Architectural Framework for IoT Driven Data Acquisition and Transmission for Dynamic Assessments of Personal Driving Risk Indicator

Connected and autonomous vehicles can potentially increase traffic safety by using various information and communication technologies (ICT). Data collected using technologies such as the Internet of Things (IoT) enables better traffic safety based on specific safety indicators. Modeling these indicators implies considering traditional traffic components such as driver-vehicle-road-environment. Eventually, if expressed in a suitable aggregate manner, traffic safety indicators can be presented and displayed to drivers to increase their attention and influence them to make decisions to avoid and mitigate traffic incidents. Existing driving risk assessment models usually consider a limited set of indicators related to individual drivers and their psycho-physical abilities which are important for participation in traffic. Data collected using IoT infrastructure alongside distributed computing and cloud technologies enables an expanded set of traffic safety indicators and a better assessment of driving risk. In this study, the common driver-vehicle-road-environment traffic safety indicators were considered and extended with the same indicators collected from neighboring drivers, weather conditions, surrounding awareness, and driver behavior data. We propose a novel architectural framework to provide dynamic driving risk assessment based on data collected using IoT technologies. The architectural framework provides a foundation for efficient data transmission between multiple sources and their processing, thus enabling the prediction of personal driving risk indicators.