Towards Data-Centric and Context-Aware Decision Making in Software-Defined Vehicles

As vehicular computing environments move towards software-defined paradigms and microservice architectures, more data becomes available from the in-vehicular sensors for various applications to be developed. Combining such local vehicular information with feedback from drivers and passengers, the environment, and external data sources opens new avenues for application development and enchanting vehicular capabilities, services, and systems. However, in managing more data effectively, reliably, securely, and privately, more focus is given to data analysis, machine learning, and artificial intelligence tasks and how they are integrated into vehicle-cloud architectures. This paper considers a vision for a data-centric architecture that utilizes open-source building blocks. We aim to bring together data from humans, vehicles, and environments to support novel application development and context-aware decision-making.