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Found 5 papers, 0 papers with code
Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning
However, it has been computationally difficult to scale these solution approaches to large networks partly due to the curse of dimensionality that is encountered as the number of intersections increases.
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture
To address this question, this study proposes a two-stage model that combines GAQ (Graph Attention Network - Deep Q Learning) and EBkSP (Entropy Based k Shortest Path) using a fog-cloud architecture, to reroute vehicles in a dynamic urban environment and therefore to improve travel efficiency in terms of travel speed.
Reason induced visual attention for explainable autonomous driving
Deep learning (DL) based computer vision (CV) models are generally considered as black boxes due to poor interpretability.
Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion
In addressing this objective, we duly recognize that one of the main challenges of RL-based CAV controllers is the variety and complexity of inputs that exist in the real world, such as the information provided to the CAV by other connected entities and sensed information.
Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control
From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.
