Project

Modeling Uncertainty for Safer Cooperative Mobility Simulations investigates how uncertainty in communication, positioning, and sensing influences traffic simulation results. Real-world  mobility systems are affected by delays, signal loss, and positioning errors that are often idealized or omitted in current simulations. By introducing and quantifying these uncertainties, the project aims to improve the realism and safety relevance of mobility simulations.

This is particularly important for evaluating cooperative systems such as Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Assist (GLOSA), where small deviations in timing or location can significantly impact vehicle behavior and safety margins. The overarching goal is to establish a structured approach for incorporating uncertainty models into traffic simulations, enabling more accurate safety assessments and scenario design for connected vehicle research. 

The work includes a review of the state of the art, identification of key uncertainty parameters and traffic performance metrics, implementation of simplified uncertainty modules, and exploration of representative traffic scenarios. The pre-study will deliver a catalogue of uncertainty factors, a prototype implementation, and a final report summarizing methods and findings. 

In the longer term, these results will serve as the basis for large-scale simulation studies that connect communication performance, positioning reliability, and vehicle control strategies to safety outcomes. Improved modeling of uncertainty will allow future projects to better assess how realistic data imperfections affect cooperative driving and system validation.