Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic

Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp merging in mixed traffic as a study case, and establish human-in-the-loop (HuiL) simulations. The results show that our proposed platform can facilitate data collection and performance assessment for modeling personalized behaviors and interactions in CADS under various traffic scenarios.