Additionally, scheduling parameters of services are optimized under the consideration of dependencies between data flows and computations within services. The optimization minimizes power consumption and the maximum execution times of critical effect chains in a multi-objective optimization problem. The evaluation investigates the achievable reduction in power consumption using an exemplary system. Furthermore, we demonstrate a sharp reduction in maximum execution times of effect chains that span multiple services and ECUs.

This paper demonstrates a system comprised of infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the system to recognize our beacons in both daytime and nighttime conditions. High precision localization is a key enabler for automated vehicles but remains unsolved, despite strong recent improvements. Our low-cost, infrastructure-based approach is a potential step towards solving the localization problem. All datasets are made available here https://embedded.rwth-aachen.de/doku.php?id=forschung:mobility:infralocalization:concept.

Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment.