Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
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 progr...
| Authors | Dénes Toth, George Ambroladze, Edwin Sundberg et al. |
|---|---|
| Primary category | eess.SY |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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