Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors
The development of autonomous vehicles has shown great potential to enhance the
efficiency and safety of transportation systems. However, the decision-making issue in …
efficiency and safety of transportation systems. However, the decision-making issue in …
[HTML][HTML] Learning-based modeling of human-autonomous vehicle interaction for improved safety in mixed-vehicle platooning control
The rising presence of autonomous vehicles (AVs) on public roads necessitates the
development of advanced control strategies that account for the unpredictable nature of …
development of advanced control strategies that account for the unpredictable nature of …
Safe and Robust Multi-Agent Reinforcement Learning for Connected Autonomous Vehicles under State Perturbations
Sensing and communication technologies have enhanced learning-based decision making
methodologies for multi-agent systems such as connected autonomous vehicles (CAV) …
methodologies for multi-agent systems such as connected autonomous vehicles (CAV) …
Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections
R Zhao, Y Li, F Gao, Z Gao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Autonomous Intersection Management (AIM) systems present a new paradigm for conflict-
free cooperation of connected autonomous vehicles (CAVs) at road intersections, the aim of …
free cooperation of connected autonomous vehicles (CAVs) at road intersections, the aim of …
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events
such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV …
such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV …
Development of a stochastic traffic environment with generative time-series models for improving generalization capabilities of autonomous driving agents
Automated lane changing is a critical feature for advanced autonomous driving systems. In
recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded …
recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded …
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach
Traffic intersections present significant challenges for the safe and efficient maneuvering of
connected and automated vehicles (CAVs). This research proposes an innovative roadside …
connected and automated vehicles (CAVs). This research proposes an innovative roadside …
Learning-enabled decision-making for autonomous driving: framework and methodology
Z Huang - 2023 - dr.ntu.edu.sg
The growing adoption of autonomous vehicles (AVs) holds the promise of transforming
transportation systems, enhancing traffic safety, and supporting environmental sustainability …
transportation systems, enhancing traffic safety, and supporting environmental sustainability …
Learning highway ramp merging via reinforcement learning with temporally-extended actions
Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …
are active areas of research in autonomous driving. In order to properly navigate these …
[PDF][PDF] Offline Learning for Stochastic Multi-Agent Planning in Autonomous Driving
A Villaflor - 2024 - kilthub.cmu.edu
Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and
revolutionize how people travel and how we transport goods. Many of the major challenges …
revolutionize how people travel and how we transport goods. Many of the major challenges …