In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scen...In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scenarios,including traffic flow,speed and density,was researched.Considering the complexity of traffic flow and fuzziness of human thinking,fuzzy c-means clustering algorithm based on the genetic algorithm(GA-FCM) was adopted in soft classification of urban road traffic conditions.Genetic algorithm(GA) introduced into fuzzy clustering could avoid fuzzy c-means(FCM) algorithm converging to the local infinitesimal point,which made the cluster result more precise.By means of computer simulation,data exchanging environment in IoV was imitated,and then test data set was divided into four parts.The simulation indicates that the identification method is feasible and effective for urban road traffic conditions in IoV scenarios.展开更多
5G is the hottest topic in telecommunication area in recent years.ITU has defined 5G as IMT-2020,and presented the scenarios of IMT-2020.These scenarios for IMT-2020 can be concluded as follows：-Enhanced Mobile Broad...5G is the hottest topic in telecommunication area in recent years.ITU has defined 5G as IMT-2020,and presented the scenarios of IMT-2020.These scenarios for IMT-2020 can be concluded as follows：-Enhanced Mobile Broadband：Mobile Broadband addresses the human-centric use cases for access to multi-media content,services and data.The demand for mobile展开更多
representation capability of deep learning(DL) and the optimal decision making and control capability of reinforcement learning(RL), is a good approach to address this problem. Traffic environment is built up by combi...representation capability of deep learning(DL) and the optimal decision making and control capability of reinforcement learning(RL), is a good approach to address this problem. Traffic environment is built up by combining intelligent driver model(IDM) and lane-change model as behavioral model for vehicles. To increase the stochastic of the established traffic environment, tricks such as defining a speed distribution with cutoff for traffic cars and using various politeness factors to represent distinguished lane-change style, are taken. For training an artificial agent to achieve successful strategies that lead to the greatest long-term rewards and sophisticated maneuver, deep deterministic policy gradient(DDPG) algorithm is deployed for learning. Reward function is designed to get a trade-off between the vehicle speed, stability and driving safety. Results show that the proposed approach can achieve good autonomous maneuvering in a scenario of complex traffic behavior through interaction with the environment.展开更多
基金the Humanity and Social Science Youth Foundation of Ministry of Education in China(No.12YJC630200)Natural Science Foundations of Gansu Province in China(Nos.145RJZA190,1308RJYA042)the Social Science Planning Project of Gansu Province in China(No.13YD066)
文摘In accordance with the specific deployment way of infrastructure and data exchanging technology in the Internet of vehicles(IoV),the acquiring and calculating method for three basic traffic flow parameters in IoV scenarios,including traffic flow,speed and density,was researched.Considering the complexity of traffic flow and fuzziness of human thinking,fuzzy c-means clustering algorithm based on the genetic algorithm(GA-FCM) was adopted in soft classification of urban road traffic conditions.Genetic algorithm(GA) introduced into fuzzy clustering could avoid fuzzy c-means(FCM) algorithm converging to the local infinitesimal point,which made the cluster result more precise.By means of computer simulation,data exchanging environment in IoV was imitated,and then test data set was divided into four parts.The simulation indicates that the identification method is feasible and effective for urban road traffic conditions in IoV scenarios.
文摘5G is the hottest topic in telecommunication area in recent years.ITU has defined 5G as IMT-2020,and presented the scenarios of IMT-2020.These scenarios for IMT-2020 can be concluded as follows：-Enhanced Mobile Broadband：Mobile Broadband addresses the human-centric use cases for access to multi-media content,services and data.The demand for mobile
文摘representation capability of deep learning(DL) and the optimal decision making and control capability of reinforcement learning(RL), is a good approach to address this problem. Traffic environment is built up by combining intelligent driver model(IDM) and lane-change model as behavioral model for vehicles. To increase the stochastic of the established traffic environment, tricks such as defining a speed distribution with cutoff for traffic cars and using various politeness factors to represent distinguished lane-change style, are taken. For training an artificial agent to achieve successful strategies that lead to the greatest long-term rewards and sophisticated maneuver, deep deterministic policy gradient(DDPG) algorithm is deployed for learning. Reward function is designed to get a trade-off between the vehicle speed, stability and driving safety. Results show that the proposed approach can achieve good autonomous maneuvering in a scenario of complex traffic behavior through interaction with the environment.
