发表机构
Chicago State University（芝加哥州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究信号交叉口Eco-CACC系统，提出完整性门控框架，结合多种因素构建统一完整性度量调节控制权，仿真表明其在模型一致时保持效率，完整性下降时能早期保守响应，解决现有方法在传感等问题下的不足。
AI 中文摘要
生态协同自适应巡航控制（Eco-CACC）系统依靠精确的定位、信号定时和交互感知来优化信号交叉口的能耗。现有方法通常假定用于优化的内部世界模型保持有效，当传感中断或语义不一致使规划前提无效时易受影响。本文提出一个完整性门控Eco-CACC框架，明确监测车辆内部信念与外部传感之间的一致性。通过结合位置创新、可观测性损失和语义不一致构建统一的完整性度量。所得信任分数调节控制权，实现标称生态驾驶与安全主导的 fallback 操纵之间的转换。与在不确定性下保持性能的鲁棒控制方法不同，该框架调节能量最优控制是否仍然可容许。基于场景的仿真表明，该方法在保持模型一致性时保持标称效率，同时在完整性下降时实现早期和保守响应。
英文摘要
Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems rely on accurate localization, signal timing, and interaction awareness to optimize energy consumption at signalized intersections. Existing approaches typically assume that the internal world model used for optimization remains valid, making them vulnerable when sensing outages or semantic inconsistencies invalidate planning premises. This letter proposes an Integrity-Gated Eco-CACC framework that explicitly monitors the consistency between internal vehicle beliefs and external sensing. A unified integrity metric is constructed by combining positional innovation, observability loss, and semantic inconsistencies. The resulting trust score regulates control authority, enabling a transition between nominal eco-driving and a safety-dominant fallback maneuver. Unlike robust control methods that attempt to preserve performance under uncertainty, the proposed framework regulates whether energy-optimal control remains admissible. Scenario-based simulations demonstrate that the method preserves nominal efficiency when model consistency is maintained, while enabling early and conservative responses under integrity degradation.
