Li Yushan, Zhang Wenbo, Ji Xuewu, Ren Chuanxiang, Wu Jian
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2019 Apr 2;19(7):1584. doi: 10.3390/s19071584.
The curvature of the lane output by the vision sensor caused by shadows, changes in lighting and line breaking jumps over in a period of time, which leads to serious problems for unmanned driving control. It is particularly important to predict or compensate the real lane in real-time during sensor jumps. This paper presents a lane compensation method based on multi-sensor fusion of global positioning system (GPS), inertial measurement unit (IMU) and vision sensors. In order to compensate the lane, the cubic polynomial function of the longitudinal distance is selected as the lane model. In this method, a Kalman filter is used to estimate vehicle velocity and yaw angle by GPS and IMU measurements, and a vehicle kinematics model is established to describe vehicle motion. It uses the geometric relationship between vehicle and relative lane motion at the current moment to solve the coefficient of the lane polynomial at the next moment. The simulation and vehicle test results show that the prediction information can compensate for the failure of the vision sensor, and has good real-time, robustness and accuracy.
视觉传感器输出的车道曲率会因阴影、光照变化和线中断而在一段时间内出现跳跃，这给无人驾驶控制带来严重问题。在传感器跳跃期间实时预测或补偿真实车道尤为重要。本文提出了一种基于全球定位系统（GPS）、惯性测量单元（IMU）和视觉传感器多传感器融合的车道补偿方法。为了补偿车道，选择纵向距离的三次多项式函数作为车道模型。在该方法中，利用卡尔曼滤波器通过GPS和IMU测量来估计车速和偏航角，并建立车辆运动学模型来描述车辆运动。它利用当前时刻车辆与相对车道运动之间的几何关系来求解下一时刻车道多项式的系数。仿真和车辆测试结果表明，该预测信息能够补偿视觉传感器的失效，具有良好的实时性、鲁棒性和准确性。
