Reinforcement-Learning Lane Assignment for Scalable Traffic Flow
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Solution Overview
Problem
Conventional centralized lane-assignment strategies for traffic flow regulation face scalability issues due to the curse of dimensionality and suffer from communication latency and execution rate concerns, making them ineffective in practical real-world conditions.
Innovation Solution
A hierarchical traffic-flow regulation system using a reinforcement-learning-based model with a single neural network and shared parameters for coordinated vehicle lane assignment, where a locality manager determines target lateral flows and section managers convert these into lane-change actions for connected vehicles, optimizing traffic flow while minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized lane-assignment strategies are used, then traffic flow regulation can be achieved, but scalability deteriorates due to the curse of dimensionality
Solution Approach 1:
The patent divides the centralized lane-assignment system into hierarchical segments: a central controller that formulates global lane-assignment strategies and distributed vehicle units that execute local lane-change actions. This segmentation allows the system to maintain global coordination while reducing the computational burden on individual components, thereby improving scalability without sacrificing traffic flow regulation effectiveness.
Solution Approach 2:
The patent transitions from a purely centralized two-dimensional control architecture to a hierarchical three-dimensional architecture that adds the vertical dimension of distributed execution. This dimensional change enables the system to handle high-dimensional state spaces by distributing computation across multiple levels: global strategy formulation, local state processing, and individual vehicle execution.
2Reliability
If centralized lane-assignment strategies are used, then traffic flow regulation can be achieved, but communication latency increases
Solution Approach 1:
The central controller pre-computes and transmits lane-assignment strategies to vehicles in advance, before real-time execution is needed. This preliminary action allows vehicles to receive and process control instructions ahead of time, reducing the need for frequent real-time communication and thereby minimizing communication latency while maintaining effective traffic flow regulation.
Solution Approach 2:
Vehicles autonomously execute lane-change actions based on pre-received lane-assignment strategies and their own local state information, without requiring continuous real-time communication with the central controller. This self-service capability reduces communication frequency and latency, as vehicles make local decisions independently once guided by the central strategy.
3Reliability
If centralized lane-assignment strategies are used, then traffic flow regulation can be achieved, but execution rate decreases
Solution Approach 1:
The execution of lane-assignment strategies is segmented between the central controller (strategy formulation) and individual vehicles (action execution). This segmentation enables parallel processing where multiple vehicles can simultaneously execute lane changes based on their own processing of the global strategy, thereby increasing the overall execution rate while maintaining regulatory effectiveness.
Solution Approach 2:
Vehicles independently process their own state information and execute lane-change actions autonomously based on the received lane-assignment strategies, without requiring sequential coordination with the central controller for each action. This self-service execution model dramatically increases the execution rate by enabling parallel, simultaneous lane changes across multiple vehicles.
Data Source
AI summary
Systems and methods described herein relate to coordinated vehicle lane assignment using reinforcement learning. One embodiment receives from a locality manager, at a section manager that communicates with a plurality of connected vehicles in a section of a roadway, target lateral flows for two or more lanes of the roadway in the section of the roadway; receives, at the section manager, traffic state information from the plurality of connected vehicles; processes, at the section manager, the traffic state information and the target lateral flows using a reinforcement-learning-based model to determine lane-change actions for the plurality of connected vehicles, wherein the reinforcement-learning-based model is based on a single neural network with shared parameters for the plurality of connected vehicles; and transmits the lane-change actions from the section manager to the plurality of connected vehicles.