arXiv:2602.07784cs.CV2026-02

基于视觉的交通信号控制,考虑感知不确定性与安全约束。

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

  • 构建车辆队列、到达与服务年龄的信念状态,实时评估决策可行性
  • 在模拟中消除冲突区违规,约束服务时长避免饥饿问题
  • 适合需要高安全性和可解释性的智能交通系统部署

基于摄像头的自适应交通信号控制本质上是部分可观测的:目标检测可能遗漏,车速与距离存在噪声,且一旦黄灯启动,相位切换即不可逆。本文提出UCATSC,一种可解释的不确定性感知约束决策层,用于视觉驱动的自适应信号控制。该方法在运动层面维护队列、到达和服 务年龄的简化信念状态;通过信念空间内的有限时域反事实回溯评估可行相位动作;并在执行前利用预测的冲突区安全性和服务年龄/饥饿约束筛选候选动作。在SUMO仿真中,采用匹配种子、经典基线、训练好的DQN强化学习基线、安全掩码DQN变体、针对性的安全性/活性/不确定性压力测试以及三交叉口走廊扩展场景进行评估。结果表明,UCATSC在移动性表现上具有竞争力,受约束版本完全消除冲突区违规,饥饿压力测试中有效限制服务时间,且在线运行延迟保持在毫秒级。附带的物理视觉实验平台验证了视觉到信念转换接口的可行性,但不构成对安全、排放或实际部署的现场验证。

原文摘要 · Abstract (English)

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.

交通控制不确定性建模强化学习视觉感知

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