Active safety evaluation method of advanced driving assistance systems

doi:10.3969/j.issn.1674-8484.2023.01.008

Abstract

Abstract:

An method of active safety of advanced driver assistance systems was established to meet the advanced driver assistance systems(ADAS) needs of the active safety of autonomous vehicles based on quantification of margins and uncertainties (QMU), The evaluation system and process applicable to the active safety of autonomous vehicles were developed by combining the basic principle of QMU, using the order relation analysis (G1) methods to determine the weighting factors of the evaluating indicator, and using the fuzzy comprehensive evaluation method to derive the comprehensive evaluation results. The autonomous emergency braking (AEB) function of an automatic vehicle was tested and evaluated in the closed test site. The results show that the evaluation results of the test car speed, acceleration, distance from the target car and steering wheel corner speed are 1.347 3, 6.398 1, 1.079 3 and 1.462 4, and the comprehensive result is 2.783 4, indicating that the proposed evaluation method makes up for the lack of horizontal comparison of single-indicator evaluation results and objectively quantifies the longitudinal evaluation results of active safety of autonomous driving systems.

Key words: active safety, advanced driver assistance systems (ADAS), evaluation method, quantification of margins and uncertainties (QMU)

CLC Number:

U461.91

WANG Yinting, WU Changshui. Active safety evaluation method of advanced driving assistance systems[J]. Journal of Automotive Safety and Energy, 2023, 14(1): 62-68.

Figures/Tables 8

References 21

[1]	李克强, 戴一凡, 李升波, 等. 自动网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8(1): 1-14.
	LI Keqiang, DAI Yifan, LI Shengbo, et al. State-of-the-art and technical trends of intelligent and connected vehicles[J]. *J Autom Safe Energ*, 2017, 8(1): 1-14. (in Chinese)
[2]	Kirovskii O. Determination of validation testing scenarios for an ADAS functionality: Case study[R]. SAE Technical Paper, 2019.
[3]	Kim B J, Lee S B. Safety evaluation of autonomous vehicles for a comparative study of camera image distance information and dynamic characteristics measuring equipment[J]. IEEE Access, 2022, 10: 18486-18506. doi: 10.1109/ACCESS.2022.3151075 URL
[4]	Son T D, Bhave A,Van der Auweraer H. Simulation-based testing framework for autonomous driving development[C]// 2019 IEEE Int’l Conf Mech (ICM). IEEE, 2019, 1: 576-583.
[5]	Braun H, Gerke M, Marchthaler R. Safety in autonomous driving-evaluation by maximum entropy[J]. ATZ Worldwide, 2021, 123(3): 62-67.
[6]	陈君毅, 李如冰, 邢星宇, 等. 自动驾驶车辆自动性评价研究综述[J]. 同济大学学报(自然科学版), 2019, 47(12): 1785-1790+1824.
	CHEN Junyi, LI Rubing, XING Xingyu, et al. Survey on Interllingce evaluation of autonomous vehicles[J]. J Tongji Univ (Nat Sci Edit), 2019, 47(12): 1785-1790+1824. (in Chinese)
[7]	朱冰, 张培兴, 刘斌, 等. 基于自然驾驶数据的自动驾驶汽车安全性评价方法[J/OL]. 中国公路学报: 1-12. (2022-06-05), http://kns.cnki.net/kcms/detail/61.1313.U.20220304.1706.002.html.
	ZHU Bing, ZHANG Peixing, LIU Bin, et al. Safety evaluation method of automated vehicle based on naturalistic driving data[J/OL]. China J Highway Transport: 1-12. (2022-06-05), http://kns.cnki.net/kcms/detail/61.1313.U.20220304.1706.002.html. (in Chinese)
[8]	朱冰, 张培兴, 赵健. 面向多维度逻辑场景的自动驾驶安全性聚类评价方法[J]. 汽车工程, 2020, 42(11): 1458-1463+1505. doi: 10.19562/j.chinasae.qcgc.2020.11.002
	ZHU Bing, ZHANG Peixing, ZHAO Jian. Clustering evaluation method of autonomous driving safety for multi-dimensional logical scenario[J]. Autom Engi, 2020, 42(11): 1458-1463+1505. (in Chinese)
[9]	魏子茹, 卢延辉, 王鹏宇, 等. 基于CRITIC法的灰色关联理论在无人驾驶车辆测试评价中的应用[J]. 机械工程学报, 2021, 57(12): 99-108. doi: 10.3901/JME.2021.12.099
	WEI Ziru, LU Yanhui, WANG Pengyu, et al. Application of grey correlation theory based on critic method in autonomous vehicles test and evaluation[J]. J Mech Engi, 2021, 57(12): 99-108. (in Chinese)
[10]	李茹, 马育林, 田欢, 等. 基于熵值和G1法的自动驾驶车辆综合自动定量评价[J]. 汽车工程, 2020, 42(10): 1327-1334. doi: 10.19562/j.chinasae.qcgc.2020.10.005
	LI Ru, MA Yulin, TIAN Huan, et al. Comprehensive intelligent quantitative evaluation of autonomous vehicle based on entropy and G1 methods[J]. Autom Engi, 2020, 42(10): 1327-1334. (in Chinese)
[11]	彭忠明, 王玉明. 基于试验数据的性能裕量及其不确定性量化方法[J]. 信息与电子工程, 2010, 8(6): 682-686.
	PENG Zhongming, WANG Yuming. Methods of quantifying performance parameter margins and uncertainties based on test data[J]. Info Elect Engi, 2010, 8(6): 682-686. (in Chinese)
[12]	Helton C J, Pilch M. Quantification of margins and uncertainties[J]. Reliab Engi Syst Safe, 2011, 96(9): 959-964.
[13]	Wallstrom C T. Quantification of margins and uncertainties: A probabilistic framework[J]. Relia Engi Syst Safe, 2011, 96(9): 959-964.
[14]	陈陌, 郭亚军, 于振明. 改进型序关系分析法及其应用[J]. 系统管理学报, 2011, 20(3): 352-355.
	CHEN Mo, GUO Yajun, YU Zhenming. An improved method for rank correlation analysis and its application[J]. J Syst Manag, 2011, 20(3): 352-355. (in Chinese)
[15]	谢朝阳, 李贵杰, 彭忠明, 等. 基于证据理论和代理模型的QMU分析[J]. 电子科技大学学报, 2018, 47(1): 66-72.
	XIE Chaoyang, LI Guijie, PENG Zhongming, et al. Quantification of margins and uncertainties approach using evidence theory and surrogate model[J]. J Univ Elect Sci Tech China, 2018, 47(1): 66-72. (in Chinese)
[16]	Menzel T, Bagschik G, Maurer M. Scenarios for development, test and validation of automated vehicles[C]// 2018 IEEE Intel Vehi Symp (IV). IEEE, 2018: 1821-1827.
[17]	徐晓岭, 王磊. 统计学[M]. 北京: 人民邮电出版社, 2015:1-419.
	XU Xiaoling, WANG Lei. Statistics[M]. Beijing: Posts & Telecom Press, 2015: 1-419. (in Chinese)
[18]	朱冰, 张培兴, 赵健, 等. 基于场景的自动驾驶汽车虚拟测试研究进展[J]. 中国公路学报, 2019, 32(6): 1-19. doi: 10.19721/j.cnki.1001-7372.2019.06.001
	ZHU Bing, ZHANG Peixing, ZHAO Jian, et al. Review of scenario-based virtual validation methods for automated vehicles[J]. China J High Transport, 2019, 32(6): 1-19. (in Chinese)
[19]	韩勇, 李永强, 许永虹, 等. 基于VRUs深度事故重建的AEB效能对头部损伤风险的影响[J]. 汽车安全与节能学报, 2021, 12(4): 490-498.
	HAN Yong, LI Yongqiang, XU Yonghong, et al. Effectiveness of AEB system for head injury risk based on VRUs in-depth accident reconstruction[J]. J Autom Safe Energ, 2021, 12(4): 490-498. (in Chinese)
[20]	林国庆, 逯超, 韩龙飞, 等. 汽车自动紧急制动系统行人测试与评价方法[J]. 汽车安全与节能学报, 2020, 11(3): 296-304.
	LIN Gouqing, LU Chao, HAN Longfei, et al. Test and evaluation method of pedestrian automatic emergency braking system[J]. J Autom Safe Energ, 2020, 11(3): 296-304. (in Chinese)
[21]	宫诚举, 李伟伟, 郭亚军. 群体评价中的序关系分析法[J]. 运筹与管理, 2020, 29(11): 152-156.
	GONG Chengju, LI Weiwei, GUO Yajun. Rank correlation analysis method in group evaluation[J]. Operat Res Manag Sci, 2020, 29(11): 152-156. (in Chinese)

r_k	说明
1.0	指标X_n_-1比X_n同样重要
1.2	指标X_n_-1比X_n稍微重要
1.4	指标X_n_-1比X_n明显重要
1.6	指标X_n_-1比X_n强烈重要
1.8	指标X_n_-1比X_n极端重要

r_k	说明
1.0	指标X_n_-1比X_n同样重要
1.2	指标X_n_-1比X_n稍微重要
1.4	指标X_n_-1比X_n明显重要
1.6	指标X_n_-1比X_n强烈重要
1.8	指标X_n_-1比X_n极端重要

功能名称	测试场景	测试项目
AEB/FCW	CCR(车辆)	前车静止 (CCRs)
		前车慢行 (CCRm)
		前车制动 (CCRb)
	Ped(成人)	车辆碰撞近端行人(CPNA)
	Ped(成人)	车辆碰撞远端行人(CPFA)
LKA/LDW	保持直道行驶	设定横向偏移速度

功能名称	测试场景	测试项目
AEB/FCW	CCR(车辆)	前车静止 (CCRs)
		前车慢行 (CCRm)
		前车制动 (CCRb)
	Ped(成人)	车辆碰撞近端行人(CPNA)
	Ped(成人)	车辆碰撞远端行人(CPFA)
LKA/LDW	保持直道行驶	设定横向偏移速度

测试场景	目标物速度（km·h^-1）	测试车速（km·h^-1）	偏置率 %	场景权重
CCRm	20	60	100	2
CPFA	6.5	60	50	2