Design and Knowledge Requirements for Human-Machine Hybrid Intelligence in Autonomous Driving Systems

Abstract

Abstract

Hybrid intelligence in autonomous driving systems can potentially augment human-machine capabilities and lead to better data-driven decision-making. Hence, various decision scenarios, such as route planning and change, can benefit from increased optimization. However, while advancements in research and practice focus on developing technologies for enhancing vehicle autonomy, performance, and algorithms, the requirements for designing such complex hybrid intelligence systems were not found to be elaborated in the existing literature. Accordingly, as part of the 6G Visible project and as a result of expert interviews, this paper proposes a set of design requirements for developing autonomous driving systems with hybrid intelligence. The design requirements cover a range of multi-faceted attributes that should be reflected in the system, including the decision, decision-making process, knowledge, data, human, machine, and decision evaluation considerations. Consequently, a set of knowledge requirements for the system is proposed, from which an ontology can be developed, and the required data can be further determined. Therefore, the design and knowledge requirements contribute to theory by establishing the initial objectives of a design science artifact, which can be developed in future research. Furthermore, they support a more comprehensive and sociotechnical view for the practical development and implementation of hybrid intelligence in autonomous driving systems beyond the prevailing focus on vehicular capabilities.