车载雷达关键场景的数据采集与标记方法

doi:10.19562/j.chinasae.qcgc.2024.12.012

Abstract

Abstract:

False alarm and missed alarm of automotive radar are key factors affecting the safety and reliability of autonomous driving systems， thus requiring a large amount of labeled test data for targeted research. However， the occurrence probability of false alarm and missed alarm is low， and the unstable status of radar targets makes it difficult to label them. Therefore， in this paper， firstly efficient test schemes are designed to obtain key radar data based on the generation mechanism of radar false alarm and missed alarm. Then， by constructing a correlation function to quantify the correlation between radar targets and scene targets and using genetic algorithms to optimize this function， an automatic labeling method for radar targets is established. Finally， the effectiveness of the proposed method is verified through real data acquisition. The experimental results show that the proposed method can efficiently obtain crucial false alarm and missed alarm data. The labeling method in this paper can accurately identify radar targets corresponding to scene targets and distinguish between false alarm and real targets.

Key words: autonomous driving, millimeter-wave radar, false alarm, missed alarm, object labeling

Kaibo Huang,Weiwen Deng,Ying Wang,Rui Zhao,Juan Ding. Data Collection and Annotation Method for Radar on Some Key Scenarios[J].Automotive Engineering, 2024, 46(12): 2257-2266.

Figures/Tables 15

场景

高速

场景

郊区

场景

城市

场景

低速场景

速度/（km·h^-1）

60-120

40-80

30-60

0-20

距离/

m

33.3-66.6

16.6-83.5

1-20

速度/（km·h^-1）	低速	中速	高速
速度/（km·h^-1）	0-30	30-60	60-120
距离/ $m$	近距离	中距离	远距离
距离/ $m$	0-20	20-83.5	83.5-160
车辆之间的距离/ $m$	近距离	中远距离
车辆之间的距离/ $m$	1-7	7-160

参数	$w r 0$	$w r 1$	$w r 2$	$w r 3$	$w r 4$
初始值	1	4	4	0.13	0.13
优化值	0.21	1.8	5.3	0.56	0.97

References 19

1	FRIEDRICH B.The effect of autonomous vehicles on traffic［M］//MAURER M， GERDES J C， LENZ B， et al. Autonomous driving：technical， legal and social aspects. Berlin， Heidelberg：Springer Berlin Heidelberg， 2016：317-334.
2	MEINL F， STOLZ M， KUNERT M， et al.An experimental high performance radar system for highly automated driving［C］. 2017IEEE MTT-S International Conference on Microwaves for Intelligent Mobility （ICMIM）. IEEE， 2017： 71-74.
3	GONG Y， XUE Y， DONG Q， et al. Quality evaluation of image dataset based on label file［C］. 2021 8th International Conference on Dependable Systems and Their Applications （DSA）： 639-646.
4	MA Yifang， WANG Zhenyu， YANG Hong，et al.Artificial intelligence applications in the development of autonomous vehicles： a survey［J］.IEEE/CAA J. Autom. Sinica， 2020，7（2）： 315-329.
5	詹军，董学才，洪峰，等. 智能汽车传感器实时功能模型及验证［J］. 汽车工程， 2019， 41（7）： 731-737，743.
	ZHAN J， DONG X C， HONG F， et al. Real-time functional model and verification of intelligent vehicle sensors［J］. Automotive Engineering， 2019， 41（7）： 731-737，743.
6	LI Y， CHAO X. Toward sustainability： trade-off between data quality and quantity in crop pest recognition［J］. Frontiers in Plant Science，2021，12.
7	LEDERER A， CAPONE A， UMLAUFT J， et al. How training data impacts performance in learning-based control［J］. IEEE Control Systems Letters， 2020，5： 905-910.
8	BARNES D， GADD M， MURCUTT P，et al. The Oxford Radar RobotCar Dataset： a radar extension to the Oxford RobotCar Dataset［C］. 2020 IEEE International Conference on Robotics and Automation （ICRA）：6433-6438.
9	LIM T， MARKOWITZ S， DO M. RaDICaL： a synchronized FMCW radar， depth， IMU and RGB camera data dataset with low-level FMCW radar signals［J］. IEEE Journal of Selected Topics in Signal Processing，2021， 15： 941-953.
10	KIM G， PARK Y， CHO Y， et al. MulRan： multimodal range dataset for urban place recognition［C］. 2020 IEEE International Conference on Robotics and Automation （ICRA）： 6246-6253.
11	WANG Y， JIANG Z， LI Y， et al. RODNet： a real-time radar object detection network cross-supervised by camera-radar fused object 3D localization［J］. IEEE Journal of Selected Topics in Signal Processing， 2021，15：954-967.
12	CAESAR H， BANKITI V， LANG A， et al. NuScenes： a multimodal dataset for autonomous driving［C］. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）： 11618-11628.
13	SCHUMANN O， HAHN M， SCHEINER N， et al. RadarScenes： a real-world radar point cloud data set for automotive applications［C］. 2021 IEEE 24th International Conference on Information Fusion （FUSION）： 1-8.
14	OUAKNINE A， NEWSON A， REBUT J， et al. CARRADA dataset： camera and automotive radar with range- angle- doppler annotations［C］. 2020 25th International Conference on Pattern Recognition （ICPR）： 5068-5075.
15	李勇滔，孙晨旭，郑伟光，等.基于毫米波雷达与视觉融合的碰撞预警［J］.汽车工程，2023，45（9）：1666-1676.
	LI Y T， SUN C X， ZHENG W G， et al. Collision warning based on fusion of millimeter wave radar and vision［J］. Automotive Engineering， 2023， 45（9）： 1666-1676.
16	LI X， DENG W， ZHANG S. et al. Research on millimeter wave radar simulation model for intelligent vehicle［J］. Int.J Automot. Technol.，2020， 21： 275-284.
17	MITCHELL M. An introduction to genetic algorithms［M］. Cambridge， Mass. ： MIT Press， 1996.
18	WHITLEY L. A genetic algorithm tutorial［J］. Statistics and Computing， 199，44：65-85.
19	GOLDBERG D. Genetic algorithms in search optimization and machine learning［M/OL］. 1988. https：//doi.org/10.5860/choice.27-0936.

数据集	场景目标	数据量	漏报数据数量	漏报概率/%	虚警数据数量	虚警概率/%
考虑车辆交互	目标车1	63 650	15 550	24.43	15 129	23.77
考虑车辆交互	目标车2	63 650	13 506	21.22	12 920	20.30
不考虑车辆交互	目标车1	16 721	847	5.07	1 560	9.33
不考虑车辆交互	目标车2	16 721	602	3.60	1 393	8.33

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Data Collection and Annotation Method for Radar on Some Key Scenarios

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