Sustainable Eco-driving Strategy at Signalized Intersections from Driving Data

Energy consumption of vehicles at signalized intersections is highly influenced by acceleration/deceleration maneuvers and idling time. Existing research on fuel-efficient driving at signalized intersections mainly focused on connected and automated vehicle (CAV) technologies. This paper introduces a sustainable eco-driving strategy learning scheme from driving data of a vehicle that generates the optimal speed trajectory when approaching a signalized intersection. The goal of the proposed system is to reduce fuel consumption by advising an appropriate driving strategy near a signalized intersection. Our main contribution is that the driving performance in traffic scenarios in the existing signalized intersection can be improved without utilization of vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communications. A Gaussian process regression (GPR) model is developed that predicts intersection crossing time and crossing probability of a vehicle from its driving data. Based on the crossing probability, the optimal speed profile is calculated through an optimization algorithm for fuel-efficient driving. The viability of the proposed system is assessed using microscopic traffic simulation and the findings show significant fuel economy enhancements.