mlsanityData to 7 Oct 2026

UCATSC: Uncertainty-Aware Constrained Traffic Signal Control Under Vision-Based Partial Observability

arXiv 2602.07784 ↗First posted 8 Feb 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Camera-based adaptive traffic signal control is inherently partially observable: detections can be missed, vehicle speeds and distances can be noisy, and a phase-change decision becomes temporally irreversible once yellow onset is initiated. This paper presents UCATSC, an interpretable uncertainty-aware constrained decision layer for vision-based adaptive signal control. UCATSC maintains a reduced movement-level belief state over queue, arrival, and service-age variables; evaluates admissible phase actions through finite-horizon counterfactual rollouts in belief space; and filters candidate actions using predictive dilemma-zone safety and service-age/starvation constraints before execution. The method is evaluated primarily in SUMO using matched seeds, classical baselines, a trained DQN-RL baseline, a safety-masked DQN variant, targeted safety/liveness/uncertainty stress tests, and a three-intersection corridor extension. In the tested scenarios, UCATSC demonstrates competitive mobility performance, eliminates observed dilemma-zone violations for the constrained variants, bounds service age in starvation stress tests, and maintains millisecond-level online runtime. A controlled physical vision testbed is included as supplementary feasibility evidence for the vision-to-belief interface; it is not presented as field validation of safety, emissions, or deployment readiness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 29 references that are in the map. First posted on arXiv 8 Feb 2026.

Builds on

4 of 29 references.

References · 4

  1. 2016Concrete Problems in AI Safetycited by 1,582
  2. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  3. 2019Learning an Interpretable Traffic Signal Control Policycited by 20
  4. 2022Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Maskingcited by 6

Built on by

Not cited yet.