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Signalized intersections in arterial roads result in persistent vehicleidling and excess accelerations, contributing to fuel consumption and CO2emissions. There has thus been a line of work studying eco-driving controlstrategies to reduce fuel consumption and emission levels at intersections.However, methods to devise effective control strategies across a variety oftraffic settings remain elusive. In this paper, we propose a reinforcementlearning (RL) approach to learn effective eco-driving control strategies. Weanalyze the potential impact of a learned strategy on fuel consumption, CO2emission, and travel time and compare with naturalistic driving and model-basedbaselines. We further demonstrate the generalizability of the learned policiesunder mixed traffic scenarios. Simulation results indicate that scenarios with100% penetration of connected autonomous vehicles (CAV) may yield as high as18% reduction in fuel consumption and 25% reduction in CO2 emission levelswhile even improving travel speed by 20%. Furthermore, results indicate thateven 25% CAV penetration can bring at least 50% of the total fuel and emissionreduction benefits.

Learning Eco-Driving Strategies at Signalized Intersections