Human centric data fusion in vehicular cyber-physical systems

Building effective Vehicular Cyber-Physical Systems (VCPS) to improve road safety is a non-trivial challenge, especially when we examine how the driver benefits from the existing and proposed technologies in the presence of Human Factors (HF) related negative factors such as information overload, confusion, and distraction. In this paper, we address a human-centric data fusion problem in VCPS. To the best of our knowledge, this work is the first to apply HF to the data fusion problem, which has both theoretical value and practical implications. In particular, we present a new architecture by defining a distinct High-Level (HL) data fusion layer with HF considerations, that is placed between the safety applications on the VCPS and the human driver. A data fusion algorithm is proposed to fuse multiple messages (based on reaction time, message type, preferred evasive actions, severity of the hazards, etc) and to maximize the total utility of the messages. The algorithm is tested with real human drivers to demonstrate the potential benefit of incorporating such human-centric fusion in existing warning systems.