SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive Control for Robust, Dependable Traffic Management

2026-08-18

AI Summary

论文提出SIGMA，一种结合大语言模型的强化学习框架，解决交通信号控制中的多目标适应问题，通过自然语言指令调整优先级，提高交通流量和稳定性。

Abstract

Traffic signal control is a complex sequential decision-making problem requiring real-time adaptation and trade-offs among throughput, delay fairness, signal stability, and emergency vehicle priority. Existing RL methods often fix objectives, ignore dynamic priority changes, and fail to generalize across geometrically similar intersections.We propose SIGMA (Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive traffic control), an RL framework enhanced with a large language model (LLM) for adaptive objective tuning and orientation-invariant learning. SIGMA converts natural-language emergency commands into priority vectors for a multi-objective actor-critic controller, avoiding manual reward engineering. Rotational augmentation improves transferability across four-way intersections, while offline-to-online learning ensures stable initialization and gradual adaptation to changing traffic.We define reliability properties covering emergency service levels, graceful degradation under LLM failures, and demand sensitivity, validated via bootstrap statistics. Evaluated in SUMO on four Kolkata-based urban intersections against fixed-time, actuated, and DQN controllers, SIGMA reduces average/emergency waiting times and queue lengths, and boosts throughput. Ablation studies confirm robustness to component failures and geometric rotations. Overall, SIGMA offers a reliable, language-guided, multi-objective traffic control system with statistical reliability assurance.

Problem

• Traffic Signal Control
• Dynamic Priority Changes
• Geometric Similarity

Innovation

• Symmetry-aware
• Adaptive Objective Tuning
• Orientation-invariant Learning
• Rotational Augmentation
• Offline-to-Online Learning