基于语义似然与高精度地图匹配的智能车辆同时定位与检测

doi:10.16183/j.cnki.jsjtu.2023.086

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (10): 1618-1628.doi: 10.16183/j.cnki.jsjtu.2023.086

基于语义似然与高精度地图匹配的智能车辆同时定位与检测

赖国良¹^,², 胡钊政¹^,²(), 周哲²^,³, 万金杰¹^,², 任靖渊¹^,²

1.武汉理工大学信息工程学院,武汉 430070
2.武汉理工大学智能交通系统研究中心,武汉 430063
3.武汉理工大学重庆研究院,重庆 401120

收稿日期:2023-03-09 修回日期:2023-04-10 接受日期:2023-04-13 出版日期:2024-10-28 发布日期:2024-11-01
通讯作者: 胡钊政,教授,博士生导师;E-mail:zzhu@whut.edu.cn.
作者简介:赖国良(1998—),硕士生,从事智能车辆定位研究.
基金资助:
国家重点研发计划(2022YFB2502904);湖北省重点研发计划项目(2022BAA082);重庆市科技创新重大研发项目(CSTB2020TIAD-STX0003);武汉市人工智能创新专项(2022010702040064)

Simultaneous Detection and Localization for Intelligent Vehicles Based on HD Map Matching and Semantic Likelihood Model

LAI Guoliang¹^,², HU Zhaozheng¹^,²(), ZHOU Zhe²^,³, WAN Jinjie¹^,², REN Jingyuan¹^,²

1. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
3. Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China

Received:2023-03-09 Revised:2023-04-10 Accepted:2023-04-13 Online:2024-10-28 Published:2024-11-01

摘要/Abstract

摘要：

车载传感器数据与高精度地图的精确匹配是提升智能车辆感知与定位的关键.提出基于语义似然模型(SLM)的高精度地图匹配新算法,实现智能车辆同时定位与目标检测任务.首先通过U-Net提取路面语义目标,利用核密度估计建立SLM.基于粒子滤波框架,利用位姿变换将高精度地图上目标抽样点映射至SLM中,计算该抽样点与传感器数据的匹配程度对每个粒子的权重更新,实现智能车辆的高精度定位.最后利用定位结果完成地图上的要素目标到图像的映射,实现目标的精准检测.利用在校园道路与城市道路环境下采集的数据对所提算法进行验证,实验结果表明,算法的平均定位误差约为14 cm,路面路标检测结果平均交并比(MIoU)均大于80.较之深度神经网络等当前最佳(SOTA)的检测方法,所提算法引入高精度地图的先验信息可显著提升智能车辆定位与目标检测性能.

关键词: 智能车辆定位, 高精度地图, 语义似然模型, 粒子滤波

Abstract:

Accurate matching between in-vehicle sensor data and high-definition (HD) maps is crucial to improve the performance of perception and localization of intelligent vehicles. A novel algorithm of HD map matching based on the developed semantic likelihood model (SLM) is proposed to achieve intelligent vehicle localization and object detection simultaneously. First, semantic pavement objects are extracted from front-view images by using U-Net, and SLM is constructed with kernel density estimation (KDE). Under a particle filter framework, the likelihood between the sensor data and HD map is calculated by projecting each sample point from HD map with pose transformation onto SLM to update the weight of each particle. Simultaneously, accurate detection of pavement markings is accomplished by projecting all elements onto the HD map with the computed localization results. In the experiment, data collected on campus and on urban roads are used to validate the proposed algorithm. The experimental results show that the localization errors in both scenarios are about 14 cm, and the mean intersection over union (MIoU) of road marking detection is above 80. The results demonstrate that the proposed algorithm can significantly improve both localization and detection performance by effectively utilizing the prior information of HD maps, compared with the state of the art (SOTA) methods, such as deep learning-based detection methods.

Key words: intelligent vehicle localization, high-definition (HD) map, semantic likelihood model (SLM), particle filter

中图分类号:

TP242.6

赖国良, 胡钊政, 周哲, 万金杰, 任靖渊. 基于语义似然与高精度地图匹配的智能车辆同时定位与检测[J]. 上海交通大学学报, 2024, 58(10): 1618-1628.

LAI Guoliang, HU Zhaozheng, ZHOU Zhe, WAN Jinjie, REN Jingyuan. Simultaneous Detection and Localization for Intelligent Vehicles Based on HD Map Matching and Semantic Likelihood Model[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1618-1628.

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参考文献 21

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方法	最小误差/cm	最大误差/cm	平均误差/cm
本文算法	3.4	49.9	14.4
文献[11]	11.0	125.8	54.7
文献[13]	11.8	59.5	30.4

方法	最小误差/cm	最大误差/cm	平均误差/cm
本文算法	2.8	26.9	14.0
文献[11]	2.2	103.8	45.9
文献[13]	17.2	29.8	24.2

类别	IoU/%
类别	本文方法	Swin- Unet^[20]	ConvNext- Unet^[21]	U-Net
虚线	86.0	74.0	65.3	70.1
白色实线	90.6	65.7	56.3	78.1
停止线	85.7	69.9	64.8	62.3
直行箭头	87.6	72.6	71.8	60.2
直行或左转箭头	85.3	71.9	65.8	66.3
圆形井盖	81.4	66.9	60.7	58.7
方形井盖	84.3	71.3	70.1	58.6
黄色车道线	88.3	72.2	68.5	64.4
平均交并比	86.1	62.7	58.1	57.6

基于语义似然与高精度地图匹配的智能车辆同时定位与检测

Simultaneous Detection and Localization for Intelligent Vehicles Based on HD Map Matching and Semantic Likelihood Model

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