A Selective Federated Reinforcement Learning Strategy for Autonomous Driving

【Author】 Fu, Yuchuan; Li, Changle; Yu, F. Richard; Luan, Tom H.; Zhang, Yao

【Source】IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

【影响因子】9.551

【Abstract】Currently, the complex traffic environment challenges the fast and accurate response of a connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to collaborate and share knowledge. To remedy that, this paper proposes a selective federated reinforcement learning (SFRL) strategy to achieve online knowledge aggregation strategy to improve the accuracy and environmental adaptability of the autonomous driving model. First, we propose a federated reinforcement learning framework that allows participants to use the knowledge of other CAVs to make corresponding actions, thereby realizing online knowledge transfer and aggregation. Second, we use reinforcement learning to train local driving models of CAVs to cope with collision avoidance tasks. Third, considering the efficiency of federated learning (FL) and the additional communication overhead it brings, we propose a CAVs selection strategy before uploading local models. When selecting CAVs, we consider the reputation of CAVs, the quality of local models, and time overhead, so as to select as many high-quality users as possible while considering resources and time constraints. With above strategic processes, our framework can aggregate and reuse the knowledge learned by CAVs traveling in different environments to assist in driving decisions. Extensive simulation results validate that our proposal can improve model accuracy and learning efficiency while reducing communication overhead.

【Keywords】Autonomous vehicles; Adaptation models; Reinforcement learning; Computational modeling; Data models; Training; Task analysis; Networked autonomous driving; federated learning; knowledge aggregation

【发表时间】

【收录时间】2022-11-28

【文献类型】观点阐述

【主题类别】

区块链应用-实体经济-交通领域