Robustness and Resilience Evaluation of Eco-Driving Strategies at Signalized Intersections

TL;DR

This work addresses evaluating eco-driving strategies at signalized intersections under real-world uncertainties. It introduces formal robustness and resilience indicators, $R(F(\cdot))$ and $G(F(\cdot),\Delta\mathbf{O})$, within a unified planning-control framework that separates planning interval $Δt$ from execution interval $δ$. Real-vehicle experiments compare an optimization-based controller and an analytical trajectory controller, revealing that optimization-based methods offer more consistent robustness and resilience across disturbances while analytical methods are more sensitive to severe perturbations. The framework provides a practical, quantitative basis for assessing eco-driving methods beyond ideal efficiency, with future potential extensions to multi-vehicle settings and adaptive disturbance modeling.

Abstract

Eco-driving strategies have demonstrated substantial potential for improving energy efficiency and reducing emissions, especially at signalized intersections. However, evaluations of eco-driving methods typically rely on simplified simulation or experimental conditions, where certain assumptions are made to manage complexity and experimental control. This study introduces a unified framework to evaluate eco-driving strategies through the lens of two complementary criteria: control robustness and environmental resilience. We define formal indicators that quantify performance degradation caused by internal execution variability and external environmental disturbances, respectively. These indicators are then applied to assess multiple eco-driving controllers through real-world vehicle experiments. The results reveal key tradeoffs between tracking accuracy and adaptability, showing that optimization-based controllers offer more consistent performance across varying disturbance levels, while analytical controllers may perform comparably under nominal conditions but exhibit greater sensitivity to execution and timing variability.

Figures (3)

• Figure 1: Real-world experimental setup.
• Figure 2: Test platform
• Figure 3: Actual vs Prediction