在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考...在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考虑到节点间未来一段时间内的最大相遇概率以及对应的相遇时间,结合分簇结果和相遇情况生成动态路由表,完成一种单副本的路由方法.该方法首先依据节点间路径的相似程度进行分簇,然后运用半马尔可夫模型预测节点间未来某一时刻的相遇概率,依据源节点和目的节点所在的分簇确定可以应用到路由中的节点集合,最后根据路径搜索策略找到最优路径,生成与当前时刻有关的动态路由表.仿真结果表明CRSMP在缓存较小的情况下投递成功率远高于DirectDeliveryRouter、FirstContactRouter和SimBetRouter三种单副本路由方式以及Spray and Wait、Epidemic和Prophet三种多副本路由协议.在10M缓存下的CRSMP有着与500M缓存下的Epidemic相近的路由性能.进一步在真实数据集上进行测试,测试结果表明CRSMP算法依然有着较好的路由性能.展开更多
As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longi...As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle(OV)during overtaking.This paper proposed a novel AOS based on hierarchical reinforcement learning,where the longitudinal reaction is given by a data-driven social preference estimation.This AOS incorporates two modules that can function in different overtaking phases.The first module based on semi-Markov decision process and motion primitives is built for motion planning and control.The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV.Based on realistic overtaking data,the proposed AOS and its modules are verified experimentally.The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data,and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.展开更多
文摘在容迟网络环境下,文中提出一种基于动态半马尔可夫路径搜索模型的分簇路由方法 CRSMP(Clustering Routing method based on Semi-Markov process and Path-finding strategy),该方法既考虑了节点拥有的社会属性所导致的分簇问题,又考虑到节点间未来一段时间内的最大相遇概率以及对应的相遇时间,结合分簇结果和相遇情况生成动态路由表,完成一种单副本的路由方法.该方法首先依据节点间路径的相似程度进行分簇,然后运用半马尔可夫模型预测节点间未来某一时刻的相遇概率,依据源节点和目的节点所在的分簇确定可以应用到路由中的节点集合,最后根据路径搜索策略找到最优路径,生成与当前时刻有关的动态路由表.仿真结果表明CRSMP在缓存较小的情况下投递成功率远高于DirectDeliveryRouter、FirstContactRouter和SimBetRouter三种单副本路由方式以及Spray and Wait、Epidemic和Prophet三种多副本路由协议.在10M缓存下的CRSMP有着与500M缓存下的Epidemic相近的路由性能.进一步在真实数据集上进行测试,测试结果表明CRSMP算法依然有着较好的路由性能.
基金The authors would like to appreciate the financial support of the National Natural Science Foundation of China(Grant No.61703041)the technological innovation program of Beijing Institute of Technology(2021CX11006).
文摘As intelligent vehicles usually have complex overtaking process,a safe and efficient automated overtaking system(AOS)is vital to avoid accidents caused by wrong operation of drivers.Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle(OV)during overtaking.This paper proposed a novel AOS based on hierarchical reinforcement learning,where the longitudinal reaction is given by a data-driven social preference estimation.This AOS incorporates two modules that can function in different overtaking phases.The first module based on semi-Markov decision process and motion primitives is built for motion planning and control.The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV.Based on realistic overtaking data,the proposed AOS and its modules are verified experimentally.The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data,and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.
