移动边缘计算辅助智能驾驶中基于高效联邦学习的碰撞预警算法

唐伦; 文明艳; 单贞贞; 陈前斌

doi:10.11999/JEIT220797

移动边缘计算辅助智能驾驶中基于高效联邦学习的碰撞预警算法

doi: 10.11999/JEIT220797

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学移动通信重点实验室重庆 400065

基金项目: 国家自然科学基金(62071078)，四川省科技计划(2021YFQ0053) ，重庆市教委科学技术研究项目(KJZD-M201800601)

详细信息

作者简介:
唐伦：男，教授，博士生导师，研究方向为新一代无线通信网络、异构蜂窝网络、软件定义无线网络等

文明艳：女，硕士生，研究方向为移动边缘计算辅助智能驾驶技术、联邦学习效率优化等

单贞贞：女，硕士生，研究方向为边缘智能协同计算资源分配、联邦学习资源协同优化等

陈前斌：男，教授，博士生导师，研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络、异构蜂窝网络等

通讯作者:
文明艳　wenming155968@163.com

中图分类号: TN929.5
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出版历程
- 收稿日期: 2022-06-16
- 修回日期: 2022-08-28
- 网络出版日期: 2022-09-05
- 刊出日期: 2023-07-10

Collision Warning Algorithm Based on Efficient Federated Learning in Mobile Edge Computing Assisted Intelligent Driving

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), Sichuan Science and Technology Program(2021YFQ0053), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)

摘要

摘要: 智能驾驶中的碰撞避免任务存在对时延要求极高和隐私保护等挑战。首先，该文提出一种基于自适应调整参数的半异步联邦学习(SFLAAP)的门控循环单元联合支持向量机(GRU_SVM)碰撞多级预警算法，SFLAAP可根据训练和资源情况动态调整两个训练参数：本地训练次数和参与聚合的局部模型数量。然后，为解决资源受限的移动边缘计算(MEC)下碰撞预警模型协作训练的效率问题，根据上述参数与SFLAAP训练时延的关系，建立训练总时延最小化模型，并将其转化为马尔可夫决策过程(MDP)。最后，在所建立的MDP中采用异步优势演员-评论家(A3C)学习求解，自适应地确定最优训练参数，从而减少碰撞预警模型的训练完成时间。仿真结果表明，所提算法有效地降低训练总时延并保证预测精度。
- 碰撞预警 /
- 联邦学习 /
- 移动边缘计算 /
- 异步优势演员-评论家算法
Abstract: Collision avoidance tasks in intelligent driving have challenges such as extremely high latency requirements and privacy protection. First, a Gated Recurrent Unit_Support Vector Machine (GRU_SVM) collision multi-level warning algorithm based on Semi-asynchronous Federated Learning with Adaptive Adjustment of Parameters (SFLAAP) is proposed. SFLAAP can dynamically adjust two training parameters according to training and resource conditions: the number of local training times and the number of local models participating in aggregation. Then, in order to solve the efficiency problem of collaborative training of collision warning model under resource-constrained Mobile Edge Computing (MEC), according to the relationship between the above parameters and SFLAAP training delay, a model for minimizing the total training delay is established, and it is transformed into a Markov Decision Process (MDP). Finally, in the established MDP, the Asynchronous Advantage Actor-Critic (A3C) algorithm is employed to determine adaptively the optimal training parameters, thereby reducing the training completion time of the collision warning model. The simulation results show that the proposed algorithm can effectively reduce the total training delay and ensure prediction accuracy.
- Collision warning /
- Federated Learning(FL) /
- Mobile Edge Computing(MEC) /
- Asynchronous Advantage Actor-Critic (A3C) algorithm

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图 1 系统场景

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图 2 基于SFLAAP的GRU_SVM碰撞多级预警方案的训练过程

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图 3 SFLAAP示意图

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图 4 3种模型的预测性能对比

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图 5 DRL的训练收敛性能

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图 6 不同算法的模型性能对比

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算法1 基于A3C的SFLAAP算法
输入：全局参数 ${{\mathbf{\theta }}_a}$ 和 ${{\mathbf{\theta }}_c}$ ，折扣因子 $\gamma$ ，熵超参数 $\beta$ ，主Agent的最　大步数 ${T_{ {{\rm{global}} - {\rm{max}}} } }$ 和步数 ${t_{ { {\rm{global} } } } } = 0$ ，子Agent的最大步数　 ${T_{ {{\rm{local}} - {\rm{max}}} } }$ 和步数 ${t_{ {{\rm{local}}} } } = 0$ ，主Agent的更新频率 ${T_{ {{\rm{up}}} } }$ ，actor和　critic的学习步长 ${\alpha _1}$ 和 ${\alpha _2}$
输出：最优动作 $a'$
(1) for epoch $k \in \{ 1,2,\cdots,K\}$ do
(2) 　　for ${t_{ {{\rm{global}}} } } \le {T_{ {{\rm{global}} - {\rm{max}}} } }$ do
(3) 　　　　 ${\rm{d}}{ {\mathbf{\theta } }_a} \leftarrow 0$ , ${\rm{d}}{ {\mathbf{\theta } }_c} \leftarrow 0$ , ${ {\mathbf{\theta } }'_a} = { {\mathbf{\theta } }_a}$ , ${ {\mathbf{\theta } }'_c} = { {\mathbf{\theta } }_c}$
(4) 　　　　for ${t_{ {{\rm{local}}} } } \in \{ 0,1,\cdots,{T_{ {{\rm{local - {\rm{max}}}}} } }\}$ do
(5) 　　　　　　actor网络根据策略获得SFLAAP参数取值动作
(6) 　　　　　　由式(15)得到奖励 ${r_t}$ 和下一个状态 ${s_{t + 1}}$
(7) 　　　　　　if ${s_t} \ne { {\rm{termina} } }{ {{\rm{l}}}^{} }{s_t}$ 或 ${t_{ {{\rm{local}}} } }\% ({T_{ {{\rm{up}}} } } - 1) \ne 0$ then
(8) 　　　　　　　　将 ${\rm{d}}{ {\mathbf{\theta } }_a}$ , ${\rm{d}}{ {\mathbf{\theta } }_c}$ 推送至主Agent进行异步更新
(9) 　　　　　　　　 ${ {\mathbf{\theta } }_a} \leftarrow { {\mathbf{\theta } }_a} + {\alpha _1}{\rm{d}}{ {\mathbf{\theta } }_a}$ 和 ${ {\mathbf{\theta } }_c} \leftarrow { {\mathbf{\theta } }_c} + {\alpha _2}{\rm{d}}{ {\mathbf{\theta } }_c}$
(10) 　　　　　　子Agent的critic网络获得 $V({s_t};{{\boldsymbol{\theta}} '_c} )$
(11) 　　　　　　for $t = {t_{ { {\rm{local} } } } },{t_{ { {\rm{local} } } } } - 1,\cdots,{t_{ { {\rm{local} } } } } + 1 - {T_{ {{\rm{up}}} } }$ do
(12) 　　　　　　　 $V({s_t};{{\boldsymbol{\theta}} '_c} ) \leftarrow {r_t} + \gamma V({s_t};{{\boldsymbol{\theta}} '_c})$
(13) 　　　　　　　计算全局actor网络的累积梯度：　　　　　　 ${\boldsymbol{d} }{ {\mathbf{\theta } }_a} \leftarrow {\boldsymbol{d} }{ {\mathbf{\theta } }_a} + { { {\text{∇} } } _{ {{\boldsymbol{\theta}} '_a} } }\log \pi ({a_t}\|{s_t};{ {\boldsymbol{\theta} } '_a} )A({s_t},{a_t};{ {\boldsymbol{\theta} } '_c} )$ 　　　　　　 $+ \beta { {\text{∇} } _{ {{\boldsymbol{\theta}} '_a} } }H(\pi ({s_t};{ {\boldsymbol{\theta} } '_a} ))$
(14) 　　　　　　计算全局critic网络的累积梯度：　　　 ${\rm{d}}{ {\mathbf{\theta } }_c} \leftarrow {\rm{d}}{ {\mathbf{\theta } }_c} + \partial {(A({s_t},{a_t};{ {\boldsymbol{\theta} }' _c} ))^2}/\partial { {\boldsymbol{\theta} } _c}$
(15) 　　　　　end for
(16) 　　　　end if
(17) 　　　end for
(18) 　　　 ${t_{ {{\rm{global}}} } } = {t_{ {{\rm{global}}} } } + 1$
(19) 　　end for
(20) 　　选择最优动作 ${a'_k} = ({\tau _k},{N_k})$
(21) 　　在的步骤(7)中，根据最优动作 ${a'_k}$ 更新 ${\tau _{k + 1}}$ 和 ${N_{k + 1}}$
(22) end for

下载: 导出CSV

表 1 SVM分类结果

	准确度	召回率	F1分数	精度
0	0.97	0.98	0.97	0.96
1	0.84	0.78	0.80
2	0.90	0.91	0.90
宏平均	0.90	0.89	0.89	—

下载: 导出CSV

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