Co-simulation Supported Multicriteria Analysis of Autonomous Vehicles Impact on Urban Motorway Traffic Flow

Technological progress in recent years has enabled the development of operational autonomous vehicles (AVs). Since field testing and analysis of AVs is limited by financial and safety concerns, it is necessary to apply alternative methods to analyze AVs, one of which is simulation. Due to the fact that microscopic simulators may be limited in simulating AVs as a standalone platform, in this research, the state-of-the-art co-simulation framework comprised of the autonomous driving simulator CARLA and the microscopic simulator SUMO is leveraged to analyze the impact of AVs on the traffic flow. Using the co-simulation framework, we propose a holistic multicriteria analysis methodology to examine the impact of variable AV penetration rates on traffic efficiency, safety, and energy consumption under various traffic loads and speed limit configurations on an urban motorway model with a lane number reduction. Experiment results show that while AVs have the potential to improve traffic efficiency and safety, they do so at the cost of increasing the energy consumption.