arXiv AI

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.

agents reinforcement-learning safety

arXiv AI

Jul 28

Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic

arXiv:2601. 11809v2 Announce Type: replace Abstract: Connected automated vehicles (CAVs) possess the ability to communicate and coordinate with one another, enabling cooperative platooning that enhances both energy efficiency and traffic flow.

By Zeyu Mu, Shangtong Zhang, B. Brian Park

agents

arXiv Machine Learning

Sep 17

Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control

The paper introduces Composite‑Gradient Learning (CGL), a method that explicitly incorporates a model predictive controller (MPC) into the training of a deep reinforcement learning (DRL) agent by treating their control inputs as a joint action. CGL updates the DRL policy while accounting for the interaction with the MPC, unlike prior approaches that view MPC merely as part of the environment. Experiments on two freeway traffic networks show that CGL performs better than alternative methods when the interaction between DRL and MPC is strong, though overall gains are modest.

By Giray \"On\"ur, Azita Dabiri, Bart De Schutter

agents reinforcement-learning

arXiv Machine Learning

4d ago

Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving

arXiv:2610.19429v1 Announce Type: new Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, i...

By Dechen Xie, Mushuang Liu

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jul 24

Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning

arXiv:2607. 20547v1 Announce Type: new Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable.

By Esrat Farhana Dulia, Syed Arbab Mohd Shihab, Caleb Adams, Ruben Del Rosario

agents reinforcement-learning safety

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.

By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

agents reinforcement-learning robotics safety

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

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.

By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

agents reinforcement-learning safety

arXiv Machine Learning

Aug 24

Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks

arXiv:2608.20858v1 Announce Type: cross Abstract: Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are ch...

By Giray Onur, Azita Dabiri, Bart De Schutter

reinforcement-learning

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.

By Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter

reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 13

Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving

arXiv:2608. 11498v1 Announce Type: cross Abstract: Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whether training with language-structured data leads to truly adaptive control policies.

By Aditya Humnabadkar, Huaizhong Zhang, Ardhendu Behera

reinforcement-learning safety

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.

By Donggyu Min, Seongjin Choi, Dong-Kyu Kim

agents reinforcement-learning

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