自动驾驶中周围车辆识别与信息地图构建技术

doi:10.3969/j.issn.1674-8484.2022.01.013

摘要/Abstract

摘要：

为提高自动驾驶时单传感器对周围车辆识别成功率及工程实用性,提出了一种基于相机图像与激光雷达信息相融合的车辆识别和地图构建方法。对相机与激光雷达进行了联合标定和时间配准。对图像中的车辆阴影与激光雷达检测信息进行提取,获得两者间的坐标关联度特征,并根据其阈值范围确定周围是否存在车辆。将相机图像信息与激光雷达检测信息进行融合,构建周围车辆的信息地图,进行了实验验证。结果表明：采用坐标关联度特征对两传感器采集的信息进行融合,车辆识别的正确率比用D-S证据理论法结果高4.25%;本车辆信息地图,可以在安全的前提下实现基于三阶Bessel曲线的超车轨迹行驶。

关键词: 自动驾驶, 车辆识别, 地图构建, 信息融合, 时间配准

Abstract:

A method of vehicle recognition and map construction during automatic driving was proposed based on the fusion of camera image and lidar information to improve satisfactory success rate of single sensor recognition of surrounding vehicles and engineering practicability. The joint calibration and time registration of the camera and the lidar were carried out. The vehicle shadows and lidar detection information in the image were extracted, and the coordinate correlation features between the two were obtained, and whether there was a vehicle nearby was determined according to its threshold range. The camera image information was fused with the detection information of lidar, and the information map of the surrounding vehicles was constructed, and the experimental verification was carried out. The results show that the accuracy of vehicle recognition is 4.25% higher than that of (D-S) evidence theory by using the feature of coordinate correlation degree. The vehicle information map in this paper can realize overtaking track driving based on third-order Bessel curve on the premise of safety.

Key words: automatic pilot, vehicle identification, map construction, information fusion, time registration

中图分类号:

U471.15

李茂月, 吕虹毓, 河香梅, 徐光岐, 于伟. 自动驾驶中周围车辆识别与信息地图构建技术[J]. 汽车安全与节能学报, 2022, 13(1): 131-141.

LI Maoyue, LV Hongyu, HE Xiangmei, XU Guangqi, YU Wei. Surrounding vehicle recognition and information map construction technology in automatic driving[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 131-141.

图/表 26

参考文献 16

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相机位置	Δx/mm	Δy/mm	Δz/mm	α/(°)	β/(°)	γ/(°)
前置	30	0	-13	12.22	2.18	-24.52
左置	-15	-60	-41	2.47	-5.94	-24.14
右置	-15	-60	-41	2.00	2.49	-24.79
后置	-140	0	-42.5	11.93	2.38	-25.00

相机位置	Δx/mm	Δy/mm	Δz/mm	α/(°)	β/(°)	γ/(°)
前置	30	0	-13	12.22	2.18	-24.52
左置	-15	-60	-41	2.47	-5.94	-24.14
右置	-15	-60	-41	2.00	2.49	-24.79
后置	-140	0	-42.5	11.93	2.38	-25.00

图像帧序号	横坐标				计算结果
图像帧序号	x_C1	x_C1	x_R1	x_R1	Δx₁	Δx₁	ε₁	ε₂
7	92	135	103	151	11	16	0.74	0.67
13	98	138	113	147	15	9	0.62	0.71
19	278	366	285	374	7	8	0.92	0.91
28	492	547	518	563	26	16	0.53	0.64
41	472	515	497	528	25	13	0.41	0.58
57	501	542	523	557	22	15	0.46	0.56
76	488	527	504	535	16	8	0.59	0.74
95	273	358	289	375	16	17	0.81	0.80
117	81	129	95	145	14	16	0.71	0.68
128	104	145	125	157	21	12	0.49	0.63

图像帧序号	横坐标				计算结果
图像帧序号	x_C1	x_C1	x_R1	x_R1	Δx₁	Δx₁	ε₁	ε₂
7	92	135	103	151	11	16	0.74	0.67
13	98	138	113	147	15	9	0.62	0.71
19	278	366	285	374	7	8	0.92	0.91
28	492	547	518	563	26	16	0.53	0.64
41	472	515	497	528	25	13	0.41	0.58
57	501	542	523	557	22	15	0.46	0.56
76	488	527	504	535	16	8	0.59	0.74
95	273	358	289	375	16	17	0.81	0.80
117	81	129	95	145	14	16	0.71	0.68
128	104	145	125	157	21	12	0.49	0.63

图像帧	横坐标						计算结果 1			计算结果 2
图像帧	x_C3	x_C4	x_C5	x_R3	x_R4	x_R5	Δx₃	Δx₄	Δx₅	σ₁	σ₂	σ₃	σ₄
12	412	457	537	433	481	559	21	24	22	0.53	0.50	0.70	0.72
23	445	483	571	462	514	591	17	31	20	0.55	0.40	0.65	0.74
37	83	158	196	112	164	209	29	6	13	0.61	0.88	0.84	0.71
43	124	173	207	146	182	215	22	9	8	0.55	0.75	0.74	0.76
73	93	161	205	122	185	220	29	24	15	0.57	0.62	0.45	0.57
97	136	214	263	172	241	275	36	27	12	0.54	0.61	0.45	0.65
115	463	502	594	483	531	608	20	29	14	0.49	0.40	0.68	0.82
134	439	485	547	461	514	572	22	29	25	0.52	0.45	0.53	0.57