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.

翻译：生态驾驶策略在提升能源效率与减少排放方面展现出显著潜力，尤其在信号交叉口场景中。然而，现有对生态驾驶方法的评估通常依赖于简化的仿真或实验条件，其中需通过特定假设来管理复杂性与实验控制。本研究提出一个统一框架，通过控制鲁棒性与环境弹性这两个互补准则来评估生态驾驶策略。我们定义了分别量化内部执行变异性和外部环境扰动所引起性能退化的形式化指标。随后通过实车实验应用这些指标对多种生态驾驶控制器进行评估。结果揭示了跟踪精度与适应性之间的关键权衡：基于优化的控制器在不同扰动水平下表现出更稳定的性能，而解析控制器在标称条件下可能具有相当性能，但对执行与时序变异性表现出更高的敏感性。