Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
A systematic survey of control techniques and applications in connected and automated vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems
User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
A deep reinforcement learning‐based distributed connected automated vehicle control under communication failure
This paper proposes a deep reinforcement learning (DRL)‐based distributed longitudinal
control strategy for connected and automated vehicles (CAVs) under communication failure …
control strategy for connected and automated vehicles (CAVs) under communication failure …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
[HTML][HTML] Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range …
The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …