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: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...
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.
MPCFormer is a physics‑informed, data‑driven framework that explicitly models multi‑vehicle social interaction dynamics for autonomous driving. It uses a Transformer‑based encoder‑decoder to learn discrete state‑space dynamics from naturalistic data, enabling explainable, human‑like behavior planning within a Model Predictive Control (MPC) framework. In open‑loop NGSIM tests, it achieves the lowest trajectory prediction errors (ADE 0.86 m over 5 s), and in closed‑loop intense interaction scenarios it attains a 94.67 % planning success rate, 15.75 % efficiency gain, and reduces collisions from 21.25 % to 0.5 %.
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.
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.
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:2506. 12283v2 Announce Type: replace Abstract: Modeling vehicle interactions at unsignalized intersections is a challenging task due to the complexity of the underlying game-theoretic processes.
arXiv:2606. 06805v1 Announce Type: cross Abstract: Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency.
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.
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.
arXiv:2605. 29693v2 Announce Type: replace Abstract: Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution.
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.