Uncertainty-Aware Counterfactual Traffic Signal Control with Predictive Safety and Starvation-Avoidance Constraints Using Vision-Based Sensing

Abstract

Real-world deployment of adaptive traffic signal control, to date, remains limited due to the uncertainty associated with vision-based perception, implicit safety, and non-interpretable control policies learned and validated mainly in simulation. In this paper, we introduce UCATSC, a model-based traffic signal control system that models traffic signal control at an intersection using a stochastic decision process with constraints and under partial observability, taking into account the uncertainty associated with vision-based perception. Unlike reinforcement learning methods that learn to predict safety using reward shaping, UCATSC predicts and enforces hard constraints related to safety and starvation prevention during counterfactual rollouts in belief space. The system is designed to improve traffic delay and emission while preventing safety-critical errors and providing interpretable control policy outputs based on explicit models.