arXiv AI
Jul 7
Explainable Reinforcement Learning for Adaptive Traffic Signal Control
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.
SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive Control for Robust, Dependable Traffic Management
SIGMA is a reinforcement‑learning framework for traffic signal control that incorporates a large language model to adaptively tune multiple objectives based on natural‑language emergency commands. It uses rotational data augmentation to learn orientation‑invariant policies and an offline‑to‑online training pipeline to ensure stable deployment. Experiments in SUMO on four Kolkata intersections show that SIGMA reduces waiting times, queue lengths, and improves throughput compared to fixed‑time, actuated, and DQN baselines, with ablation studies confirming robustness to component failures and geometric rotations.
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Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
The paper presents a modeling and simulation framework to study reinforcement‑learning control of connected and automated vehicle (CAV) platoon joining maneuvers in mixed traffic. It evaluates Deep Q‑Network, Double Deep Q‑Network, and Proximal Policy Optimization algorithms, finding that PPO achieves a 98 % joining success rate with less than 1 % collisions by incorporating risk penalties, though it requires more decision steps. An external safety controller can prevent collisions but may reduce joining efficiency, highlighting a trade‑off between safety, effectiveness, and decision speed.
arXiv AI
Jun 2
Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.
arXiv AI
Aug 28
Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
The paper presents a modeling and simulation framework to study reinforcement learning (RL) control of connected and automated vehicle (CAV) platoon joining maneuvers in mixed traffic. Using SUMO and agent-based modeling, it evaluates Deep Q-Network (DQN), Double DQN (DDQN), and Proximal Policy Optimization (PPO) algorithms, finding that PPO achieves a 98 % joining success rate with less than 1 % collision rate by incorporating risk penalties. The study also shows a trade‑off between safety, joining effectiveness, and decision efficiency, and demonstrates that an external safety controller can prevent collisions but may reduce joining efficiency.
arXiv AI
4d ago
VLALight: A Vision-Language-Action Model for Traffic Signal Control
arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signal...
arXiv AI
Jul 22
Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority
arXiv:2607. 18286v1 Announce Type: cross Abstract: Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles.
arXiv AI
Aug 26
Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
arXiv:2412.02520v4 Announce Type: replace-cross Abstract: Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. T...
arXiv AI
Jun 2
TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
arXiv Machine Learning
Jun 26
Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy
arXiv:2606. 26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making.
arXiv AI
Jul 7
Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking
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arXiv AI
Sep 2
Risk-Aware Decision-Making for Autonomous Overtaking: A World Model-Based Mixture-of-Experts Framework
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arXiv Machine Learning
Sep 3
PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems
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