Advances in Intelligent Traffic Management

The field of intelligent traffic management is moving towards the integration of advanced technologies such as reinforcement learning, knowledge graph embeddings, and stochastic differential equations to improve traffic flow and safety. Researchers are exploring the application of these technologies in real-world scenarios, including lane change prediction, traffic signal control, and autonomous vehicle navigation. Notable trends include the development of hierarchical reinforcement learning frameworks for large-scale traffic signal control and the use of global adversarial guidance to improve coordination between individual intersections. Some noteworthy papers in this area include:

  • A paper on Real-World Deployment of a Lane Change Prediction Architecture, which demonstrates a lane-change prediction system that anticipates the target vehicle's maneuver three to four seconds in advance.
  • HiLight: A Hierarchical Reinforcement Learning Framework, which proposes a hierarchical reinforcement learning framework with global adversarial guidance for large-scale traffic signal control and exhibits significant advantages in large-scale scenarios.

Sources

Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference

Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks

Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study

A Stochastic Differential Equation Framework for Modeling Queue Length Dynamics Inspired by Self-Similarity

HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control

Mixed Traffic: A Perspective from Long Duration Autonomy

Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging

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