Journal of Central South University

, Volume 26, Issue 10, pp 2854–2869 | Cite as

Assessing performance of collision mitigation brake system in Chinese traffic environment

  • Zhi-guo Zhao (赵志国)
  • Xun-jia Zheng (郑讯佳)Email author
  • Jian-qiang Wang (王建强)
  • Qing Xu (许庆)
  • Kenji Kodaka


Advanced driver-assistance systems such as Honda’s collision mitigation brake system (CMBS) can help achieve traffic safety. In this paper, the naturalistic driving study and a series of simulations are combined to better evaluate the performance of the CMBS in the Chinese traffic environment. First, because safety-critical situations can be diverse especially in the Chinese environment, the Chinese traffic-accident characteristics are analyzed according to accident statistics over the past 17 years. Next, 10 Chinese traffic-accident scenarios accounting for more than 80% of traffic accidents are selected. For each typical scenario, 353 representative cases are collected from the traffic-management department of Beijing. These real-world accident cases are then reconstructed by the traffic-accident-reconstruction software PC-Crash on the basis of accident-scene diagrams. This study also proposes a systematic analytical process for estimating the effectiveness of the technology using the co-simulation platform of PC-Crash and rateEFFECT, in which 176 simulations are analyzed in detail to assess the accident-avoidance performance of the CMBS. The overall collision-avoidance effectiveness reaches 82.4%, showing that the proposed approach is efficient for avoiding collisions, thereby enhancing traffic safety and improving traffic management.

Key words

active-safety technology effectiveness assessment accident reconstruction autonomous emergency braking PC-Crash 



先进驾驶辅助系统,如本田的碰撞缓解制动系统(CMBS),有助于保障交通安全。本文采用自 然驾驶数据分析与仿真结合的方法,能更好地评估CMBS 在中国交通环境中的性能。首先,由于中国 交通环境中安全危急情况的多样性,本文根据过去17 年的交通事故统计数据,分析了中国交通事故 的特点;其次,筛选出占我国交通事故80%以上的10 种交通事故情景,并从北京市交通管理部门收 集了353 个具有代表性的真实事故案例。所有的真实事故案例都基于事故现场图,通过交通事故重建 软件PC-Crash 实现了事故场景重建。最终通过搭建PC-Crash 和rateEFFECT 联合仿真平台并进行了 总计176 组仿真,对CMBS 的避撞有效性进行了系统性的分析和评估。结果显示,其总体避碰效果达 到82.4%,说明该系统具有良好的避碰性能,能够提高交通安,改善交通管理。


主动安全技术 性能评估 事故重建 自动紧急制动 PC-Crash 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We appreciate HUANG He-ye and CHEN Long for their valuable comments and helpful work. We would also like to thank ZHANG Wen-hao, WANG Xin-peng, LIU Yi-cong, XU Meng-di, and WU Qing-hui for their help with data collection and accidents reconstruction. In addition, we are very grateful to DOU Yang-liu and HUANG Bin for their help in language expression of this manuscript.


  1. [1]
    TMBPSM. The PRC road traffic accident statistics annual report [R]. Traffic Management Bureau of the Public Security Ministry [2018-03-20]. (in Chinese)
  2. [2]
    ANDERSON R W, DOECKE S, MACKENZIE J R, PONTE G, PAINE D, PAINE M. Potential benefits of forward collision avoidance technology [R]. Queensland: Centre for Automatiove Safety Research, 2012. Scholar
  3. [3]
    Euro NCAP. Test protocol—AEB systems [R]. Brussels, Belgium: Eur New Car Assess Programme (Euro NCAP), 2013. Scholar
  4. [4]
    PARK M K, LEE S Y, KWON C K, KIM S W. Design of pedestrian target selection with funnel map for pedestrian AEB system [J]. IEEE Transactions on Vehicular Technology, 2017, 66(5): 3597–3609. DOI: 10.1109/TVT.2016.2604420.Google Scholar
  5. [5]
    KLIER T, LINN J. Corporate average fuel economy standards and the market for new vehiches [J]. The Annual Review of Resource Economics, 2011, 3: 3–445. DOI: 10.1146/annurev-resonrce-083110-120023.CrossRefGoogle Scholar
  6. [6]
    SOLEY Alexander. Regulation, industry, and the internet of cars [J]. DigiWorld Economic Journal, 2017, 105.,5 Google Scholar
  7. [7]
    PAINE M, PAINE D, SMITH J, CASE M, HALEY J, WORDEN S. Vehicle safety trends and the influence of NCAP safety ratings [J]. ESC. 2015, 20(41): 85. Scholar
  8. [8]
    C-NCAP. C-NCAP administrative rules (2018 edition) [EB/OL]. [2018-07-20]. (in Chinese)
  9. [9]
    SEGAWA E, SHIOHARA M, SASAKI S, HASHIGUCHI N, TAKASHIMA T, TOHNO M. Preceding vehicle detection using stereo images and non-scanning millimeter-wave radar [J]. IEICE Transactions on Information and Systems, 2006, E89-D(7): 2101–2108. DOI: 10.1093/ietisy/e89-d.7.2101.CrossRefGoogle Scholar
  10. [10]
    DUCHOŇ F, HUBINSKỲ P, HANZEL J, BABINEC A, TÖLGYESSY M. Intelligent vehicles as the robotic applications [J]. Procedia Engineering, 2012, 48: 48–105. DOI: 10.1016/j.proeng. 2012.09.492.CrossRefGoogle Scholar
  11. [11]
    BELGIOVANE D J, CHEN C C, CHIEN S Y P, SHERONY R. Surrogate bicycle design for millimeter-wave automotive radar pre-collision testing [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(9): 2413–2422. DOI: 10.1109/TITS.2016.2642889.CrossRefGoogle Scholar
  12. [12]
    BAI Jie, CHEN Si-han, CUI Hua, BI Xin, HUANG Li-bo. 3D automotive millimeter-wave radar with two-dimensional electronic scanning [J]. SAE Technical Paper Series, 2017. DOI: 10.4271/2017-01-0047.Google Scholar
  13. [13]
    HSU Ping-min, LI Ming-hung, CHANG Kuo-ching. Noise filtering in autonomous emergency braking systems with sensor fusions [J]. SAE Technical Paper Series, 2015. DOI: 10.4271/2015-01-0216.Google Scholar
  14. [14]
    ZHENG Xun-jia, HUANG Bin, NI Dai-heng, XU Qing. A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment [J]. Journal of Intelligent and Connected Vehicles, 2018, 1(2): 41–54. DOI: 10.1108/JICV-02-2018-0004.CrossRefGoogle Scholar
  15. [15]
    LOEB H S, KANDADAI V, MCDONALD C C, WINSTON F K. Emergency braking in adults versus novice teen drivers: response to simulated sudden driving events [J]. Transportation Research Record: Journal of the Transportation Research Board, 2015, 2516(1): 8–14. DOI: 10.3141/2516-02.CrossRefGoogle Scholar
  16. [16]
    SALAANI M K, MIKESELL D, BODAY C, ELSASSER D. Heavy vehicle hardware- in-the-loop automatic emergency braking simulation with experimental validation [J]. SAE International Journal of Commercial Vehicles, 2016, 9(2): 57–62. DOI: 10.4271/2016-01-8010.CrossRefGoogle Scholar
  17. [17]
    XU Cheng-cheng, LIU Pan, WANG Wei, JIANG Xuan, CHEN Yu-guang. Effects of behavioral characteristics of taxi drivers on safety and capacity of signalized intersections [J]. Journal of Central South University, 2014, 21(10): 4033–4042. DOI:10.1007/s11771-014-2392-7.CrossRefGoogle Scholar
  18. [18]
    AUST M L, ENGSTRÖM J, VISTRÖM M. Effects of forward collision warning and repeated event exposure on emergency braking [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013, 18: 18–34. DOI:0.1016/j.trf.2012.12.010.CrossRefGoogle Scholar
  19. [19]
    CHOI J, YI K, SUH J, KO B. Coordinated control of motor-driven power steering torque overlay and differential braking for emergency driving support [J]. IEEE Transactions on Vehicular Technology, 2014, 63(2): 566–579. DOI: 10.1109/TVT.2013.2279719.CrossRefGoogle Scholar
  20. [20]
    YIĞITER Ö C, TANYEL S. Lane by lane analysis of vehicle time headways: Case study of Izmir ring roads in Turkey [J]. KSCE Journal of Civil Engineering, 2015, 19(5): 1498–1508. DOI:10.1007/s12205-014-0267-y.CrossRefGoogle Scholar
  21. [21]
    SEGATA M, LO CIGNO R. Automatic emergency braking: Realistic analysis of car dynamics and network performance [J]. IEEE Transactions on Vehicular Technology, 2013, 62(9): 4150–4161. DOI: 10.1109/TVT. 2013.2277802.CrossRefGoogle Scholar
  22. [22]
    LEE K, PENG H. Evaluation of automotive forward collision warning and collision avoidance algorithms [J]. Vehicle System Dynamics, 2005, 43(10): 735–751. DOI: 10.1080/00423110412331282850.CrossRefGoogle Scholar
  23. [23]
    MOORE M, ZUBY D. Collision avoidance features: initial results [C]// 23rd International Conference on the Enhanced Safety of Vehicles. Seoul, Korea: 2013. Scholar
  24. [24]
    FILDES B, KEALL M, BOS N, LIE A, PAGE Y, PASTOR C, PENNISI L, RIZZ M, THOMAS P. Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes [J]. Accident Analysis & Prevention, 2015, 81: 24–29. DOI: 10.1016/j.aap.2015.03.029.CrossRefGoogle Scholar
  25. [25]
    WU Ye, ZHANG Shao-jun, HAO Ji-ming, LIU Huan, WU Xiao-meng, HU Jing-nan, WALSHM P, WALLINGTON T J, ZHANG K M. On-road vehicle emissions and their control in China: A review and outlook [J]. Science of the Total Environment. 2017, 574: 332–349. DOI: 10.1016/j.scitotenv.2016.09.040.Google Scholar
  26. [26]
    Honda China released 2016 terminal car sales [EB/OL]. [2108-07-24]. (in Chinese)
  27. [27]
    CICCHINO J B. Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates [J]. Accident Analysis & Prevention, 2017, 99, Part A: 142–152. DOI: 10.1016/j.aap.2016.11.009.CrossRefGoogle Scholar
  28. [28]
    SUGIMOTO Y, SAUER C. Effectiveness estimation method for advanced driver assistance system and its application to collision mitigation brake system [C]// Proceedings of the 19th International Technical Conference on the Enhanced Safety of Vehicles. Washington, DC: National Highway Traffic Safety Administration, 2005: 05–0148. Scholar
  29. [29]
    KUEHN M, HUMMEL T, BENDE J. Benefit estimation of advanced driver assistance systems for cars derived from real-life accidents [C]// 21st International Technical Conference on the Enhanced Safety of Vehicles ESV. Stuttgart, Germany, 2009: 18–27. Scholar
  30. [30]
    WANG Jian-qiang, WU Jian, LI Yang. The driving safety field based on driver–vehicle–road interactions [J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2203–2214. DOI: 10.1109/TITS.2015.2401837.CrossRefGoogle Scholar
  31. [31]
    WANG Jian-qiang, WU Jian, ZHENG Xun-jia, NI Dai-heng, LI Ke-qiang. Driving safety field theory modeling and its application in pre-collision warning system [J]. Transportation Research Part C: Emerging Technologies, 2016, 72: 72–306. DOI:10.1016/j.trc.2016.10.003.CrossRefGoogle Scholar
  32. [32]
    LI Yang, WANG Jian-qiang, WU Jian. Model calibration concerning risk coefficients of driving safety field model [J]. Journal of Central South University, 2017, 24(6): 1494–1502. DOI: 10.1007/s11771-017-3553-2.CrossRefGoogle Scholar
  33. [33]
    TOROYAN T, IAYCH K, PEDEN M. Global status report on road safety 2015 [R]. Geneva: World Health Organization, [2015-11-03].

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Traffic and Transportation SecurityHuaiyin Institute of TechnologyHuaianChina
  2. 2.State Key Laboratory of Automotive Safety and EnergyTsinghua UniversityBeijingChina
  3. 3.Honda R & D Co. Ltd. Automobile R & D CenterTochigiJapan

Personalised recommendations