为了提高城市交叉口通行效率,在车路协同环境下对无信号交叉口优化控制方法进行了研究。设计并采用基于时延Petri网(Timed Petri Net,TdPN)的无信号交叉口优化控制方法,利用TdPN建立无信号交叉口控制模型,并依此建立交叉口车辆最快消散目标函数,采用递归方式求解车辆最优通过序列;利用Q-Paramics构建基于车路协同环境下的无信号交叉口仿真平台,分析该方法在不同交通流量下对交叉口平均延迟、平均停车次数、平均排队长度和平均速度4个交通参数的影响,并将其与传统的信号控制方法进行对比。研究结果表明:基于TdPN的无信号优化控制方法能够在一定程度上缓解中小交通流量下的交叉口通行问题,并且其控制结果明显优于传统的感应控制方法。
The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.
