Risk Assessments Integrating V2X Into Cooperative Driving Automation

Cooperative Driving Automation (CDA) and Vehicle to Everything (V2X) paradigms have been developed to address the limitations single-vehicle perception and decision-making capabilities across different Levels of Driving Automation [1], [2]. V2X encompasses a combination of technologies embedded into vehicles, infrastructure, and even pedestrians, relying on a combination of sensor data, wireless connectivity networks, and real-time calculations to enhance the driver's and the vehicle's perception, localization, and planning tasks [3]. While significant research efforts have been made towards designing and implementing V2X-enabled functions and cooperative driving technologies, many challenges must still be addressed. In particular, there is a need for systematic approaches to determine the safety impact and risk reduction benefits, including key aspects of hardware, software, and human reliability, connectivity, and improved methods to assess traffic and safety impacts. This work presents an initial approach to perform risk assessments of V2X technologies based on the Hybrid Causal Logic (HCL) framework. This approach draws from novel hazard identification methodologies to define agents, critical events, and key tasks, failures, and errors. The methodology incorporates the effect driver-system team models [4], [5] on the overall system's safety, as well as enhancing context representation to consider effects of road types, weather conditions, traffic levels, and technology effectiveness under limited data availability. An example application for cooperative collision avoidance is presented together with a discussion on data sources and potential data collection efforts to strengthen PRA-based analysis of V2X technologies.