Driver behavior in car-following scenarios is highly scenario-dependent. To explore the effect of different vehicle, rear-end collision urgent situations on driver behaviors, this study designed the experimental scenarios for the urgency of vehicle rear-end collisions based on virtual simulation technology. Based on the kinematics parameters of the two vehicles and the driver’s behavior, the driving behavior under different rear-end collision conflict risks was analyzed, and a rear-end collision risk prediction model was constructed by using the binary Logistic regression method. Meanwhile, the influence of the two-factor interaction effect on the risk of rear-end collision was explored by using XGBoost and SHAP methods. The results show that with the increase of collision urgency, the brake reaction time (BRT) is shortened, and the maximum deceleration and deceleration rate increase significantly (p < 0.05). Under the same conflict urgency scenario, the higher speed of the lead vehicle will make a shorter BRT and following spacing, the maximum deceleration and the deceleration rate significantly increase (p < 0.05). With the increase of braking deceleration in the lead vehicle, the BRT decreases significantly (p < 0.05), but the following spacing and the maximum deceleration increase significantly (p< 0.05). With the decrease in time to collision (TTC) and the increase in vehicle speed, the proportion of drivers taking braking steering behavior increases, and they generally take braking measures first. The rear-end collision prediction model can accurately predict the collision risk, and the accuracy is 85.2%. The results of this study could provide a reference for the further improvement of road traffic safety management and in-vehicle assistance systems.
SinghS. Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Traffic Safety Facts Crash Stats. Report No. DOT HS 812–115; National Center for Statistics and Analysis: Washington, DC, USA, 2015.
2.
SpanoGCaffòAOLopezA, et al. Validating driver behavior and attitude measure for older Italian drivers and investigating their link to rare collision events. Front Psychol2019; 10: 368.
3.
ZhangGCaiYJiangX, et al. Causal mediation analysis of the impacts of distracted driving on crash injury risks. Int J Inj Contr Saf Promot2022; 29(4): 556–565.
4.
EustaceDWeiH. The role of driver age and gender in motor vehicle fatal crashes. J Transp Saf Secur2010; 2(1): 28–44.
5.
KalantarovSRiemerROron-GiladT. Pedestrians’ road crossing decisions and body parts’ movements. Transp Res Part F: Traffic Psychol Behav2018; 53: 155–171.
ChenLLiuMCaiY, et al. Intelligent driver model for curved roads in vehicle-infrastructure cooperation environment with consideration of preceding vehicles’ information. Adv Transp Stud2023; 61: 53–68.
8.
YangDZhuLLiuY, et al. A novel car-following control model combining machine learning and kinematics models for automated vehicles. IEEE Trans Intell Transp Syst2018; 20(6): 1991–2000.
9.
National Highway Traffic Safety Administration (NHTSA). Traffic safety facts 2017: a compilation of motor vehicle crash data. Report no: DOT HS 812806. 2019.
10.
National Highway Traffic Safety Administration (NHTSA). Traffic safety facts 2019: a compilation of motor vehicle crash data. Report no: DOT HS 813141. 2021.
11.
WuKFThorCP. Method for the use of naturalistic driving study data to analyze rear-end crash sequences. Transp Res Rec2015; 2518(1): 27–36.
12.
Van WinsumWHeinoA. Choice of time-headway in car-following and the role of time-to-collision information in braking. Ergonomics1996; 39(4): 579–592.
13.
StahlPDonmezBJamiesonGA. Anticipation in driving: the role of experience in the efficacy of pre-event conflict cues. IEEE Trans Hum Mach Syst2014; 44(5): 603–613.
14.
HennessyD. Social, personality, and affective constructs in driving. Handbook of traffic psychology. Elsevier, 2011. pp. 149–163.
15.
ÖztürkİÖzkanÖÖzB. Investigating sex, masculinity and femininity in relation to impulsive driving and driving anger expression. Transp Res Part F: Traffic Psychol Behav2021; 81: 14–26.
16.
MuellerASTrickLM. Driving in fog: the effects of driving experience and visibility on speed compensation and hazard avoidance. Accid Anal Prev2012; 48: 472–479.
17.
UnderwoodGChapmanPBowdenK, et al. Visual search while driving: skill and awareness during inspection of the scene. Transp Res Part F: Traffic Psychol Behav2002; 5(2): 87–97.
18.
de WitH. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol2009; 14(1): 22–31.
19.
El ChliaoutakisJDemakakosPTzamaloukaG, et al. Aggressive behavior while driving as predictor of self-reported car crashes. J Saf Res2002; 33(4): 431–443.
20.
LeeJDMcGeheeDVBrownTL, et al. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum Factors2002; 44(2): 314–334.
21.
AbeGRichardsonJ. Alarm timing, trust and driver expectation for forward collision warning systems. Appl Ergon2006; 37(5): 577–586.
22.
GreenM. Collison analysis 4: perception-response time. In: Roadway human factors: from science to application. Lawyers & Judges Publishing Company, Inc, 2017, pp. 409–443.
23.
KhodayariAGhaffariAKazemiR, et al. A modified car-following model based on a neural network model of the human driver effects. IEEE Trans Syst Man Cybern A Syst Hum2012; 42(6): 1440–1449.
24.
ShinarD. Driver information processing: attention, perception, reaction time, and comprehension. Traffic safety and human behavior. Emerald Publishing Limited, 2017. pp. 189–256.
25.
WuKFLinYJ. Exploring the effects of critical driving situations on driver perception time (PT) using SHRP2 naturalistic driving study data. Accid Anal Prev2019; 128: 94–102.
26.
ElhenawyMEl-ShawarbyIRakhaH. Modeling the perception reaction time and deceleration level for different surface conditions using machine learning techniques. In: Proceedings of the AHFE 2016 international conference on digital human modeling and simulation, Walt Disney World®, Florida, USA, 27–31 July 2016, pp. 131–142. Cham: Springer International Publishing.
27.
EngströmJ. Scenario criticality determines the effect of working memory load on brake response time. In: Proceedings of the 2nd European conference on human centred design for intelligent transport systems, 2010, Berlin, Germany.
28.
SchweitzerNApterYBen-DavidG, et al. A field study on braking responses during driving. II. Minimum driver braking times. Ergonomics1995; 38(9): 1903–1910.
29.
SummalaHLambleDLaaksoM. Driving experience and perception of the lead car’s braking when looking at in-car targets. Accid Anal Prev1998; 30(4): 401–407.
30.
AdavikottuAVelagaNR. Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections. Accid Anal Prev2023; 187: 107088.
31.
XuTHaoYCuiS, et al. Crash risk assessment of off-ramps, based on the gaussian mixture model using video trajectories. Sustainability2020; 12(8): 3076.
32.
MarkkulaG. Modeling driver control behavior in both routine and near-accident driving. Proc Hum Factors Ergon Soc Annu Meet2014; 58(1): 879–883.
33.
WuBZhuXShenJ, et al. Analysis of driver emergency steering lane changing behavior based on naturalistic driving data. J Tongji Univ (Nat Sci)2017; 45(04): 0554–0561.
LundbergSMLeeSI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst2017; 30: 4766–4777.
36.
FengD-CWangW-JMangalathuS, et al. Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls. J Struct Eng2021; 147(11): 04021173.
37.
JasKDodagoudarGR. Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP. Soil Dyn Earthq Eng2023; 165: 107662.
38.
MarkkulaGEngströmJLodinJ, et al. A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accid Anal Prev2016; 95: 209–226.
39.
AliYHaqueMMZhengZ. An extreme value theory approach to estimate crash risk during mandatory lane-changing in a connected environment. Anal Methods Accid Res2022; 33: 100193.
40.
WangJZhangXKangH, et al. Test method and risk factor definition of forward collision warning system. IEEE Access2020; 8: 47730–47740.
41.
TawfeekMHEl-BasyounyK. Network-level comparison of various forward collision warning algorithms. Simulation2019; 95(4): 313–325.
42.
FambroDBKoppaRJPichaDL, et al. Driver braking performance in stopping sight distance situations. Transp Res Rec2000; 1701(1): 9–16.
43.
EveryJLSalaaniMKBarickmanFS, et al. Braking behavior of truck drivers in crash imminent scenarios. SAE Int J Commer Veh2014; 07: 487–499.
44.
CappeCThutGRomeiV, et al. Selective integration of auditory-visual looming cues by humans. Neuropsychologia2009; 47(4): 1045–1052.
45.
GouldMPoulterDRHelmanS, et al. Detection of vehicle approach in the presence of additional motion and simulated observer motion at road junctions. J Exp Psychol Appl2013; 19(2): 171–184.
46.
SvärdMMarkkulaGBärgmanJ, et al. Computational modeling of driver pre-crash brake response, with and without off-road glances: parameterization using real-world crashes and near-crashes. Accid Anal Prev2021; 163: 106433.
47.
FuCSayedT. Comparison of threshold determination methods for the deceleration rate to avoid a crash (DRAC)-based crash estimation. Accid Anal Prev2021; 153: 106051.
48.
WangXZhuMChenM, et al. Drivers’ rear end collision avoidance behaviors under different levels of situational urgency. Transp Res Part C: Emerg Technol2016; 71: 419–433.
49.
AdarnsLDPlacePAdarnsL. Review of the literature on obstacle avoidance maneuvers: braking versus steering. The University of Michigan Transportation Research Institute (no. UMTRI-94-19), 1994.
50.
ChenXZhangWBaiH, et al. A sigmoid-based car-following model to improve acceleration stability in traffic oscillation and following failure in free flow. IEEE Trans Intell Transp Syst2024; 25(8): 9039–9057.
51.
ChenXZhangWBaiH, et al. The revolution of roundabouts in the autonomous driving era: a lane-free self-organized motion planning framework. Adv Eng Inform2026; 69: 104037.
52.
ChenXZhangWBaiH, et al. DGLF-MPF: a destination-guided motion planning framework for autonomous vehicles in lane-free traffic. Transp Res Part C: Emerg Technol2026; 182: 105440.
