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汽车安全与节能学报

汽车安全与节能学报 ›› 2021, Vol. 12 ›› Issue (4): 522-527.DOI: 10.3969/j.issn.1674-8484.2021.04.011

• 汽车安全 • 上一篇    下一篇

基于自适应扩展Kalman滤波的路侧多传感器融合

武一民1(), 郑凯元1,2, 高博麟2,*(), 陈明1,2, 王义锋3   

  1. 1.河北工业大学 机械工程学院,天津 300130,中国
    2.清华大学 车辆与运载学院,北京 100084,中国
    3.滴滴出行,北京 100089,中国
  • 收稿日期:2021-06-02 出版日期:2021-12-31 发布日期:2022-01-10
  • 通讯作者: 高博麟
  • 作者简介:* 高博麟,副研究员。E-mail: gaobolin@tsinghua.edu.cn。
    武一民（1963—）,男（汉）,河北,教授。E-mail: wuyimin2000@126.com。
  • 基金资助:
    广东省重点领域研发计划(2019B090912001);清华大学-滴滴未来出行联合研究中心项目资助(20192911567)

Roadside multi-sensor fusion based on adaptive extended Kalman filter

WU Yimin1(), ZHENG Kaiyuan1,2, GAO Bolin2,*(), CHEN Ming1,2, WANG Yifeng3   

  1. 1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
    2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    3. Didi Chuxing, Beijing 100089, China
  • Received:2021-06-02 Online:2021-12-31 Published:2022-01-10
  • Contact: GAO Bolin

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摘要：

路侧感知是云控车路协同感知的组成部分。为提升路侧传感器的感知精度和稳定性,该文提出了一种基于量测噪声自适应扩展Kalman滤波器(AEKF)的路侧多传感器融合方法。基于路侧相机、激光雷达、毫米波雷达的感知结果,实现了异质传感器目标级数据的融合。采用一种量测噪声在线获取方法,检测了传感器测量值的稳定性,生成了量测噪声的修正系数,自适应调整了量测噪声;经过了实车试验。结果表明：相较于单传感器,采用该多传感器融合方法使横向距离估计精度提高9.7%,纵向距离估计精度提高5.4%,速度估计精度提高26.6%;由于该算法的横向距离估计精度提高44.9%,纵向距离估计精度提高21.3%,速度估计精度提高64.4%; 因此,该文AEKF算法的估计精度高于传统扩展Kalman滤波算法(EKF) 的精度。

关键词: 自动驾驶汽车, 路侧感知, 多传感器融合, 自适应滤波, 扩展Kalman滤波（EKF）

Abstract:

Roadside perception is a component of cloud control cooperative vehicle infrastructure perception. This paper proposed a fusion method for roadside multi-sensors based on measurement noise Adaptive Extended Kalman Filter (AEKF) to improve the perception accuracy and stability of roadside sensors. The fusion of target-level data from heterogeneous sensors was realized based on the sensing results of roadside cameras, lidars, and millimeter-wave radars. An online acquisition method of measurement noise was used to detect the stability of the sensor measurement value, to generate correction coefficients for the measurement noise, and to adjust adaptively the measurement noise. This method was tested in a real vehicle. The results show that this multi-sensor fusion method improves the accuracy of lateral distance estimation by 9.7%, the longitudinal distance estimation accuracy by 5.4%, and the speed estimation accuracy by 26.6%, compared with a single sensor; With the estimation accuracy of the traditional Extended Kalman Filter (EKF) algorithm being improved by 44.9% in the horizontal distance, by 21.3% in the longitudinal distance, and by 64.4% in the speed; Therefore, the AEKF algorithm estimation accuracy of this paper is higher than that of the traditional EKF algorithm.

Key words: autonomous vehicles, roadside perception, multi-sensor fusion, adaptive filters, extended Kalman filter (EKF)

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