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 …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
A taxonomy for autonomous vehicles considering ambient road infrastructure
To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon
This paper proposes an innovative distributed longitudinal control strategy for connected
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …
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 …
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 …
Leveraging the capabilities of connected and autonomous vehicles and multi-agent reinforcement learning to mitigate highway bottleneck congestion
Active Traffic Management strategies are often adopted in real-time to address such sudden
flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts …
flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts …
A DRL-based multiagent cooperative control framework for CAV networks: A graphic convolution Q network
Connected Autonomous Vehicle (CAV) Network can be defined as a collection of CAVs
operating at different locations on a multilane corridor, which provides a platform to facilitate …
operating at different locations on a multilane corridor, which provides a platform to facilitate …
Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning
Automation and connectivity based platforms have great potential for managing highway
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …
A multi-vehicle cooperative control scheme in mitigating traffic oscillation with smooth tracking-objective switching for a single-vehicle lane change scenario
This paper proposes a multi-vehicle cooperative control scheme in mitigating traffic
oscillation (MCCS-MTO) for a single-vehicle lane change (LC) scenario applied to …
oscillation (MCCS-MTO) for a single-vehicle lane change (LC) scenario applied to …