Eco-driving at signalized intersections: A control model-based method considering lane-changing uncertainty

Shanqi Guo, Shuang Chen and Minghui Hu

Energy, 2025, vol. 326, issue C

Abstract: To address the queue uncertainty posed by lane change behaviors of surrounding vehicles, this paper introduces an eco-driving framework based on a novel eco-speed planning model for Connected and Autonomous Vehicles (CAVs). The eco-driving algorithm incorporates a target point concept, which allows CAVs to respond effectively to the lane changes of surrounding vehicles. The target point is determined using lookup tables based on Intelligent Driver Model-generated trajectories during red light stages and Long Short-Term Memory Neural Networks during green light stages. This paper presents a novel control model called Virtual Distance Model for driving at signalized intersections. The model enables CAVs to adaptively adjust their speed to achieve greater fuel efficiency when faced with uncertain lane changes by surrounding vehicles. Simulation results demonstrate that the proposed eco-driving algorithm enhances fuel economy and traffic efficiency compared to other eco-driving methods. Real experiments confirm that the proposed method improves fuel economy.

Keywords: Eco-driving; Lane-change uncertainty; Signalized intersections; Target point prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:326:y:2025:i:c:s0360544225017128

DOI: 10.1016/j.energy.2025.136070

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