A multi-agent reinforcement learning approach for safe and efficient behavior planning of connected autonomous vehicles
The recent advancements in wireless technology enable connected autonomous vehicles
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
A multi-agent reinforcement learning approach for safe and efficient behavior planning of connected autonomous vehicles
The recent advancements in wireless technology enable connected autonomous vehicles
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
Multi-agent reinforcement learning for safe lane changes by connected and autonomous vehicles: A survey
B Hegde, M Bouroche - AI Communications, 2024 - content.iospress.com
Abstract Connected Autonomous vehicles (CAVs) are expected to improve the safety and
efficiency of traffic by automating driving tasks. Amongst those, lane changing is particularly …
efficiency of traffic by automating driving tasks. Amongst those, lane changing is particularly …
Spatial-temporal-aware safe multi-agent reinforcement learning of connected autonomous vehicles in challenging scenarios
Communication technologies enable coordination among connected and autonomous
vehicles (CAVs). However, it remains unclear how to utilize shared information to improve …
vehicles (CAVs). However, it remains unclear how to utilize shared information to improve …
Augmented Reinforcement Learning with Efficient Social-Based Motion Prediction for Autonomous Decision-Making
R Gutiérrez-Moreno, C Gómez-Huelamo… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
This paper presents an approach that improves the efficiency and generalization capabilities
of Reinforcement Learning-based autonomous vehicles operating in urban driving …
of Reinforcement Learning-based autonomous vehicles operating in urban driving …
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) …
A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning
Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios
B Brito, A Agarwal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
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 …
Learning interaction-aware guidance policies for motion planning in dense traffic scenarios
B Brito, A Agarwal, J Alonso-Mora - arXiv preprint arXiv:2107.04538, 2021 - arxiv.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …