Eco-Driving Framework for Autonomous Vehicles at Signalized Intersection in Mixed-Traffic Environment

Abstract

It is interesting to realize that traffic intersections provide an essential mechanism to handle traffic flows in different directions while ironically traffic bottlenecks, gridlocks, and accidents tend to occur in the vicinity of the intersections. Meanwhile, with the recent developments in internet of things (IoT) technologies, there is a great potential for integrating them for improving operational efficiencies of road infrastructure and connected autonomous vehicles (CAVs). This paper aims to leverage the capabilities of both autonomous intersection manager (AIM) and CAVs for more energy-saving and safe-traffic management. A mixed-traffic environment where human-driven vehicles (HDVs) and CAVs sharing the same road is considered. A two-layer framework is adopted to handle signal and vehicle controls effectively. The first layer is a signal control layer where the AIM receives the traffic network states, trains with the data through machine learning (ML), and outputs a set of optimal green times for each intersection phase. The second layer is a decentralized-vehicle control layer where the CAVs receive the signal phase and timing (SpaT) information from the AIM to compute the optimal speed values. The proposed solution helps the CAVs to minimize idling at red signals or to speed up to safely arrive at and pass through a green signal. Our proposed framework is designed to optimize intersection efficiency and minimize vehicle average delay and fuel consumption. All experiments have been conducted in a microscopic traffic simulation environment, the PTV-VISSIM, simulating real-world dynamics of vehicles and drivers’ behaviors based on decades of field-data.

Keywords

• Intelligent transportation
• connected autonomous vehicles
• signalized intersections
• mixed-traffic
• machine learning
• adaptive speed control