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.

翻译：迄今为止，自适应交通信号控制在现实世界中的部署仍然有限，这主要归因于基于视觉感知的不确定性、隐含的安全性以及主要在仿真环境中学习和验证的非可解释控制策略。本文提出UCATSC，一种基于模型的交通信号控制系统，该系统在部分可观测条件下，采用带约束的随机决策过程对交叉口交通信号控制进行建模，并考虑了基于视觉感知的不确定性。与通过奖励塑形学习预测安全性的强化学习方法不同，UCATSC在信念空间的反事实推演过程中，预测并强制执行与安全性和防饥饿相关的硬约束。该系统旨在改善交通延误和排放，同时防止安全关键错误，并基于显式模型提供可解释的控制策略输出。