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

TL;DR

This paper presents a unified framework for evaluating eco-driving strategies at signalized intersections, focusing on control robustness and environmental resilience, with real-world experiments revealing tradeoffs between different controller types.

Contribution

It introduces formal indicators for robustness and resilience, and applies them to compare eco-driving controllers under real-world disturbances.

Findings

Optimization-based controllers are more consistent across disturbances.

Analytical controllers perform well under nominal conditions but are more sensitive to variability.

Tradeoffs exist between tracking accuracy and adaptability.

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.