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.
By Yuhui Liu, Samannita Halder, Shian Wang, Tianyi Li
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:2606.22108v2 Announce Type: replace-cross
Abstract: Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic si...
By Yousef AlSaqabi
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
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.
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
arXiv:2605. 29693v2 Announce Type: replace Abstract: Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution.
By Chinmay Mundane, Amith Manoharan, Arun Kumar Singh
The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.
By Ruizheng Xu (Heudiasyc), Lounis Adouane (Heudiasyc), Javier Iba\~nez-Guzm\'an, Cl\'ement Zinoune
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: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. 22691v1 Announce Type: new Abstract: Urban traffic congestion significantly increases fuel consumption, greenhouse gas emissions, and commuter delays, resulting in substantial economic losses and environmental harm in modern cities.
By Yue Ding, Tendai Mukande, Mingming Liu
arXiv:2606. 30694v1 Announce Type: cross Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand.
By Qian Hu, Haoyang Peng, Songan Zhang, Ming Yang, Hongtei Eric Tseng