Collision-free parking recommendation based on multi-agent reinforcement learning in vehicular crowdsensing  

作　　者：Xin Li Xinghua Lei Xiuwen Liu Hang Xiao 

机构地区：[1]College of Computer Science and Technology,China University of Petroleum(East China),QingDao,266580,China

出　　处：《Digital Communications and Networks》2024年第3期609-619,共11页数字通信与网络（英文版）

基　　金：supported in part by the Natural Science Foundation of Shandong Province of China(ZR202103040180);the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-004;the Fundamental Research Funds for the Central Universities under Grant 20CX05019A.

摘　　要：The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle participation.However,instead of being an isolated module,the incentive mechanism usually interacts with other modules.Based on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real time.Specifically,we utilize an LSTM method to predict parking areas roughly for recommendations accurately.Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects.In order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making.Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is Pareto-optimal.Finally,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.

关 键 词：Incentive mechanism Non-cooperative VCS game Multi-agent reinforcement learning Collision-free parking strategy Vehicular crowdsensing 

分 类 号：U46[机械工程—车辆工程]