Safety speeds estimation, as an indispensable link in the development of the aided or automatic drive, receives wide attention recently. Due to uncertain disturbances of vehicle driving, it is a challenging task to estimate the safety speed credibly. This work proposes a modified estimation approach to predict the turning safety speed by combining the static drive safety evaluation with the dynamic vehicle speed calculation. First, the driving safety state is evaluated considering the coupling of driver-vehicle-road-environment, where a comprehensive evaluation is obtained by combing the analytic hierarchy process and entropy weight analysis method. Then, a turning critical speed is calculated based on the vehicle driving dynamics considering both sideslip and rollover. The estimation of turning safety speed is achieved by modifying the critical speed with a safety correction factor obtained from the driving safety state evaluation. Finally, cases discussion on driving safety states evaluation, as well as the critical speed verification and safety speed analysis, are carried out. The results verify the validity of the driving safety state evaluation and critical speed calculation. The safety speeds have a reasonable safety margin according to the driving safety state evaluation. The maximum differences between the safety speeds and critical speeds are about 26.45% for buses and 26.39% for cars under low adhesion conditions, showing sufficient reliability for safety speed estimation.
LiuBLiaoYGuanX. The speed model of slippery pavements in curves based on driving simulation data. In: 20th COTA international conference of transportation professionals, Xi’an, China, 2020, pp.3717–3729.
2.
HosseinianSMNajafi Moghaddam GilaniVTahmasbi AmoliH, et al. Presentation of analytical methods for better decision making about the most important factor influencing rural accidents. Math Probl Eng2021; 2021(1): 1–16.
3.
WangQYangBTanG, et al. Safe travelling speed of commercial vehicles on curves based on vehicle-road collaboration. SAE technical paper 2017-01-0080, 2017.
4.
LiMWangHWangZ, et al. Analysis and warning of corner safety speed of curve based on vehicle structure modeling. In: 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC), Chongqing, China, 2020. IEEE.
5.
GlaserSNouveliereLLusettiB, et al. Speed limitation based on an advanced curve warning system. In: 2007 IEEE intelligent vehicles symposium, Istanbul, Turkey, 13 August 2007, pp.686–691.
6.
LiuYSunQCuiD.Vehicle state estimation based on PSO-RBF neural network. Int J Veh Saf2019; 11(1): 93–105.
7.
WuL. Experiment study on vehicle speed estimation using accelerometer and wheel speed measurements. In: 2011 second international conference on mechanic automation and control engineering, Inner Mongolia, China, 2011, vol. 8, pp.294–297.
8.
KumarRPathakR.Adaptive cruise control-towards a safer driving experience. Int J Sci Eng Res2012; 3(8): 1–5.
9.
SunCWuCChuD, et al. Risk prediction for curve speed warning by considering human, vehicle, and road factors. Transp Res Rec J Transp Res Board2016; 2581(1): 18–26.
10.
ŽuraulisVSokolovskijEMatijošiusJ, et al. The opportunities for establishing the critical speed of the vehicle on research in its lateral dynamics. Eksploatacja i Niezawodnosc/Maintenance and Reliability2013; 15(4): 312–318.
11.
Van WinsumWGodthelpH. Speed choice and steering behavior in curve driving. Human Factors1996; 38(3): 434–441.
12.
RogersSZhangW. Development and evaluation of a curve rollover warning system for trucks. In: IEEE IV2003 intelligent vehicles symposium. Proceedings, Columbus, OH, USA, 28 July 2003, pp.294–297.
13.
CharltonSG.Perceptual and attentional effects on drivers’ speed selection at curves. Accid Anal Prev2004; 36(5): 877–884.
14.
ZhangDWangJLiS, et al. Risk prediction based curve anti-sideslip speed warning system. J Highway Transport Res Dev2009; 26(S1): 44–48.
15.
ChuDDengZHeY, et al. Curve speed model for driver assistance based on driving style classification. IET Intell Transp Syst2017; 11(8): 501–510.
16.
ChuDYangJLuL, et al. Curve speed model considering coupled effect vehicle and road for preventions of rollover and sideslip. In: 2018 21st international conference on intelligent transportation systems (ITSC), Maui, HI, USA, 2018, pp.1358–1363.
17.
YangJChuDWangR, et al. Coupling effect modeling of driver vehicle environment factors influencing speed selections in curves. Proc IMechE, Part D: J Automobile Engineering2020; 234: 2066–2078.
18.
SagbergFBianchi PiccininiGFEngströmJ.A review of research on driving styles and road safety. Human factors2015; 57(7): 1248–1275.
19.
LacherezPWoodJMAnsteyKJ, et al. Sensorimotor and postural control factors associated with driving safety in a community-dwelling older driver population. J Gerontol A Biol Sci Med Sci2014; 69: 240–244.
20.
HassanHKingMWattK.The perspectives of older drivers on the impact of feedback on their driving behaviours: a qualitative study. Transp Res Part F Traffic Psychol Behav2015; 28: 25–39.
21.
JeihaniMBanerjeeS.Drivers’ behavior analysis under reduced visibility conditions using a driving simulator. J Traffic Logist Eng2018; 6(2): 48–52.
22.
DengZChuDWuC, et al. Curve safe speed model considering driving style based on driver behaviour questionnaire. Transp Res Part F Traffic Psychol Behav2019; 65: 536–547.
23.
OwensDAWoodJMOwensJM.Effects of age and illumination on night driving: a road test. Human Factors2007; 49(6): 1115–1131.
24.
ColombaroniCFuscoGIsaenkoN.Analysis of road safety speed from floating car data. Transp Res Procedia2020; 45: 898–905.
25.
PengJChuLFwaTF.Determination of safe vehicle speeds on wet horizontal pavement curves. Road Mater Pavement Des2021; 22: 2641–2653.
26.
ZhouHGaoJLiuH.Vehicle speed preview control with road curvature information for safety and comfort promotion. Proc IMechE, Part D: J Automobile Engineering2021; 235: 1527–1538.
27.
AhmadiAMachianiSG.Drivers’ performance examination using a personalized adaptive curve speed warning: driving simulator study. Int J Hum Comput Interact2019; 35: 996–1007.
28.
TonguçGAkçayİHGürbüzH.Improvement of the visual warning system for various driving and road conditions in road transportation. Proc IMechE, Part D: J Automobile Engineering2018; 232: 175–187.
29.
ChenQLiXZhouX, et al. Analysis and research on the man-machine dynamics behaviour coupling mechanism of the super miniature electric vehicle. Int J Veh Saf2017; 9(4): 352–369.
30.
ZhaoSQuXLiY, et al. Curve driving safety warning system for vehicle with driver-vehicle-environment synergy. J Beijing UnivTechnol2016; 42(1): 112–118.
31.
GalarzaMAParadellsJ.Categorisation of driving scenario complexity based on primary driving tasks and road characteristics. Int J Veh Saf2018; 10(2): 138–161.
32.
ChuATWKalabaRESpingarnK.A comparison of two methods for determining the weights of belonging to fuzzy sets. J Optim Theory Appl1979; 27(4): 531–538.
33.
HoWMaX.The state-of-the-art integrations and applications of the analytic hierarchy process. Eur J Oper Res2018; 267(2): 399–414.
34.
Scott-ParkerBKingMJWatsonB.The psychosocial purpose of driving and its relationship with the risky driving behaviour of young novice drivers. Transp Res Part F Traffic Psychol Behav2015; 33: 16–26.
35.
ZhaoYYaoHYanD, et al. Analysis of traffic safety evaluation model for expressway. J Chongqing Jiaotong Univ (Nat Sci)2012; 4(31): 846–851.
36.
WangXLiuYWangJ, et al. Study on influencing factors selection of driver’s propensity. Transp Res D Transp Environ2019; 66: 35–48.
37.
QZGuoXDengJ, et al. Grey relation entropy method of grey relation analysis. Syst Eng Theory Pract1996; 8: 7–11.
38.
YangJChuDWangW, et al. A modeling and factor analysis of safety speed on curve segments. J Transp Inf Saf2018; 36(6): 1–8.
39.
LarishCPiyabongkarnDTsourapasV, et al. A new predictive lateral load transfer ratio for rollover prevention systems. IEEE Trans Vehicular Technol2013; 62(7): 2928–2936.
