基于轮胎半径自适应的智能车辆组合定位

Integrated Localization Method for Intelligent Vehicles Based on Tire Radius Adaption

摘要:

定位系统是智能车辆环境感知系统的重要组成部分。设计了智能车辆轮胎半径自适应在线估计算法以提高车辆速度估计精度，从而在GNSS （Global Navigation Satellites System）不可用时提升IMU （inertial measurement unit）/ WSS （wheel speed sensor）组合定位系统的精度。首先，在GNSS信号良好时，考虑车轮动态设计了多模型融合的轮胎有效滚动半径自适应算法，以准确估计轮胎有效滚动半径；然后，基于自适应误差状态卡尔曼滤波设计了多传感器融合组合定位算法。实车试验结果表明，所设计的算法在初始轮胎半径有不足2%的误差时，丢失GNSS 40s可将定位精度提高30%以上。

Abstract:

Localization system is one of the most important parts in the environment perception system for intelligent vehicles. This paper proposed a GNSS （Global Navigation Satellites System）/IMU （Inertial Measurement Unit）/ WSS （Wheel Speed Sensor） integrated localization algorithm scheme based on tire effective radius adaption to improve the localization accuracy. First， a multi-model fusion tire effective rolling radius adaptive estimation algorithm considering wheel dynamics was designed. Then， the multi-sensor fusion integrated localization algorithm based on adaptive Kalman filter was proposed. The experimental results show that if the initial tire radius has small error， the accuracy of integrated localization algorithm can be improved by at least 30% with the tire radius adaption algorithm embedded in the system.