Distributed Collaborative Path Planning Algorithm for Multiple Autonomous vehicles Based on Digital Twin
Focusing on the problems of difficult cooperation between vehicles,low quality of the model trained by cooperation and poor effect of direct application of the obtained results to physical vehicles in the process of path planning for multiple Autonomous Vehicles(AVs),a distributed collaborative path planning algorithm is proposed for multiple AVs based on Digital Twin(DT).The algorithm is based on the Credibility-Weighted Decentralized Federated Reinforcement Learning(CWDFRL)to realize the path planning of multiple AVs.In this paper,the path planning problem of a single AVs is first modeled as the problem of minimizing the average task completion time under the constraints of driving behavior,which is transformed into Markov Decision Process(MDP)and solved by Deep Deterministic Policy Gradient algorithm(DDPG).Then Federated Learning(FL)is used to ensure the cooperation between vehicles.Aiming at the problem of low quality of global model update in centralized FL,this paper uses a decentralized FL training method based on dynamic node selection of reliability to improve the low quality.Finally,the DT is used to assist the training of the Decentralized Federated Reinforcement Learning(DFRL)model,and the trained model can be quickly deployed directly to the real-world AVs by taking advantage of the twin's ability of learning from DT environment.The simulation results show that compared with the existing methods,the proposed training framework can obtain a higher reward,effectively improve the utilization of the vehicle's own speed,and at the same time reduce the average task completion time and collision probability of the vehicle swarm.
Digital Twin(DT)Autonomous drivingDecentralized Federated Reinforcement Learning(DFRL)Path planning
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