A multi-intelligence federated reinforcement learning (FRL)-based vehicle-road cooperative control system and method at the complex intersection use a vehicle-road cooperative control framework based on the Road Side Unit (RSU) static processing module and the vehicle-based dynamic processing module. The historical road information is supplied by the proposed RSU module. The Federated Twin Delayed Deep Deterministic policy gradient (FTD3) algorithm is proposed to connect the federated learning (FL) module and the reinforcement learning (RL) module. The FTD3 algorithm transmits only neural network parameters instead of vehicle samples to protect privacy. Firstly, FTD3 selects only specific networks for aggregation to reduce the communication cost. Secondly, FTD3 realizes the deep combination of FL and RL by aggregating target critic networks with smaller Q-values. Thirdly, RSU neural network participates in aggregation rather than training, and only shared global model parameters are used.
Multi-intelligence federal reinforcement learning-based vehicle-road cooperative control system and method at complex intersection
2024-01-02
Patent
Electronic Resource
English
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
European Patent Office | 2024
|Multi-intersection signal cooperative control method based on deep reinforcement learning
European Patent Office | 2025
|European Patent Office | 2020
|Vehicle-road cooperative traffic control system and method for intersection
European Patent Office | 2023
|