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Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic — arXiv:2608.26860

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26860
License (as archived): CC0-1.0
Archived: 2026-10-17T14:30:15.346Z
SHA-256 of the archived text: 3decee25185c1c59bf6bf80944d50dc1c8e8858efa8b68f1dc3054ec0d422448
Stable URL of this page: https://metraipolis.com/documento/dc_99f8d5cc73ee

How to cite

Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic — arXiv:2608.26860 — arXiv (en), https://arxiv.org/abs/2608.26860 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_99f8d5cc73ee (Metraipolis archive dc_99f8d5cc73ee, sha256 3decee25185c1c59bf6bf80944d50dc1c8e8858efa8b68f1dc3054ec0d422448)

BibTeX

@misc{dc99f8d5cc73ee,
  title = {Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic — arXiv:2608.26860},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26860},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_99f8d5cc73ee; sha256 3decee25185c1c59bf6bf80944d50dc1c8e8858efa8b68f1dc3054ec0d422448},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26860. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control in real-world traffic is challenging due to uncertainty and heterogeneous driving behaviors. Reinforcement learning (RL) has strong potential for addressing such control problems, but its practical deployment raises challenges related to safety and learning efficiency. This paper proposes a generic modeling and simulation framework for investigating CAV platoon joining maneuvers and comparing deep reinforcement learning (DRL)-based control algorithms. The problem is particularly challenging in mixed-traffic environments, where CAVs coexist with human-driven vehicles exhibiting heterogeneous longitudinal and lateral behaviors. The objective is to achieve safe and efficient joining maneuvers by either incorporating penalties for risky behaviors into the learning process or using an external safety controller to constrain the learned policy. An agent-based modeling framework coupled with the Simulation of Urban MObility (SUMO) simulator is used to evaluate Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Results show that PPO outperforms DQN and DDQN, achieving a joining success rate of approximately 98 % and a collision rate below 1 %, largely due to risk-related penalties incorporated into the reward function. However, this improved performance requires more decision steps to complete the maneuver, revealing a trade-off between safety, joining effectiveness, and decision efficiency. An external safety controller effectively prevents collisions, although its interventions may reduce joining efficiency. The results highlight the importance of jointly considering safety and efficiency when designing RL-based controllers for CAV platoon joining in mixed traffic.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2608.26860 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T13:16:53.671Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).