Developing a comprehensive large-scale co-simulation for replication of automated driving in urban traffic scenarios

Abstract

Automated Vehicles (AVs) hold substantial potential to revolutionize urban transportation, but their safe and reliable operation in mixed-traffic conditions remains a challenge. While computer-based simulations can provide cost-effective, safe performance evaluations, they often fail to represent the complex realities of large-scale traffic systems. This paper presents a novel co-simulation approach that integrates high-fidelity AVs with adjustable penetration rates into a comprehensive traffic model. The co-simulation, developed by coupling the automated driving simulator CARLA with the microscopic traffic simulator SUMO, allows for the detailed evaluation of AV performance under diverse traffic conditions. It also aids in the estimation of AVs' impacts on traffic patterns. Despite the computational demands, the co-simulation scenario provides a scalable, dynamic, and robust platform for realistic AV performance assessment.