CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control

2024/03/31 by Bibek Poudel, Poudel, Bibek, Weizi Li +2 · 5 citations
Engineering · #FOS: Computer and information sciences #Robotics (cs.RO) #Traffic Prediction and Management Techniques #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2404.00796

openalex publication_date 2024/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately modeling such behavior is crucial for validating Robot Vehicles (RVs) in simulation and realizing the potential of mixed traffic control. However, existing approaches like parameterized models and data-driven techniques struggle to capture the full complexity and diversity. To address this, in this work, we introduce CARL, a hybrid approach that combines imitation learning for close proximity car-following and probabilistic sampling for larger headways. We also propose two classes of RL-based RVs: a safety RV focused on maximizing safety and an efficiency RV focused on maximizing efficiency. Our experiments show that the safety RV increases Time-to-Collision above the critical 4-second threshold and reduces Deceleration Rate to Avoid a Crash by up to 80%, while the efficiency RV achieves improvements in throughput of up to 49%. These results demonstrate the effectiveness of CARL in enhancing both safety and efficiency in mixed traffic.

Cited by

• Learning to Design City-scale Transit Routes
• Elevation Aware 2D/3D Co-simulation Framework for Large-scale Traffic Flow and High-fidelity Vehicle Dynamics
• Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control
• Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing
• Analyzing Collision Rates in Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning

Related

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