用主动推理优化城市路口信号灯，在噪声环境下更省时减排。
Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
- 基于高斯信念最小化预期自由能，动态选择信号相位
- 在最嘈杂场景下空转时间减少14.8%、碳排放降低4.7%
- 决策过程完全可追溯，适合需透明性的交通管理系统
物联网智能路口的交通信号控制需应对传感器遮挡、天气衰减和需求非平稳性等挑战。传统控制器在此类条件下性能下降，而学习型策略难以审计。为此，本文提出一种针对四臂信号路口的主动推理控制器，通过最小化关于各方向拥堵水平的高斯信念的预期自由能（EFE），实现相位动态选择，构建全可追溯决策流程。我们在SUMO交通仿真器中对比了规则启发式方法与深度Q网络（DQN）在四个逐步增加噪声与非平稳性的场景下的表现，涵盖传感器遮挡、恶劣天气及随机事故。每个场景进行100次独立随机评估，结果表明：在最嘈杂场景下，主动推理控制器的空转时间（56,977秒）与二氧化碳排放量（29.12公斤）均低于DQN（71,741秒，30.56公斤）。该性能提升以轻微降低公交优先服务率和相位切换频率为代价。
原文摘要 · Abstract (English)
Urban traffic signal control at IoT-instrumented intersections must remain effective under sensor occlusion, weather attenuation, and nonstationary demand. Conventional controllers degrade under these conditions, and learned policies remain difficult to audit. To address these challenges, we propose an active inference controller for a four-arm signalized intersection that dynamically selects phases by minimizing expected free energy (EFE) over Gaussian beliefs about per-direction congestion levels, yielding a fully traceable decision pipeline. We benchmark the controller in a SUMO traffic simulator against a rule-based heuristic and a deep Q-network (DQN) across four scenarios that progressively increase noise and nonstationarity, spanning sensor occlusion, adverse weather, and stochastic accidents. Across 100 independent random evaluations per scenario, active inference attains the lowest idle times and CO2 emissions in the noisiest scenarios (56,977 s and 29.12 kg vs. 71,741 s and 30.56 kg for DQN). These gains come at a modest cost in bus priority service rate and phase switch frequency.
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