Dynamic State Estimation in Vehicle Platoon System by Applying Particle Filter and Unscented Kalman Filter
Dynamic State Estimation in Vehicle Platoon System by Applying Particle Filter and Unscented Kalman Filter
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应用粒子滤波器和无迹卡尔曼滤波器的车队系统动态估计
DOI：
10.1016/j.procs.2013.10.025
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Hironori Suzuki
中科院分区：
文献类型：
--
作者：
公文俊平;大橋正和 編著;Hironori Suzuki and Takashi Nakatsuji;Hironori Suzuki
本研究应用粒子滤波器（PF）与无迹卡尔曼滤波器（UKF）来估计六车车队系统的车头时距与车速。这两个反馈估计器被用来估计车头时距和速度间接从几个测量变量，如加速度和速度，在队列中选定的车辆。为了评估所提出的两个估计器的性能，创建了人工跟车数据，以覆盖各种速度范围，包括一些加速和减速的情况。此外，当改变安装在队列系统中的探测汽车的数量时，进行估计精度的比较。数值分析表明，PF成功地估计车头时距和速度更准确地比UKF，即使当安装的探测汽车的数量较少，他们的位置是不同的排内。UKF的估计不准确，除100%安装场景外，所有探测车穿透期间过滤器均不稳定。UKF被认为只有当所有车辆都配备有传感系统时才能产生稳定和准确的估计，而PF不需要大量的探测汽车来生成准确的估计，而不管它们在车队中的位置如何。
This study applies a particle filter (PF) and an unscented Kalman filter (UKF) to estimate the headway and velocity of a six- vehicle platoon system. These two feedback estimators were used to estimate headway and velocity indirectly from several measurement variables, such as acceleration rate and velocity, of selected vehicles in the platoon. To evaluate the performance of the proposed two estimators, artificial car-following data were created to cover various speed ranges that include some acceleration and deceleration scenarios. Also, a comparison of estimation accuracy is conducted when varying the number of probe cars installed in the platoon system. Numerical analysis showed that the PF succeeded in estimating headway and velocity more accurately than the UKF, even when the number of probe cars installed is fewer and their location is varied within the platoon. The estimations by the UKF were inaccurate and the filter was unstable during all probe car penetrations except during the 100% installation scenario. The UKF is considered to yield stable and accurate estimates only when all vehicles are equipped with the sensing system, whereas the PF does not require numerous probe cars to generate accurate estimates regardless of their location in the platoon.