Robustness and Resilience Evaluation of Eco-Driving Strategies at Signalized Intersections
🤖 AI Summary
This study addresses the limitations of existing eco-driving strategy evaluations, which predominantly rely on idealized simulations and fail to capture the impact of real-world execution errors and environmental disturbances on performance. To bridge this gap, the work proposes the first unified evaluation framework that integrates control robustness and environmental resilience, formally defining a metric for performance degradation under perturbations. Through real-vehicle experiments, the authors compare optimization-based and analytical controllers. The results demonstrate that optimization-based controllers exhibit greater stability across various disturbances, whereas analytical controllers—while efficient under nominal conditions—are highly sensitive to actuation and timing deviations. This reveals a critical trade-off between precision and adaptability in eco-driving system design.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
eco-driving
robustness
resilience
signalized intersections
performance degradation
Innovation
Methods, ideas, or system contributions that make the work stand out.
eco-driving
robustness
resilience
signalized intersections
real-world evaluation
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