融合点线特征的图关系优化3D车道线检测方法

doi:10.16182/j.issn1004731x.joss.25-0591

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (5): 1303-1319.doi: 10.16182/j.issn1004731x.joss.25-0591

• • 上一篇

融合点线特征的图关系优化3D车道线检测方法

姜彦吉¹^,², 肖星佚¹^,², 董浩²^,³^,⁴, 于淼⁵, 黄金山⁶, 刘大千¹, 费博雯¹

^1.辽宁工程技术大学软件学院，辽宁葫芦岛 125105
^2.优策(江苏)安全科技有限公司 OpenSafe实验室，江苏苏州 215100
^3.清华大学苏州汽车研究院(相城)，江苏苏州 215100
^4.苏州工学院汽车工程学院，江苏常熟 215500
^5.中国第一汽车集团研发总院，吉林长春 130000
^6.中国第一汽车集团有限公司工程技术部，吉林长春 130012

收稿日期:2025-06-24 修回日期:2025-11-11 出版日期:2026-05-21 发布日期:2026-05-29
通讯作者: 董浩
第一作者简介:姜彦吉(1985-)，男，副教授，博士，研究方向为预期功能安全和自动驾驶视觉感知。
基金资助:
国家自然科学基金青年基金(62302509);国家自然科学基金青年基金(62303477);国家自然科学基金面上项目(52274205);浙江省自然科学基金面上项目(LMS25G010003);葫芦岛市科技计划(2023JH(1)4/02b);广东省科技创新战略专项市县科技创新支撑项目(STKJ2023071)

Detection Method for 3D Lanes Based on Graph Relationship Optimization Integrating Point and Lane Features

Jiang Yanji¹^,², Xiao Xingyi¹^,², Dong Hao²^,³^,⁴, Yu Miao⁵, Huang Jinshan⁶, Liu Daqian¹, Fei Bowen¹

^1.School of Software, Liaoning Technical University, Huludao 125105, China
^2.OpenSafe Lab, Utcer (Jiangsu) Safety Technology Co. , Ltd, Suzhou 215100, China
^3.Suzhou Automobile Research Institute (Xiangcheng), Tsinghua University, Suzhou 215100, China
^4.School of Automotive Engineering, Suzhou University of Technology, Changshu 215500, China
^5.China FAW Group Research and Development Institute, Changchun 130000, China
^6.Engineering & Technology Department, China FAW Group Co. , Ltd, Changchun 130012, China

Received:2025-06-24 Revised:2025-11-11 Online:2026-05-21 Published:2026-05-29
Contact: Dong Hao

摘要/Abstract

摘要：

针对复杂道路条件下车道线因细长结构、占比小导致视觉特征模糊、定位精度不足，进而威胁自动驾驶道路安全的问题，提出一种融合点与线特征的图关系优化3D车道线检测方法GPLNet (graph-based point and lane optimization network)。通过骨干网络完成初步特征提取，再经联合查询嵌入生成模块获取具备几何约束的3D空间位置编码；利用图关系优化网络对车道点与线级别特征开展图关系计算及优化建模，强化车道线上下文感知能力；借助3D预测头实现车道线预测与损失函数计算。实验结果表明：所提方法整体性能优于现有主流3D车道线检测算法，且在低能见度车道线场景下检测精度更高，验证了该方法的有效性。

关键词: 自动驾驶, 3D车道线检测, 位置编码, 查询嵌入, 图关系优化

Abstract:

Under complex road conditions, the thin and elongated structure and small proportion of lanes lead to blurred visual features and insufficient positioning accuracy, which in turn threatens the road safety of autonomous driving. To address these issues, a 3D lane detection method or graph-based point and lane optimization network (GPLNet), based on graph relationship optimization integrating point and lane features, was proposed. Preliminary feature extraction was completed by the backbone network. 3D spatial positional coding with geometric constraints was obtained through a joint query embedding generation module. A graph relationship optimization network was utilized to perform graph relationship calculation and optimization modeling on point- and lane-level features of lanes to enhance the context awareness capability of lanes. Lane prediction and loss function calculation were achieved with a 3D prediction head. Experimental results indicate that the overall performance of the proposed method is superior to existing mainstream 3D lane detection algorithms, and the detection accuracy is higher in low-visibility lane scenarios, which verifies the effectiveness of the method.

Key words: autonomous driving, 3D lane detection, positional coding, query embedding, graph relationship optimization

中图分类号:

TP391.9

姜彦吉,肖星佚,董浩等 . 融合点线特征的图关系优化3D车道线检测方法[J]. 系统仿真学报, 2026, 38(5): 1303-1319.

Jiang Yanji,Xiao Xingyi,Dong Hao,et al . Detection Method for 3D Lanes Based on Graph Relationship Optimization Integrating Point and Lane Features[J]. Journal of System Simulation, 2026, 38(5): 1303-1319.

图/表 17

图1

图2

图3

图4

图5

图6

图7

表1

表2

图8

表3

表4

表5

表6

各模块消融研究

联合查询嵌入	图关系优化	F1值/%	C.ACC/%	$E x$ /(C/m)	$E x$ /(F/m)	$E z$ /(C/m)	$E z$ /(F/m)
×	×	67.19	89.17	0.276	0.343	0.099	0.132
√	×	69.34	91.49	0.260	0.319	0.098	0.128
×	√	68.60	90.60	0.259	0.320	0.098	0.131
√	√	71.30	93.03	0.247	0.307	0.097	0.125

表6

表7

图关系优化消融研究

结构关系	类别关系	F1值/%	C.ACC/%	$E x$ /(C/m)	$E x$ /(F/m)	$E z$ /(C/m)	$E z$ /(F/m)
×	×	69.34	91.49	0.260	0.319	0.098	0.128
√	×	71.13	92.94	0.248	0.311	0.097	0.126
×	√	69.45	91.97	0.255	0.324	0.097	0.130
√	√	71.30	93.03	0.247	0.307	0.097	0.125

表7

图9

表8

距离阈值ϵ的消融研究

$ϵ$	F1值/ %	C.ACC/ %	$E x$ /(C/m)	$E x$ / (F/m)	$E z$ / (C/m)	$E z$ / (F/m)
0	69.36	91.54	0.256	0.322	0.128	0.154
10	70.77	91.96	0.251	0.316	0.098	0.130
15	70.93	92.87	0.252	0.311	0.097	0.125
20	70.96	92.72	0.245	0.293	0.095	0.119
25	71.15	92.93	0.249	0.310	0.097	0.126
30	70.93	92.63	0.234	0.315	0.095	0.129
35	70.24	92.25	0.245	0.319	0.096	0.127

表8

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方法	F1值/%	C.ACC/%	E_x /(C/m)	E_x /(F/m)	E_z /(C/m)	E_z /(F/m)
3D-LaneNet^[29]	44.1	‒	0.479	0.572	0.367	0.443
Performer^[13]	50.5	92.3	0.485	0.553	0.364	0.431
CurveFormer++^[30]	52.7	88.1	0.337	0.801	0.198	0.676
Anchor3DLane^[14]	53.1	90.0	0.300	0.311	0.103	0.139
LaneCPP^[31]	60.3	‒	0.264	0.310	0.077	0.117
LATR^[15]	61.9	91.9	0.219	0.259	0.073	0.104
GPLNet	63.1	92.4	0.195	0.230	0.070	0.100

方法	全部	上下坡	弯道	极端天气	夜间	交叉路口	合并与分流
3D-LaneNet	44.1	40.8	46.5	47.5	41.5	32.1	41.7
Performer	50.5	42.4	55.6	48.6	46.6	40.0	50.7
CurveFormer++	52.7	48.3	59.4	50.6	48.4	45.0	48.1
Anchor3DLane	53.1	45.5	56.2	51.9	47.2	44.2	50.5
LaneCPP	60.3	53.6	64.4	56.7	54.9	52.0	58.7
LATR	61.9	55.2	68.2	57.1	55.4	52.3	61.5
GPLNet	63.1	53.3	68.5	58.5	55.7	53.8	62.0

方法	参数量×10⁶	帧率/(帧/s)	F1值/%
Performer	63.11	11.67	50.5
LATR	44.47	13.34	61.9
GPLNet	43.55	13.75	63.1

方法	F1值/ %	精确率/%	召回率/%	距离误差/m
3D-LaneNet	44.73	35.16	61.46	0.127
Performer	74.33	80.30	69.18	0.074
Anchor3DLane	74.44	80.50	69.23	0.064
DecoupleLane^[32]	75.07	81.19	69.26	0.062
CurveFormer++	77.85	82.22	72.79	0.081
LATR	80.59	86.12	75.73	0.052
GPLNet	80.62	83.92	77.57	0.040

场景	评估指标	3D-LaneNet	CLGO	PersFormer	CurveFormer^[33]	Anchor3DLane	LATR	GPLNet
平衡场景	F1值/%	86.4	91.9	92.9	95.8	95.6	96.8	96.9
	E_x /(C/m)	0.068	0.061	0.054	0.078	0.052	0.022	0.019
	E_x /(F/m)	0.477	0.361	0.356	0.326	0.306	0.253	0.243
	E_z /(C/m)	0.015	0.029	0.01	0.018	0.015	0.007	0.005
	E_z /(F/m)	0.202	0.25	0.234	0.219	0.223	0.202	0.199
稀有场景	F1值/%	72.0	86.1	87.5	95.6	94.4	96.1	96.3
	E_x /(C/m)	0.166	0.147	0.107	0.182	0.094	0.050	0.040
	E_x /(F/m)	0.855	0.735	0.782	0.737	0.693	0.600	0.580
	E_z /(C/m)	0.039	0.071	0.024	0.039	0.027	0.015	0.011
	E_z /(F/m)	0.521	0.609	0.602	0.561	0.579	0.532	0.527
视觉变体	F1值/%	72.5	87.3	89.6	90.8	91.4	95.1	95.2
	E_x /(C/m)	0.115	0.084	0.074	0.125	0.068	0.045	0.036
	E_x /(F/m)	0.601	0.464	0.430	0.410	0.367	0.315	0.302
	E_z /(C/m)	0.032	0.045	0.015	0.028	0.020	0.016	0.015
	E_z /(F/m)	0.23	0.312	0.266	0.254	0.232	0.228	0.220

融合点线特征的图关系优化3D车道线检测方法

Detection Method for 3D Lanes Based on Graph Relationship Optimization Integrating Point and Lane Features

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