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.
By Biao Yin, Abderrahmane Kasmi, Nadir Farhi
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
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
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
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
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
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
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
arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.
By Qi Lan, Yining Tang, Yu Shen, Yi Zhou, Yuhao Wei, Jie Li, Guofa Li
arXiv:2607. 18637v1 Announce Type: cross Abstract: Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions.
By Jingzheng Li, Yufei Ge, Zhijun Chen, Qianren Mao, Zizhe Wang, Binhang Qi, Bing Li, Keyu Chen, Baochang Zhang, Xianglong Liu, Philip S Yu
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
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...
By Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli