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) …
Cooperative multiagent deep deterministic policy gradient (CoMADDPG) for intelligent connected transportation with unsignalized intersection
T Wu, M Jiang, L Zhang - Mathematical Problems in …, 2020 - Wiley Online Library
Unsignalized intersection control is one of the most critical issues in intelligent transportation
systems, which requires connected and automated vehicles to support more frequent …
systems, which requires connected and automated vehicles to support more frequent …
HGRL: Human-Driving-Data Guided Reinforcement Learning for Autonomous Driving
H Zhuang, H Chu, Y Wang, B Gao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …
However, designing appropriate reward functions to guide RL agents towards complex …
A reinforcement learning benchmark for autonomous driving in intersection scenarios
In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
BB Elallid, H El Alaoui… - … Conference on Innovation …, 2023 - ieeexplore.ieee.org
In this paper, we explore the challenges associated with navigating complex T-intersections
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …
Centralized Cooperation for Connected Autonomous Vehicles at Intersections by Safe Deep Reinforcement Learning
R Zhao, Y Li, K Wang, Y Fan, F Gao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Connected and automated vehicles (CAVs) have the potential to transform traffic
management, especially at intersections. Traditional traffic signals might become obsolete …
management, especially at intersections. Traditional traffic signals might become obsolete …
Decision-Making Models for Autonomous Vehicles at Unsignalized Intersections Based on Deep Reinforcement Learning
SY Xu, XM Chen, ZJ Wang, YH Hu… - … on Advanced Robotics …, 2022 - ieeexplore.ieee.org
Decision making at unsignalized intersections is a critical challenge for autonomous
vehicles. Navigating through urban intersections requires determining the intentions of other …
vehicles. Navigating through urban intersections requires determining the intentions of other …
Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness
The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
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 …
Deep reinforcement learning for autonomous vehicles collaboration at unsignalized intersections
As conservative intersection management, signalized intersection has a significant
bottleneck in improving traffic efficiency when it comes to connected autonomous vehicles …
bottleneck in improving traffic efficiency when it comes to connected autonomous vehicles …