Traffic Operational Analysis from Sensor Application of Electric Autonomous Vehicles and Internal Combustion Engine Autonomous Vehicles Focusing on Difference in Acceleration Profile

Abstract

With the development of sensor technology in the autonomous and electric vehicle industry, more vehicles are expected to be electric and have autonomous driving capability. It is believed that electric vehicles allow for the simpler integration of advanced sensor application technologies required for the cleaner and safer operation of autonomous vehicles. Although electric autonomous vehicles have many advantages over their gasoline-powered counterparts, not all autonomous vehicles are manufactured as electric vehicles. Therefore, it is expected that they will be operated in a mixed environment. Even though autonomous vehicles operate without human inputs, electric and internal-combustion-engine autonomous vehicles would operate differently owing to their respective characteristics including acceleration profiles. Their different acceleration profiles would lead to differences in traffic operation characteristics. From the data acquired from sensors, in this study, we investigate the traffic operational characteristics of electric and internal-combustion-engine autonomous vehicles in a fully autonomous driving environment. Acceleration potential curves for electric and internal-combustion-engine autonomous vehicles are modeled in a simple traffic network in a microscopic traffic simulation model. It is demonstrated that more vehicles can pass a signalized intersection when there are electric autonomous vehicles than when there are internal-combustion-engine autonomous vehicles. Also, the impacts of different speed limits and market penetration rates are investigated.