高精度轨迹预测算法每5毫秒更新一次，实测误差仅1.25厘米/米。
Implementation and evaluation of a prediction algorithm for an autonomous vehicle
- 融合动态自行车模型与扩展卡尔曼滤波，每5毫秒预测一次轨迹。
- 实测定位偏差仅1.25厘米/米，比运动学模型精度提升82.6%。
- 提出新型光学追踪测轮胎侧偏刚度方法，适合自动驾驶系统开发。
本文提出一种自动驾驶车辆轨迹预测算法，每5毫秒更新一次。对比了运动学模型与动力学自行车模型，后者在高速下表现更优。通过实验确定了车辆质量、质心位置、转动惯量及侧偏刚度等参数，其中侧偏刚度采用新型光学位置追踪测量法。该模型集成于扩展卡尔曼滤波器，并以C++在ROS节点中实现。整个测试行程中，位置偏差仅为1.25厘米/米，相较运动学模型精度提升高达82.6%。
原文摘要 · Abstract (English)
This paper presents a prediction algorithm that estimates the vehicle trajectory every five milliseconds for an autonomous vehicle. A kinematic and a dynamic bicycle model are compared, with the dynamic model exhibiting superior accuracy at higher speeds. Vehicle parameters such as mass, center of gravity, moment of inertia, and cornering stiffness are determined experimentally. For cornering stiffness, a novel measurement procedure using optical position tracking is introduced. The model is incorporated into an extended Kalman filter and implemented in a ROS node in C++. The algorithm achieves a positional deviation of only 1.25 cm per meter over the entire test drive and is up to 82.6% more precise than the kinematic model.
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