arXiv AI
Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
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arXiv AI
Sep 18
Learn2Drive: A neural network-based framework for socially compliant automated vehicle control
The paper presents Learn2Drive, a neural‑network‑based framework for socially compliant adaptive cruise control in automated vehicles. It incorporates social value orientation to let AVs consider their impact on human‑driven vehicles and overall traffic flow, aiming to reduce congestion and improve efficiency. Numerical experiments show that shifting the AV’s objective from personal energy savings to collective traffic flow can boost downstream vehicle speeds by over 38% and dampen traffic oscillations.
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.
arXiv Machine Learning
Oct 8
Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
arXiv:2606.22108v2 Announce Type: replace-cross Abstract: Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic si...
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.
Hugging Face Trending Papers
Aug 27
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
Oct 12
Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles
The paper introduces a preference-conditioned multi-objective reinforcement learning framework for tactical decision making in heavy-duty vehicles. It explicitly models safety, energy efficiency, and time efficiency as separate objectives and uses Generalized Policy Improvement and Proximal Policy Optimization to learn policies that approximate the Pareto front across these conflicting goals. Experiments in a scalable highway traffic simulation show smooth, interpretable Pareto fronts and meaningful trade-offs, with the policy enabling behavior transitions without retraining.