评估信号交叉口节能驾驶策略的鲁棒性与抗扰能力
Robustness and Resilience Evaluation of Eco-Driving Strategies at Signalized Intersections
- 构建统一框架，从控制鲁棒性和环境韧性双角度量化性能退化
- 实车实验显示优化型控制器在不同干扰下表现更稳定
- 解析型控制器在理想条件下表现好，但对执行误差敏感
节能驾驶策略在信号交叉口展现显著提升能效与降低排放的潜力。然而，现有评估多基于简化仿真或实验条件，依赖特定假设以控制复杂性。本研究提出统一评估框架，从控制鲁棒性与环境韧性两个互补维度出发，定义可量化内部执行变异与外部环境扰动导致性能下降的指标。这些指标被应用于多个节能驾驶控制器的实车实验评估。结果揭示追踪精度与适应性间的权衡：优化型控制器在各类扰动下表现更一致；解析型控制器在理想条件下性能相当，但对执行与时间偏差更敏感。
原文摘要 · Abstract (English)
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.
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