Dynamic Estimation of Headway Distance in Vehicle Platoon System under Unexpected Car-following Situations
Dynamic Estimation of Headway Distance in Vehicle Platoon System under Unexpected Car-following Situations
复制标题
DOI：
10.1016/j.trpro.2015.03.014
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Hironori Suzuki;T. Nakatsuji
中科院分区：
文献类型：
--
作者：
Hironori Suzuki;T. Nakatsuji
排中车辆之间的车头距离很难测量，除非车辆配备了昂贵的设备，例如激光或雷达传感器，或图像处理系统。本文提出了一种替代方法，根据可测量变量（例如队列中选定车辆的加速度和速度）间接估计车头距离。假设有六辆车的排，应用粒子滤波器（PF）和无迹卡尔曼滤波器（UKF），以便根据排中三辆（或所有）车辆的测量变量来估计每辆车的车头时距。 PF和UKF的状态空间模型由车头时距守恒方程和传统跟车模型给出。为了评估 PF 和 UKF 性能，准备了两种场景：一种假设模型参数的先验知识与实际观察到的后验信息不同，另一种是排车在跟车过程中意外减速的情况。在这两种情况下，状态空间模型本身无法精确描述车头距离的动态，并且应用PF和UKF来最小化由不正确的模型参数和意外的车辆减速引起的车头距离误差。数值分析表明，即使跟驰模型没有表达真实的跟驰现象，PF 和 UKF 都能成功估计车头距离。我们还确定，如果所有车辆都配备探测车，从而能够测量加速度和速度，则在精确估计车头距离时，PF 优于 UKF。然而，当并非对所有车辆进行测量时，尤其是当排中的车辆在跟车过程中意外减速时，UKF 比 PF 更稳定。
The headway distance between vehicles in a platoon is difficult to measure unless the vehicles are equipped with costly equipment such as laser or radar sensors, or an image processing system. This paper proposes an alternate approach to estimating headway distance indirectly from measurable variables such as the acceleration rate and velocity of selected vehicles in the platoon. Assuming a six-vehicle platoon, a particle filter (PF) and an unscented Kalman filter (UKF) are applied in order to estimate the headway of each vehicle based on the measurement variables of three (or all) vehicles in the platoon. The state-space models of the PF and the UKF are given by the conservation equation of headway and the conventional car-following model. To evaluate the PF and UKF performance, two scenarios were prepared: one assumed that prior knowledge of a model parameter differed from what was actually observed as posterior information, the other was a situation where a platoon vehicle slowed down unexpectedly during the car-following process. In both situations, the state-space model itself was unable to describe the dynamics of headway distance precisely, and the PF and the UKF were applied to minimize the headway distance errors caused by the incorrect model parameter and the unexpected vehicle slowdown. Numerical analysis demonstrated that both the PF and UKF were successful in estimating the headway distance, even when the car-following model did not express the true car-following phenomena. We also determined that, if all vehicles are equipped as probe cars, and thus capable of measuring the acceleration rate and velocity, the PF is superior to UKF when estimating the headway distance precisely. However, UKF is more stable than the PF when measurements are not taken from all vehicles, especially when a vehicle in the platoon unexpectedly slows downs during the car-following process.