AI 中文总结
提出MDRC，一种基于元扩散的交通信号控制状态恢复防御，结合DDIM与Reptile元学习，在多个基准上显著降低平均出行时间并保证高决策可用性。
AI 中文摘要
交通信号控制（TSC）是一个依赖实时感知的安全关键型网络物理系统。由对抗性扰动或传感器故障引起的观测损坏可能从感知层传播到控制器，并降低交通效率。现有的基于强化学习（RL）的鲁棒TSC方法通常面临跨城市泛化能力有限、推理延迟高以及部分可观测性下恢复能力弱的问题。我们提出了MDRC（用于抵御对抗性攻击和传感器故障的弹性交通信号控制的元扩散框架），这是一种插入在感知与控制之间的检测后状态恢复防御机制。MDRC在控制器使用交通状态之前重建可信的交通状态。它结合了去噪扩散隐式模型（DDIM）以实现高效的状态恢复，以及Reptile元学习以实现跨城市的可迁移初始化。我们提供了DDIM恢复动力学的基于优化的视角，并建立了一个恢复误差界限，该界限将分数近似、数值离散化和初始化不匹配分离开来。在七个基于真实世界的CityFlow基准测试中，MDRC在随机和策略感知攻击下将平均出行时间降低了6.77%，在结构化传感器丢失下降低了12.75%，同时提高了状态恢复保真度。我们进一步评估了50%检测器通道禁用下的3600秒真实路边测量数据，并将MDRC集成到硬件在环交通信号栈中。在9.16小时、包含32,389个感知/控制周期的运行中，系统实现了99.79%的决策可用性，未产生任何超出计划的建议，并且每个一秒控制间隔的组件级处理时间约为38毫秒。
英文摘要
Traffic Signal Control (TSC) is a safety-critical cyber-physical system that relies on real-time sensing. Corrupted observations caused by adversarial perturbations or sensor failures can propagate from the sensing layer into the controller and degrade traffic efficiency. Existing robust Reinforcement Learning (RL)-based TSC methods often suffer from limited cross-city generalization, high inference latency, and weak recovery under partial observability. We present MDRC (Meta-Diffusion-based framework for Resilient traffic signal Control against adversarial attacks and sensor failures), a post-detection state-recovery defense inserted between sensing and control. MDRC reconstructs trustworthy traffic states before they are consumed by the controller. It combines Denoising Diffusion Implicit Models (DDIM) for efficient state recovery with Reptile meta-learning for a transferable initialization across cities. We provide an optimization-based view of the DDIM recovery dynamics and establish a recovery-error bound that separates score approximation, numerical discretization, and initialization mismatch. Across seven real-world-derived CityFlow benchmarks, MDRC reduces Average Travel Time by 6.77% under stochastic and policy-aware attacks and by 12.75% under structured sensor loss, while improving state-recovery fidelity. We further evaluate 3,600 seconds of real roadside measurements with 50% of detector channels disabled and integrate MDRC into a hardware-in-the-loop traffic-signal stack. Over a 9.16-hour run with 32,389 sensing/control cycles, the system achieves 99.79% decision availability, produces no out-of-plan recommendations, and requires approximately 38 ms of component-wise processing per one-second control interval.
