|Table of Contents|

Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年02期
Page:
172-183
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic
Author(s):
LYU Neng-chao1; 2;  ZHENG Meng-fan1; 2;  HAO Wei3;  WU Chao-zhong1; 2;  WU Hao-ran1; 2
(1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China; 2. Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan 430063, Hubei, China; 3. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410205, Hunan, China)
Keywords:
traffic information;  advanced driver assistance system;  objective risk perception;  collision warning;  natural driving;  time-to-collision
PACS:
U491.6
DOI:
10.19818/j.cnki.1671-1637.2020.02.014
Abstract:
In order to improve the adaptability of advanced driver assistance system(ADAS)warning algorithm in complex driving environments, a comprehensive warning algorithm named the objective risk perception(ORP)algorithm based on the vehicle kinematics and risk perception characteristic was proposed. The analysis and derivation under typical risk conditions show that the proposed warning algorithm is a comprehensive mode of time headway(THW), time-to-collision(TTC)and safety margine(SM)based warning algorithms. In order to calibrate the parameter thresholds of the proposed warning algorithm, a total of 4 500 km natural driving experiments were carried out, and finally 409 valid near-crash events were extracted. The distribution characteristics of objective risk perception parameters when release accelerator and press brake were obtained. The risk warning algorithm parameters were calibrated based on the near-crash events and their parameter characteristics extracted from the natural driving data. The forward collision warning algorithm was developed under a simulated driving environment, and the verification experiments of the algorithm were carried out based on four risk scenarios. Research result shows that based on the parameter calibration of natural driving data, the two-level warning parameter thresholds of the ORP warning algorithm are 1.4 and 0.8 s, respectively. Based on the comparison of driving behavior under typical risk conditions, in terms of warning effectiveness, the ORP warning algorithm is slightly higher than the RP warning algorithm, and the effectiveness of secondary early warnings is significantly higher than that of the TTC warning algorithm. In terms of the average minimum time-to-collision of all driving segments under the warning algorithm, the ORP warning algorithm is 2.02 s, the RP warning algorithm is 1.90 s, and the TTC warning algorithm is 1.65 s, which shows that the ORP warning algorithm can adapt to the risk identification in complex risk environment. Based on a large number of natural driving test based parameter calibration and effect verification, the proposed warning algorithm can be used for the risk identification of advanced driver assistance systems.6 tabs, 8 figs, 27 refs.

References:

[1] WANG Jian-qiang, ZHENG Yang, LI Xiao-fei, et al. Driving risk assessment using near-crash database through data mining of tree-based model[J]. Accident Analysis and Prevention, 2015, 84: 54-64.
[2] 刘永涛,华 珺,赵俊玮,等.场景风险引导下驾驶人应激反应能力研究[J].交通信息与安全,2019,37(3):35-41,50.
LIU Yong-tao, HUA Jun, ZHAO Jun-wei, et al. Emergency response ability of drivers under risk guidance situations [J]. Journal of Transport Information and Safety, 2019, 37(3): 35-41, 50.(in Chinese)
[3] 徐美华,张凯欣,蒋周龙.一种实时车道线偏离预警系统算法设计和实现[J].交通运输工程学报,2016,16(3):149-158.
XU Mei-hua, ZHANG Kai-xin, JIANG Zhou-long. Algorithm design implementation for a real-time lane departure pre-warning system[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 149-158.(in Chinese)
[4] GIETELINK O, PLOEG J, DE SCHUTTER B, et al.
Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations[J]. Vehicle System Dynamics, 2006, 44(7): 569-590.
[5] 吕能超,秦 羚,罗 毅.先进驾驶辅助系统的接受程度及其影响因素分析[J].交通信息与安全,2017,35(6):54-59.
LYU Neng-chao, QIN Ling, LUO Yi. An analysis of the acceptance of advanced driver assistance systems and influencing factors[J]. Journal of Transport Information and Safety, 2017, 35(6): 54-59.(in Chinese)
[6] 吕能超,段至诚,吴超仲.驾驶经验对先进驾驶辅助系统的作用研究[J].交通运输系统工程与信息,2017,17(6):48-55.
LYU Neng-chao, DUAN Zhi-cheng, WU Chao-zhong. The impact of driving experience on advanced driving assistant systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 48-55.(in Chinese)
[7] SEILER P, SONG B, HEDRICK J K. Development of a
collision avoidance system[J]. Automotive Engineering, 1998, 106(9): 24-28.
[8] YANG H H, PENG H. Development and evaluation of collision warning/collision avoidance algorithms using an errable driver model [J]. Vehicle System Dynamics, 2010, 48(10): 525-535.
[9] CHEN Y L, SHEN K Y, WANG S C. Forward collision warning system considering both time-to-collision and safety braking distance[J]. International Journal of Vehicle Safety, 2013, 6(4): 347-360.
[10] TAWFEEK M H, EL-BASYOUNY K. A perceptual forward
collision warning model using naturalistic driving data[J]. Canadian Journal of Civil Engineering, 2016, 45(10): 899-907.
[11] DANG R, ZHANG F, TAKAE Y, et al. Braking characteristics of Chinese driver in highway and urban road[J]. IFAC Proceedings Volumes, 2013, 46(21): 322-327.
[12] CHEN Rong, SHERONY R, GABLER H C. Comparison of time to collision and enhanced time to collision at brake application during normal driving[R]. New York: SAE International, 2016.
[13] KONDOH T, YAMAMURA T, KITAZAKI S, et al.
Identification of visual cues and quantification of drivers' perception of proximity risk to the lead vehicle in car-following situations[J]. Journal of Mechanical Systems for Transportation and Logistics, 2008, 1(2): 170-180.
[14] LU Guang-quan, CHENG Bo, LIN Qing-feng, et al. Quantitative indicator of homeostatic risk perception in car following[J]. Safety Science, 2012, 50(9): 1898-1905.
[15] AOKI H, HUNG N V Q, YASUDA H. Perceptual risk
estimate(PRE): an index of the longitudinal risk estimate[C]∥NHTSA. Proceedings of the 22nd International Technical Conference on the Enhanced Safety of Vehicles(ESV). Washington DC: NHTSA, 2011: 1-6.
[16] 迟瑞娟,肖 庆,丁洁云.基于风险状态预估的弯道车辆前撞报警算法[J].中国公路学报,2013,26(4):146-153,168.
CHI Rui-juan, XIAO Qing, DING Jie-yun. Vehicle forward collision warning algorithm based on risk prediction on curved roads [J]. China Journal of Highway and Transport, 2013, 26(4): 146-153, 168.(in Chinese)
[17] 吴 斌,朱西产,沈剑平,等.基于自然驾驶数据的危险评估算法研究[J].汽车工程,2017,39(8):907-914.
WU Bin, ZHU Xi-chan, SHEN Jian-ping, et al. A study on risk assessment algorithm based on natural driving data [J]. Automotive Engineering, 2017, 39(8): 907-914.(in Chinese)
[18] 李 霖,朱西产,马志雄.驾驶员在真实交通危险工况中的制动反应时间[J].汽车工程, 2014,36(10):1225-1229,1253.
LI Lin, ZHU Xi-chan, MA Zhi-xiong. Driver brake reaction time under real traffic risk scenarios[J]. Automotive Engineering, 2014, 36(10): 1225-1229, 1253.(in Chinese)
[19] 李 霖,朱西产,陈海林.驾驶员制动和转向避撞极限[J].同济大学学报(自然科学版),2016,44(11):1743-1748.
LI Lin, ZHU Xi-chan, CHEN Hai-lin. Drivers' collision avoidance limit by braking and steering[J]. Journal of Tongji University(Natural Science), 2016, 44(11): 1743-1748.(in Chinese)
[20] LYU Neng-chao, DENG Chao, XIE Lian, et al. A field
operational test in China: exploring the effect of an advanced driver assistance system on driving performance and braking behavior[J]. Transportation Research Part F: Traffic Psychology and Behavior, 2019, 65, 730-747.
[21] 吴超仲,吴浩然,吕能超.人机共驾智能汽车的控制权切换与安全性综述[J].交通运输工程学报,2018,18(6):131-141.
WU Chao-zhong, WU Hao-ran, LYU Neng-chao. Review of research on control switch safety of man-machine shared driving intelligent vehicles[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 131-141.(in Chinese)
[22] GUO Feng, SIMONS-MORTON B G, KLAUER S E, et al. Variability in crash and near-crash risk among novice teenage drivers: a naturalistic study[J]. The Journal of Pediatrics, 2013, 163(6): 1670-1676.
[23] 杨 曼,吴超仲,张 晖,等.行车安全事件的驾驶风险影响因素研究[J].交通信息与安全,2018,36(5):34-39.
YANG Man, WU Chao-zhong, ZHANG Hui, et al. Influencing factors of driving risk based on critical incident events[J]. Journal of Transport Information and Safety, 2018, 36(5): 34-39.(in Chinese)
[24] DAGAN E, MANO O, STEIN G P, et al. Forward collision warning with a single camera[C]∥IEEE. 2004 IEEE Intelligent Vehicles Symposium. New York: IEEE, 2004: 37-42.
[25] BELLA F, RUSSO R. A collision warning system for rear-end collision: a driving simulator study[J]. Procedia—Social and Behavioral Sciences, 2011, 20: 676-686.
[26] 王 畅,付 锐,张 琼,等.换道预警系统中参数TTC特性研究[J].中国公路学报,2015,28(8):91-100.
WANG Chang, FU Rui, ZHANG Qiong, et al. Research on parameter TTC characteristics of lane change warning system[J]. China Journal of Highway and Transport, 2015, 28(8): 91-100.(in Chinese)
[27] MUHRER E, REINPRECHT K, VOLLRATH M. Driving with a partially autonomous forward collision warning system: how do drivers react[J]. Human Factors, 2012, 54(5): 698-708.

Memo

Memo:
-
Last Update: 2020-05-22