Sage Journals: Discover world-class research

Abstract

Recent research suggests that advanced driver assistance systems (ADAS) technologies, such as forward collision warning (FCW), have the potential to reduce rear-end and other forward collisions. The primary objective of this literature review is to provide a summary of relevant research on how the presence of FCWs may or may not impact driver perception response time (PRT) to forward hazards. Our review of the literature identified several primary variables that impact driver PRT including warning modality, inattention/distraction, and expectancy. These variables, their implications, and future research are discussed.

Keywords

forward collision warning FCW perception response time PRT review collision mitigation systems driver reaction time brake reaction time

Rear-end collisions accounted for approximately 29% of all collisions in 2021, 7.5% of which were fatal (National Highway Traffic Safety Administration [NHTSA], 2023). Recent research suggests that ADAS technologies, such as FCW, have the potential to reduce these numbers. For example, one analysis posits that FCW technology has the potential to prevent or mitigate approximately 1.2 million crashes, or approximately 20% of all crashes, per year (Jermakian, 2011).

As a Level 0 ADAS, FCW provides momentary assistance to the driver in the form of auditory, visual, and/or haptic warnings in order to provide information to the driver about a potential forward hazard so that the driver may respond as necessary (e.g., braking and/or steering). Level 0 ADAS describe that the driver is expected to be alert, be attentive, and to maintain control of the vehicle at all times (NHTSA, 2022; SAE International On-Road Automated Driving committee, 2021). Given that drivers maintain control of the vehicle and are responsible for responses to hazardous situations, it is important to understand whether, and how, the presence of a FCW system impacts a driver’s ability to safely operate their vehicle and respond to potential hazards.

The primary objective of this literature review is to provide a summary of relevant research on how the presence of FCWs may or may not impact driver PRT to forward hazards, as well as to look at drivers’ PRT to the FCWs themselves, including when there may not be driver responses prior to a collision. This review investigates (1) how the presence and type of FCW systems impact driver PRT, and (2) how driver factors (e.g., inattention or distraction) impact driver PRT and response when a FCW system is present.

Methods

We conducted a systematic literature search with the aim of identifying as many studies as possible that examined and reported PRTs to either FCWs or to hazards with a FCW system present. We searched multiple databases (specifically, Google Scholar, TRID, HFES, ProQuest, PubMed, SAE International, PsycInfo, and Web of Science), scanned the reference lists of papers, and used the cited by feature in Google Scholar. Typical keywords searched included “FCW,” “Forward Collision Warning,” “Rear-end Collision Warning,” “Front-to-rear Collision Warning,” “Collision Warning,” “Collision Mitigation System,” “PRT,” “Perception Response Time,” “Perception Reaction Time,” “Reaction Time,” “RT,” “Time to React,” and “Hazard Perception.” Studies had to fulfill several criteria to be selected for consideration in the review, including examining forward collision warnings, examining PRT or reaction time, and not being about design, algorithms, or non-transportation contexts.

Results

The search yielded 766 relevant abstracts, which were each reviewed by two scientists. From these, 206 potentially relevant full-text records were identified, and further reviewed for eligibility. After excluding 146 records that did not meet the criteria, 60 records remained for inclusion.

Our review of these 60 articles identified several primary variables that impact driver PRT. These variables include (1) warning modality (i.e., visual, auditory, tactile, or some combination of these), (2) inattention or non-driving related task engagement (NDRT), and (3) expectancy (e.g., expectancy of the warning, expectancy of the hazard).

Warning Modality

All reviewed articles provided warnings communicated through auditory, visual, tactile, and/or mixed warning signals. A total of 49 papers provided warnings with an auditory component (e.g., beeps, car horn sounds, or automated voices), 31 papers provided warnings with a visual component (e.g., lights, icons), 18 papers provided warnings with a tactile component (e.g., vibration of the pedal or seatbelt), and 30 papers provided warnings that consisted of a combination of modalities (e.g., visual icon with an auditory beep). Four of the papers compared PRTs in response to warning signals that contained some form of all three modalities (see Table 1).

Table 1.

Summary of PRTs in Response to Warnings Comparing Across Warning Modality.

Study	Modality	NDRT	Mean PRT (s)
Forkenbrock et al. (2011) ^**	AVT	VC	1.26
	A	VC	1.60
	AV	VC	1.44
	T	VC	1.42
	AT	VC	1.09
	VT	VC	1.13
	V	VC	2.00
	None	VC	1.80
Kiefer et al. (1999) ^**	AV	C	0.50
	AV	C	0.52
	AV	C	0.57
	VT	C	0.63
	AV	C	0.68
	AV	C	0.71
	AV	C	1.03
	VT	C	1.20
	AV	C	0.62
	AV	C	0.67
	AV	C	0.68
	VT	C	0.77
	AV	C	0.75
	AV	C	0.68
	AV	C	0.93
	AVT	C	0.85
	AV	C	0.83
Lubbe (2017)	AV	VC	2.80^{^}
	T	VC	0.80^{^}
	AV	VC	4.10^{^}
	AV	VC	2.30^{^}
	AV	VC	1.00
	T	VC	0.80
	AV	VC	1.00
	AV	VC	0.90
Scott and Gray (2008)	None	None	1.19^*
	None	None	0.66^*
	V	None	0.95^*
	V	None	0.61^*
	A	None	0.85^*
	A	None	0.56^*
	T	None	0.72^*
	T	None	0.53^*

Note. A = Auditory; V = Visual; T = Tactile; M = Manual; C = Cognitive.

PRT value is estimated from graph.

Test Track Study, all others use simulators.

PRTs include participants who did not successfully brake prior to collision.

Inattention and Non-Driving Related Task

Across all of the articles, a total of 30 papers used NDRTs or other forms of inattentive driving (e.g., drowsiness) in order to distract drivers from the driving task. Of these papers, 10 defined the onset of PRT as the presence of the hazard, and 20 defined the onset of PRT as the onset of the FCW (one of which also had a condition which defined the onset of PRT as a specific time to collision). Only eight total papers compared PRTs across a NDRT condition and a non-distracted control (see Table 2). Only three papers compared different levels of NDRTs. Researchers have used a variety of different visual (e.g., navigation instructions), manual, cognitive (e.g., hands-free conversation), or some combination of tasks (e.g., texting) as distractions from the driving task (e.g., Hamilton & Grabowski, 2013).

Table 2.

Summary of PRTs Comparing Across a NDRT Condition and a Non-Distracted Control.

Study	Warning	NDRT	PRT Onset	PRT End	NDRT Mean PRT (s)	Non-NDRT Mean PRT (s)
Bueno et al. (2014)	None	C	H	A	0.52	0.47
Bueno et al. (2014)	A	C	H	A	0.51	0.44
Crump et al. (2015) ^**	AV	C	H	B	0.37	0.30
Lee et al. (2000)	AV	VM	H	A	1.35	1.03
Lee et al. (2000)	None	VM	H	A	2.21	1.73
Lylykangas et al. (2016)	V	V	W	B	0.75^*	0.71^*
	T	V	W	B	0.63^*	0.63^*
	VT	V	W	B	0.62^*	0.62^*
Biondi et al. (2017)	None	C	H	B	1.90^*	1.66^*
	A	C	W	B	0.89^*	0.72^*
	T	C	W	B	0.97^*	0.80^*
	AT	C	W	B	0.66^*	0.59^*
Lee et al. (2002)	AV	VM	W	A	1.04	0.76
Lee et al. (2002)	None	VM	H	A	1.59	1.16
Mohebbi et al. (2009)	None	C	H	B	0.91^*	0.77^*
	None	C	H	B	0.96^*
	T	C	W	B	0.73^*	0.67^*
	T	C	W	B	0.82^*
	A	C	W	B	0.84^*	0.69^*
	A	C	W	B	0.92^*
Reinmueller et al. (2020)	V	C	W	B	0.76^*	0.43^*
Reinmueller et al. (2020)	V	C	W	B	0.68^*	0.45^*

Note. A = Auditory; V = Visual; T = Tactile; M = Manual; C = Cognitive; H = Hazard; W = Warning; A = Accelerator release; B = Brake Press.

PRT value is estimated from graph.

Test Track Study, all others use Simulators.

Expectancy

The present review identified studies that manipulated driver expectancies about FCWs, hazards, or both. There were 37 papers that provided participants with instructions that the procedure would involve FCWs. Drivers expected hazards in 16 of the papers. There were 12 papers where the brake lights on the lead vehicle were disabled during deceleration, making the need to brake less expected. Importantly, five papers included within-study manipulations of expectations (see Table 3). Moreover, hazards were generally considered expected when they occurred at intersections or pedestrian walkways, and hazards were considered unexpected when they occurred on straight roads unless otherwise indicated by the authors.

Table 3.

Summary of PRTs to Warnings Comparing Expected and Unexpected Conditions.

Study	Location	Expectancy manipulation	Expected mean PRT (s)	Unexpected mean PRT (s)
Bakowski et al. (2015)	SR	W	2.75^*	3.75^*
	SR	W	2.59^*	3.8^*
	SR	W	1.85^*	3.55^*
	SR	W	1.86^*	3.13^*
Ho et al. (2007) ⁺⁺	SR	H	0.98^*	1.02^*
	SR	H	0.85^*	0.93^*
	SR	H	0.77^*	0.84^*
Kiefer et al. (1999)	SR	H	0.62	0.68
	SR	H	0.67	0.71
	SR	H	0.68	1.02
	SR	H	0.77	1.20
Lubbe (2017) ⁺	Int	W	2.30^{* ^}	4.10^{* ^}
Muhrer et al. (2012)	Int	H	0.96^*	0.92^*
	SR	H	1.30^*	1.45^*
	SR	H	1.36^*	1.27^*

Note. Expectancy Manipulation indicates whether the FCW was unexpected, or the hazard was unexpected. H = Hazard; W = Warning; SR = Straight Road; Int = Intersection.

PRT value is estimated from graph.

Pedestrian hazard, all others are lead vehicle.

Hazard expectancy manipulated by disabling brake lights in unexpected condition.

PRTs include participants who did not successfully brake prior to collision

Discussion

Warning Modality

Review of the data from all 60 articles indicated three key findings. Firstly, warnings across all modalities (auditory visual, tactile, or a combination thereof) generally yielded shorter PRTs than non-warning control groups. Second, FCWs presented in multiple concurrent modalities (e.g., an auditory tone in conjunction with a visual HUD message) generally yielded shorter PRTs than FCWs presented in a single modality. Third, the reviewed studies showed that PRTs to FCWs with tactile components are generally shorter than FCWs without tactile components. This is consistent with research on human response times to stimuli across perceptual modalities, which has found that tactile stimuli tend to elicit shorter response times than visual and auditory stimuli (e.g., Ng & Chan, 2012). For example, Forkenbrock et al. (2011) used three types of FCW signals: an auditory beep, a visual HUD alert, and a seatbelt vibration. PRTs were shortest when alerts combined all modalities (beep + HUD + belt), combined auditory and tactile modalities (beep + belt), or combined visual and tactile modalities (HUD + belt). When isolated, PRTs were shortest for tactile-only signals compared to auditory-only or visual-only signals. Furthermore, 15 of the 17 trials in which drivers were able to avoid collision included a tactile signal.

Inattention and Non-Driving Related Tasks

Consistent with PRT research in non-FCW contexts (e.g., Krauss, 2015), research examining driver NDRT engagement suggests that participating in an NDRT increases driver PRTs when compared to attentive driving, regardless of whether a FCW is present (Biondi et al., 2017; Bueno et al., 2014; Crump et al., 2015; Lee et al., 2000, 2002; Mohebbi et al., 2009). In addition to differences in PRT depending on NDRT, many of these studies also demonstrate an overall effect of warning. Specifically, the presence of the warning results in faster PRT than the absence of the warning (Biondi et al., 2017; Lee et al., 2000, 2002; Mohebbi et al., 2009). Lee et al. (2002) concludes that this result suggests that the warning provides an overall safety benefit to drivers regardless of whether the driver is attentive or distracted. Interestingly however, many of the studies that demonstrate this overall effect of the warning measure PRT using the onset of the warning for their warning conditions, while PRT is the onset of the hazard in their no-warning condition (see Lee et al., 2000 for exception). This method does not consider any drivers that may have already begun their PRT to the hazard prior to the onset of the warning. Of the studies that measure PRT using the onset of the hazard, Bueno et al. (2014) and Lee et al. (2000) found that the warning led to faster response but did not report whether there were drivers that responded prior to the onset of the warning. In contrast, Crump et al. (2015) found that the majority of attentive drivers in their study responded prior to the warning, whereas only two drivers responded prior to the warning in their NDRT condition. Overall, they concluded that FCW is not a replacement for an alert and attentive driver.

Although eight of the 30 papers that used a NDRT compared it to non-distracted control, only three papers examined how different levels of NDRTs or inattention can impact PRT (Gaspar et al., 2019; Kiefer et al., 2005; Mohebbi et al., 2009). For example, Gaspar et al. (2019) examined the effectiveness of auditory and tactile FCWs (as compared to no warning) to a sudden reveal of a stopped vehicle for drowsy drivers. They found no effect of warning—but demonstrated that severe drowsiness results in slower PRTs than moderate drowsiness. The authors suggest that, unlike distracted drivers, drowsy drivers may already be looking toward the forward roadway at the time of the warning—and therefore would not necessarily benefit from the orienting effect of FCWs. Mohebbi et al. (2009) examined the effectiveness of auditory and tactile FCWs to a lead vehicle braking event (disabled brake lights) for drivers engaged in conversation of various complexity. They found that a simple conversation increased PRT in the no warning and auditory warning conditions, but not in the tactile warning condition, whereas the complex conversation increased PRT in all warning conditions. The authors suggest that conversation increases auditory load, so tactile warnings may be more helpful in orienting drivers who are distracted by cell phone conversation to the forward roadway.

Expectancy

Prior studies have demonstrated that expectancy can influence task speed and accuracy when driving (e.g., Krauss, 2015). When driver expectations are met, performance is enhanced relative to when they are not. Accordingly, the present review found that PRTs were shorter when drivers expected FCWs (e.g., Bakowski et al., 2015; Lubbe, 2017) and when drivers expected hazards (Ho et al., 2007; Kiefer et al., 1999; Muhrer et al., 2012). When driver expectations are violated (e.g., a hazard emerges), additional demands are placed on attention and information processing (e.g., Krauss, 2015).

Studies varied in their manipulations of expectations pertaining to both warnings and hazards. For example, Wang et al. (2023) “concisely introduced” participants to the study’s collision mitigation system and warnings, only commencing when drivers demonstrated comprehension. Abe and Richardson (2006), meanwhile, provided drivers with 10-minute practice drives to become familiar with the study’s lead vehicle decelerations. In other cases, drivers were informed about the potential for hazards before encountering lead-vehicle decelerations (e.g., Ho et al., 2006; Winkler et al., 2018a), pedestrians (Song et al., 2022; Winkler et al., 2018a) and cyclists (Puente Guillen & Gohl, 2019; Winkler et al., 2018a).

Conclusions

When considering the modalities of warnings, studies indicate that haptic signals can be efficacious as FCWs. However, these signals tend to be perceived as disruptive, which may limit market penetration (e.g., Campbell et al., 2014). Our findings show shorter PRTs associated with tactile warnings; hence, exploring the effectiveness of various tactile signals (e.g., accelerator vibration) in reducing forward collisions is warranted.

Our review suggests that NDRTs increased PRT regardless of the presence of an FCW. Crump et al. (2015) suggests that attentive drivers generally do not need assistance from ADAS to avoid preventable collisions, but this was the only study to quantify the number of drivers who responded prior to the FCW. Our results suggest that FCWs may help drivers with momentary distractions perceive and respond to the potential hazard faster than if no warning were present. Further research is needed to better understand the potential benefits of FCW for distracted drivers in real world settings, where PRTs are measured from the onset of the hazard rather than the onset of the warning.

Regarding driver expectancy, our findings are consistent with this research demonstrating that individuals tend to react more quickly to expected hazards than unexpected hazards (e.g., Krauss, 2015). Further, our review suggests that drivers may respond more quickly when they are aware of the presence of the FCW, providing evidence that educating drivers about the existence and function of FCWs may help reduce PRTs in crash or near-crash scenarios.

Overall, our results suggest that FCWs may reduce PRTs under certain circumstances, which in turn has the potential to provide a safety benefit to the driver. This is consistent with data that suggest that FCW systems have the potential to mitigate or prevent crashes (e.g., PARTS, 2022). These data show that FCW alone has the potential to reduce all front-to-rear crashes by 16% (PARTS, 2022), but many others are unavoidable. FCWs do not prevent all crashes and, in many cases where an individual crashed, PRTs would have exceeded time to collision from the warning or hazard (e.g., Crump et al., 2015; Forkenbrock et al., 2011; Kiefer et al., 2005; Lee et al., 2000).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

*Studies included in the review

*Abe

Richardson

(2004). The human factors of collision warning systems: System performance, alarm timing, and driver trust. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 48, pp. 2232–2236). Sage Publications.

Abe

Richardson

(2006). Alarm timing, trust and driver expectation for forward collision warning systems. Applied Ergonomics, 37(5), 577–586.

*Ahtamad

Gray

Reed

Spence

(2015, June). Informative collision warnings: Effect of modality and driver age [Conference session]. Driving Assessment Conference (Vol. 8, No. 2015). University of Iowa.

*Ahtamad

Spence

Gray

(2016). Warning drivers about impending collisions using vibrotactile flow. IEEE Transactions on Haptics, 9(1), 134–141.

Bakowski

D. L.

Davis

S. T.

Moroney

W. F.

(2015). Reaction time and glance behavior of visually distracted drivers to an imminent forward collision as a function of training, auditory warning, and gender. Procedia Manufacturing, 3, 3238–3245.

*Bao

LeBlanc

D. J.

Sayer

J. R.

Flannagan

(2012). Heavy-truck drivers’ following behavior with intervention of an integrated, in-vehicle crash warning system: A field evaluation. Human Factors, 54(5), 687–697.

*Becker

(2016). Effects of looming auditory FCW on brake reaction time under conditions of distraction. Arizona State University.

Biondi

Strayer

D. L.

Rossi

Gastaldi

Mulatti

(2017). Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety. Applied Ergonomics, 58, 238–244.

10.

Bueno

Fabrigoule

Ndiaye

Fort

(2014). Behavioural adaptation and effectiveness of a forward collision warning system depending on a secondary cognitive task. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 158–168.

11.

Campbell

J. L.

Doerzaph

Z. R.

Richard

C. M.

Bacon

L. P.

(2014). Human factors design principles for the driver-vehicle interface (DVI) [Conference session]. In Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 1-6). Association for Computing Machinery.

12.

*Chang

S. H.

Lin

C. Y.

Fung

C. P.

Hwang

J. R.

Doong

J. L.

(2008). Driving performance assessment: Effects of traffic accident location and alarm content. Accident Analysis and Prevention, 40(5), 1637–1643

13.

*Chen

W. H.

Lin

T. W.

Kao

K. C.

Hwang

S. L.

(2008). Safety assessment of different in-vehicle interface designs for bus collision warning systems. Transportation Research Record, 2072(1), 57–63.

14.

*Chun

Han

S. H.

Park

Seo

Lee

Choi

(2012). Evaluation of vibrotactile feedback for forward collision warning on the steering wheel and seatbelt. International Journal of Industrial Ergonomics, 42(5), 443–448.

15.

*Chun

Han

S. H.

Park

Seo

Choi

Han

Park

(2010). Evaluating the effectiveness of haptic feedback on a steering wheel for FCW [Conference session]. Proceedings of the 9th Pan-Pacific Conference on Ergonomics. CRC Press.

16.

Crump

Cades

Rauschenberger

Hildebrand

Schwark

Barakat

Young

(2015). Driver Reactions in a Vehicle with Collision Warning and Mitigation Technology (No. 2015-01-1411). SAE Technical Paper.

17.

*Cummings

M. L.

Kilgore

R. M.

Wang

Tijerina

Kochhar

D. S.

(2007). Effects of single versus multiple warnings on driver performance. Human Factors, 49(6), 1097–1106.

18.

*Fitch

G. M.

Blanco

Morgan

J. F.

Wharton

A. E.

(2010). Driver braking performance to surprise and expected events. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 54, pp. 2075–2080). Sage Publications.

19.

Forkenbrock

Heitz

Hoover

R. L.

O’Harra

Vasko

Smith

(2011). A test track protocol for assessing forward collision warning driver-vehicle interface effectiveness (No. HS-811 501). National Highway Traffic Safety Administration.

20.

Gaspar

J. G.

Schwarz

C. W.

Brown

T. L.

Kang

(2019). Gaze position modulates the effectiveness of forward collision warnings for drowsy drivers. Accident Analysis and Prevention, 126, 25–30.

21.

*Graham

(1999). Use of auditory icons as emergency warnings: Evaluation within a vehicle collision avoidance application. Ergonomics, 42(9), 1233–1248.

22.

*Gray

(2011). Looming auditory collision warnings for driving. Human Factors, 53(1), 63–74.

23.

*Gray

Spence

(2014). A comparison of different informative vibrotactile forward collision warnings: Does the warning need to be linked to the collision event? Plos One, 9(1), e87070.

24.

Hamilton

Grabowski

(2013). Cognitive distraction: Something to think about-lessons learned from recent studies. AAA Foundation for Traffic Safety.

25.

*Harder

K. A.

Bloomfield

Chihak

B. J.

(2003). The Effectiveness of Auditory Side-and Forward-Collision Avoidance Warnings on Snow Covered Roads in Conditions of Poor Visibility. Minnesota Department of Transportation.

26.

Reed

Spence

(2006). Assessing the effectiveness of “intuitive” vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator. Accident Analysis and Prevention, 38(5), 988–996.

27.

Reed

Spence

(2007). Multisensory in-car warning signals for collision avoidance. Human Factors, 49(6), 1107–1114.

28.

Jermakian

J. S.

(2011). Crash avoidance potential of four passenger vehicle technologies. Accident Analysis and Prevention, 43(3), 732–740.

29.

*Kazazi

Winkler

Vollrath

(2015). Accident prevention through visual warnings: how to design warnings in head-up display for older and younger drivers [Conference session]. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (pp. 1028−1034). IEEE

30.

Kiefer

R. J.

LeBlanc

Palmer

M. D.

Salinger

Deering

R. K.

Shulman

(1999). Development and validation of functional definitions and evaluation procedures for collision warning/avoidance systems (No. DOT-HS-808-964). United States. Department of Transportation. National Highway Traffic Safety Administration.

31.

Kiefer

R. J.

LeBlanc

D. J.

Flannagan

C. A.

(2005). Developing an inverse time-to-collision crash alert timing approach based on drivers’ last-second braking and steering judgments. Accident Analysis and Prevention, 37(2), 295–303.

32.

*Kim

H. K.

Lee

S. B.

Lee

H. R.

Hong

S. J.

Ryung

M. H.

; Dept. of Future Tech. and Convergence Research, Korea Institute of Civil Eng, and Building Tech. (2021). Estimation of traffic safety improvement effect of forward collision warning (FCW). Journal of the Korea Institute of Intelligent Transport Systems, 20(2), 43–57.

33.

*Koustanaï

Cavallo

Delhomme

Mas

(2012). Simulator training with a forward collision warning system: Effects on driver-system interactions and driver trust. Human Factors, 54(5), 709–721.

34.

Krauss

D. A.

(2015). Forensic aspects of driver perception and response. Lawyers & Judges Publishing Company.

35.

*Lee

J. D.

Hoffman

J. D.

Hayes

(2004). Collision warning design to mitigate driver distraction. In Proceedings of the SIGCHI Conference on Human factors in Computing Systems (pp. 65-72). Association for Computing Machinery.

36.

Lee

J. D.

McGehee

D. V.

Brown

T. L.

Reyes

M. L.

(2002). Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Human Factors, 44(2), 314–334.

37.

Lee

J. D.

Ries

M. L.

McGehee

D. V.

Brown

T. L.

Perel

(2000). Can collision warning systems mitigate distraction due to in-vehicle devices? NHTSA Driver Distraction Internet Forum.

38.

*Lerner

Singer

Huey

Brown

Marshall

Chrysler

Chiang

D. P.

(2015). Driver-vehicle interfaces for advanced crash warning systems: Research on evaluation methods and warning signals (No. DOT HS 812 208). National Highway Traffic Safety Administration.

39.

Lubbe

(2017). Brake reactions of distracted drivers to pedestrian forward Collision Warning systems. Journal of Safety Research, 61, 23–32.

40.

Lylykangas

Surakka

Salminen

Farooq

Raisamo

(2016). Responses to visual, tactile and visual–tactile forward collision warnings while gaze on and off the road. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 68–77.

41.

*Ma

Huang

(2022). Evaluation of multimodal and multi-staged alerting strategies for forward collision warning systems. Sensors, 22(3), 1189.

42.

*Marshall

Boyle

L. N.

Brown

T. L.

(2018). Auditory alert characteristics impact on crash avoidance warning response (No. DOT HS 812 511). United States. Department of Transportation. National Highway Traffic Safety Administration.

43.

*McGehee

D. V.

Brown

T. L.

Lee

J. D.

Wilson

T. B.

(2002). Effect of warning timing on collision avoidance behavior in a stationary lead vehicle scenario. Transportation Research Record, 1803(1), 1–6.

44.

Mohebbi

Gray

Tan

H. Z.

(2009). Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone. Human Factors, 51(1), 102–110.

45.

Muhrer

Reinprecht

Vollrath

(2012). Driving with a partially autonomous forward collision warning system: how do drivers react? Human Factors, 54(5), 698–708.

46.

National Highway Traffic Safety Administration (NHTSA. (2022). Levels of Automation.https://www.nhtsa.gov/sites/nhtsa.gov/files/2022-03/Levels_of_Automation_Static_022822-v4-tag.pdf

47.

National Highway Traffic Safety Administration. (NHTSA. (2023). Traffic safety facts 2021: A compilation of motor vehicle traffic crash data (Report No. DOT HS 813 527). National Highway Traffic Safety Administration.

48.

A. W.

Chan

A. H.

(2012, March). Finger response times to visual, auditory and tactile modality stimuli [Conference session]. Proceedings of the international multiconference of engineers and computer scientists (Vol. 2, pp. 1449-1454). IMECS.

49.

PARTS. (2022). Real-world effectiveness of model year 2015-2020 advanced driver assistance systems. Partnership for Analytics Research in Traffic Safety.

50.

Puente Guillen

Gohl

. (2019). Forward collision warning based on a driver model to increase drivers’ acceptance. Traffic Injury Prevention, 20(sup1), S21–S26.

51.

Reinmueller

Kiesel

Steinhauser

(2020). Adverse behavioral adaptation to adaptive forward collision warning systems: An investigation of primary and secondary task performance. Accident Analysis and Prevention, 146, 105718.

52.

*Reinmueller

Steinhauser

(2019). Adaptive forward collision warnings: The impact of imperfect technology on behavioral adaptation, warning effectiveness and acceptance. Accident Analysis and Prevention, 128, 217–229.

53.

SAE International On-Road Automated Driving committee. (2021). Taxonomy and definitions for terms related to driving automation systems on on-road motor vehicles (SAE J3016) SAE International. https://www.sae.org/

54.

*Sayer

LeBlanc

Bogard

Funkhouser

Bao

Buonarosa

M. L.

Blankespoor

(2011). Integrated vehicle-based safety systems field operational test: Final program report (No. Fhwa-jpo-11-150; UMTRI-2010-36). United States. Joint Program Office for Intelligent Transportation Systems.

55.

*Schwarz

Fastenmeier

(2017). Augmented reality warnings in vehicles: Effects of modality and specificity on effectiveness. Accident Analysis and Prevention, 101, 55–66.

56.

Scott

J. J.

Gray

(2008). A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Human Factors, 50(2), 264–275.

57.

Song

Wang

Gan

Yang

Liu

(2022). Danger or avoidance indication: Dynamics interact with semantics in auditory icons to avoid collisions. International Journal of Industrial Ergonomics, 92, 103353.

58.

*Straughn

S. M.

Gray

Tan

H. Z.

(2009). To go or not to go: Stimulus-response compatibility for tactile and auditory pedestrian collision warnings. IEEE Transactions on Haptics, 2(2), 111–117.

59.

*Tidwell

Blanco

Trimble

Atwood

Morgan

J. F.

(2015). Evaluation of Heavy-Vehicle Crash Warning Interfaces (No. DOT HS 812 191). National Highway Traffic Safety Administration.

60.

*University of Iowa. (2006). Rear end crash avoidance system (RECAS) algorithms and alerting strategies : effects of adaptive cruise control and alert modality on driver performance. Final report. Human Factors & Vehicle Safety Research.

61.

Wang

Song

Wang

Yang

(2023). Head-up display graphic warning to support collision avoidance: Effect of graphic animation and border on driving behavior and eye movement pattern. Transportation Research Record, 2677(5), 636–652.

62.

*Wang

Wang

Gan

Liu

Yang

(2022). Effect of mapping characteristic on audiovisual warning: Evidence from a simulated driving study. Applied Ergonomics, 99, 103638.

63.

Winkler

Kazazi

Vollrath

(2018a). How to warn drivers in various safety-critical situations – Different strategies, different reactions. Accident Analysis and Prevention, 117, 410–426.

64.

*Winkler

Kazazi

Vollrath

(2018b). Practice makes better – Learning effects of driving with a multi-stage collision warning. Accident Analysis and Prevention, 117, 398–409.

65.

*Winkler

Kazazi

Vollrath

(2015). Distractive or supportive–how warnings in the head-up display affect drivers’ gaze and driving behavior [Conference Session]. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (pp. 1035−1040). IEEE.

66.

*Wu

Boyle

L. N.

Marshall

O’Brien

(2018). The effectiveness of auditory forward collision warning alerts. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 164–178.

67.

*Yan

Zhang

(2015). The influence of in-vehicle speech warning timing on drivers’ collision avoidance performance at signalized intersections. Transportation Research Part C: Emerging Technologies, 51, 231–242.

68.

*Zhao

Gong

Zhao

Liu

Sze

N. N.

Huang

(2023). Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments. Accident Analysis and Prevention, 186, 107053.

69.

*Zhu

Wang

(2020). Impact on car following behavior of a forward collision warning system with headway monitoring. Transportation Research Part C: Emerging Technologies, 111, 226–244.

Looking Back on Forward Collision Warnings: A Review of Perception Response Times from Empirical Studies

Abstract

Keywords

Methods

Results

Warning Modality

Inattention and Non-Driving Related Task

Expectancy

Discussion

Warning Modality

Inattention and Non-Driving Related Tasks

Expectancy

Conclusions

Footnotes

Declaration of Conflicting Interests

Funding

References