Uncertainty-Aware Counterfactual Traffic Signal Control with Predictive Safety and Starvation-Avoidance Constraints Using Vision-Based Sensing
This addresses real-world deployment challenges in traffic management for urban planners and engineers, though it appears incremental as it builds on existing model-based and constraint-based methods.
The paper tackled the problem of adaptive traffic signal control under uncertainty from vision-based sensing by introducing UCATSC, a model-based system that enforces hard safety and starvation-avoidance constraints during counterfactual rollouts, aiming to improve traffic delay and emissions while preventing safety-critical errors.
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.