Driver’s perception of risk directly affects driver’s driving behavior and driving safety, but the existing research on driver risk perception characteristics is still incomplete. In this study, a risk perception metric system was developed to quantify driver risk perception in dynamic curve driving conditions. The system includes four innovative indicators that consider the relative positions and motions between the ego vehicle, adjacent vehicle, and lane boundaries, which aim to calculate drivers’ subjective risk perception. Then, this study designed two subjective evaluation experiments based on the magnitude estimation method, which are the stable cornering condition designed for latent risk and the lane departure condition designed for overt risk respectively. After that, the quantitative model of the driver risk perception in curves based on Stevens’ power law was built to fit the subjective evaluation data. Finally, the rationality and applicability of the model were verified by collecting the driver’s natural driving data through the driver-in-the-loop simulation experiment. The quantitative model proposed in this study can be applied in the calculation and design of safety terms in decision making and trajectory planning.
FlemingJMAllisonCKYanX, et al. Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data. Saf Sci2019; 119: 76–83.
2.
ZhaoBChenHRanW, et al. Verification of adaptive demand of lane centering control system. Automobile Technol2021; 546(3): 1–6.
3.
ChenJChenHLanX, et al. Study on discrepancy between drivers’ favorable LKA intervention timing characteristics and their naturalistic driving characteristics. Automot Eng2021; 43(6): 842–850.
4.
BasuCYangQHungermanD, et al. Do you want your autonomous car to drive like you. In: ACM/IEEE international conference on human-robot interaction (HRI), Vienna, Austria, 6–9 March 2017, pp. 417–425. New York: ACM.
5.
YusofNMKarjantoJTerkenJ, et al. The exploration of autonomous vehicle driving styles: preferred longitudinal, lateral, and vertical accelerations. In: Automotive’UI 16: proceedings of the 8th international conference on automotive user interfaces and interactive vehicular applications, Ann Arbor, MI, USA, 24–26 October 2016, pp. 245–252. New York: ACM.
SivakM.The information that drivers use: is it indeed 90% visual?Perception1996; 25(9): 1081–1089.
8.
TangRJiangZ.Driver’s perception model in driving assist. In: 2020 IEEE 20th international conference on software quality, reliability and security companion (QRS-C), Macau, China, 11–14 December 2020, pp. 237–240. New York: IEEE.
9.
UllebergPRundmoT.Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers. Saf Sci2003; 41(5): 427–443.
10.
EboliLMazzullaGPungilloG.Measuring the driver’s perception error in the traffic accident risk evaluation. IET Intell Transp Syst2017; 11(10): 659–666.
11.
PingPShengYQinW, et al. Modeling driver risk perception on city roads using deep learning. IEEE Access2018; 6: 68850–68866.
12.
GaoHQuTHuY, et al. Personalized driver car-following model—considering human’s limited perception ability | risk assessment characteristics. In: 2022 6th CAA international conference on vehicular control and intelligence (CVCI), Nanjing, China, 28–30 October 2022, pp. 1–6. New York: IEEE.
13.
LeeDN.A theory of visual control of braking based on information about time-to-collision. Perception1976; 5(4): 437–459.
14.
GoodrichMBoerEInoueH. A characterization of dynamic human braking behavior with implications for ACC design. In: Proceedings of the 199 IEEE/IEEJ/JSAI international conference on intelligent transport system (Cat. No.99TH8383), Tokyo, Japan, 5–8 October 1999, pp. 964–969. New York: IEEE.
15.
KondohTYamamuraTKitazakiS, et al. Identification of visual cues and quantification of drivers’ perception of proximity risk to the lead vehicle in car-following situations. J Mech Syst Transp Logist2008; 1(2): 170–180.
16.
KimSYOhSY. A driver adaptive lane departure warning system based on image processing and a fuzzy evolutionary technique. In: IEEE IV2003 intelligent vehicles symposium. Proceedings (Cat. No.03TH8683), Columbus, OH, USA, 9–11 June 2003, pp. 361–365. New York: IEEE.
17.
MelmanTAbbinkDAVan PaassenMM, et al. What determines drivers’ speed? A replication of three behavioural adaptation experiments in a single driving simulator study. Ergonomics2018; 61(7): 966–987.
18.
GuoZChenHXiaT, et al. Study on quantification of driver’s subjective risk perception in curve driving condition. Automot Eng2022; 44(9): 1447–1455.
19.
WesthofenLNeurohrCKoopmannT, et al. Criticality metrics for automated driving: a review and suitability analysis of the state of the art. Arch Comput Methods Eng2023; 30(1): 1–35.
20.
KolekarSde WinterJAbbinkD.Human-like driving behaviour emerges from a risk-based driver model. Nat Commun2020; 11(1): 4850.
21.
MoleCDKountouriotisGBillingtonJ, et al. Optic flow speed modulates guidance level control: new insights into two-level steering. J Exp Psychol Hum Percept Perform2016; 42: 1818–1838.
22.
BarendswaardSvan BreugelLSchelfautB, et al. Effect of velocity and curve radius on driver steering behaviour before curve entry. In: 2019 IEEE international conference on systems, man and cybernetics (SMC), Bari, Italy, 6–9 October 2019, pp. 3866–3871. New York: IEEE.
23.
BalasVEBalasMM.Driver assisting by inverse time to collision. In: 2006 world automation congress, Budapest, Hungary, 24–26 July 2006, pp. 1–6. New York: IEEE.
24.
KondohTFuruyamaNHiroseT, et al. Direct evidence of the inverse of TTC hypothesis for driver’s perception in car-closing situations. Int J Automot Eng2014; 5(4): 121–128.
25.
BoerER.Satisficing curve negotiation: explaining drivers’ situated lateral position variability. IFAC Pap Online2016; 49(19): 183–188.
26.
StevensSS.The direct estimation of sensory magnitudes: loudness. Am J Psychol1956; 69(1): 1.
27.
BoltonML.Modeling human perception could Stevens’ power law be an emergent feature. In: 2008 IEEE international conference on systems, man and cybernetics (SMC), Singapore, 12–15 October 2008, pp. 1073–1078. New York: IEEE.
28.
StevensSS.On the psychophysical law. Psychol Rev1957; 64: 153–181.
29.
LeeSHJangSWKimES, et al. The quantitative model for optimal threshold and gamma of display using brightness function. IEICE Trans2006; 89-A: 1720–1723.
30.
KamadiaVYoonYSchillingMW, et al. Relationships between odorant concentration and aroma intensity. J Food Sci2006; 71(3): S193–S197.
31.
ChoubeyB. A wide dynamic range CMOS pixel with Steven’s power law response. In: Detectors and imaging devices: infrared, focal plane, single photon, San Diego, CA, USA, 1–5 August 2010, vol. 7780, pp. 259–267. SPIE.
32.
BaldwinCLEisertJLGarciaA, et al. Multimodal urgency coding: auditory, visual, and tactile parameters and their impact on perceived urgency. Work2012; 41Suppl 1: 3586–3591.
33.
FakhouryECulmerPHensonB. The impact of visual cues on haptic compliance discrimination using a pseudo-haptic robotic system. In: 2017 IEEE international conference on robotics and biomimetics (ROBIO), Macau, China, 5–8 December 2017, pp. 1719–1725. New York: IEEE.
34.
YingZ.General principles and technical standards for highway ramp design. High Speed Railway Technol1991; 1(2): 41–49.
35.
KahnemanDWakkerPSarinR.Back to Bentham? Explorations of experienced utility. Q J Econ1997; 112(2): 375–405.
36.
YuanYWangXCalvertS, et al. A risk-based driver behaviour model. IET Intell Transp Syst2024; 18(1): 88–100.
37.
NiDGuoFZhouY, et al. Determination of risk perception of drivers using fuzzy-clustering analysis for road safety. IEEE Access2020; 8: 125501–125512.
