期刊文献+

基于行人安全的交通信号灯智能控制算法研究

Research on Intelligent Control Algorithm of Traffic Light Based on Pedestrian Safety
下载PDF
导出
摘要 提出了一种基于深度确定性策略梯度(DDPG,deep deterministic policy gradient)的行人安全智能交通信号控制算法;通过对交叉口数据的实时观测,综合考虑行人安全与车辆通行效率,智能地调控交通信号周期时长,相位顺序以及相位持续时间,实现交叉路口安全高效的智能控制;同时,采用优先经验回放提高采样效率,加速了算法收敛;由于行人安全与车辆通行效率存在相互矛盾,研究中通过精确地设计强化学习的奖励函数,折中考虑行人违规引起的与车辆的冲突量和车辆通行的速度,引导交通信号灯学习路口行人的行为,学习最佳的配时方案;仿真结果表明在动态环境下,该算法在行人与车辆冲突量,车辆的平均速度、等待时间和队列长度均优于现有的固定配时方案和其他的智能配时方案。 An intelligent traffic signal control algorithm based on Deep Deterministic Policy Gradient(DDPG)with Pedestrian Safeis proposed.Through the real-time observation of intersection data,the pedestrian safety and vehicle traffic efficiency are comprehensively considered,and the cycle duration,phase sequence and phase duration of traffic signals are intelligently controlled,safe and efficient intelligent control of intersections is realized.Meanwhile,priority empirical replay is adopted to improve sampling efficiency and accelerate algorithm convergence.Due to the contradiction between pedestrian safety and vehicle traffic efficiency,the reward function of reinforcement learning isaccurately designed,the pedestrian-vehicle conflicts caused by pedestrian violations and the speed of vehicles is considerd,traffic light isguided to learn pedestrian behaviors at intersections,and the best timing scheme is learned.The simulation results show that in the dynamic environment,the algorithm in terms of the number of collisions between pedestrians and vehicles,the average speed of vehicles,waiting time and queue length are better than the existing fixed timing schemes and other intelligent timing schemes.
作者 张乾隆 胡智群 肖海林 ZHANG Qianlong;HU Zhiqun;XIAO Hailin(School of Computer and Information Engineering,Hubei University,Wuhan 430062,China)
出处 《计算机测量与控制》 2022年第4期114-120,共7页 Computer Measurement &Control
基金 国家自然科学基金(61901163)。
关键词 交通信号灯 动态配时 强化学习 行人安全 车辆效率 优先经验回放 traffic signal light dynamic timing reinforcement learning pedestrian safety vehicle efficiency prioritized experience replay
  • 相关文献

参考文献2

二级参考文献24

  • 1Zhu F,Ning J,Ren Y,et al.Optimization of image processing in video-based traffic monitoring[J].Elektron Elektrotech,2012,18(8):91-96.
  • 2Baskar L D,Schutter B D,Hellendoorn H.Traffic management for automated highway systems using model-based predictive control[J].IEEE Transactions on Intelligent Transportation Systems,2012,3(2):838-847.
  • 3Sutton R S,Barto A G.Reinforcement learning:an introduction[M].Cambridge:MIT Press,1998.
  • 4Mase K,Yamamoto H.Advanced traffic control methods for network management[J].IEEE Communications Magazine,1990,28(10):82-88.
  • 5Baskar L D,Schutter B D,Hellendoorn J,et al.Traffic control and intelligent vehicle highway systems:a survey[J].IET Intelligent Transport Systems,2011,5(1):38-52.
  • 6Zegeye S,Schutter B D,Hellendoorn J,et al.A predictive traffic controller for sustainable mobility using parameterized control policies[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(3):1420-1429.
  • 7Chin Y K,Wei Y K,Wei L K,et al.Q-learning traffic signal optimization within multiple intersections traffic network[C]//Proceedings of the 6th UKSIM/AMSS European Symposium on Computer Modeling and Simulation(EMS’12),2012:343-348.
  • 8Prashanth L A,Bhatnagar S.Reinforcement learning with function approximation for traffic signal control[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(2):412-421.
  • 9Wiewiora E.Potential-based shaping and Q-value initialization are equivalent[J].Journal of Artificial Intelligence Research,2003,19:205-208.
  • 10Martin M.On-line support vector machine regression[C]//Proceedings of the European Conference on Machine Learning(ECML’02),2002:173-198.

共引文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部