This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).
Background
Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.
Method
Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.
Results
The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.
Conclusion
Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.
Application
Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.
AkaikeH. (1973). Information theory as an extension of the maximum likelihood principle. In PetrovB. N.CsakiF. (Eds.), 2nd International Symposium on Information Theory (pp. 267–281). Budapest, Hungary: Akadémia Kiadó.
2.
BärgmanJ.BodaC. N.DozzaM. (2017). Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems. Accident Analysis & Prevention, 102, 165–180.
3.
BarrettG. V. (1968). Feasibility of studying driver reaction to sudden pedestrian emergencies in an automobile simulator. Human Factors, 10, 19–26.
4.
BlommerM.CurryR.SwaminathanR.TijerinaL.TalamontiW.KochharD. (2017). Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes. Transportation Research Part F, 45, 93–101.
5.
ClarkA. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–204.
6.
ClarkA. (2016). Surfing uncertainty. Oxford, UK: Oxford University Press.
7.
DesmondP. A.HancockP. A. (2001). Active and passive fatigue states. In HancockP. A.DesmondP. A. (Eds.), Stress, workload, and fatigue (Human factors in transportation) (pp. 455–465). Mahwah, NJ: Lawrence Erlbaum.
8.
DikmenM.BurnsC. M. (2016, October 24–26). Autonomous driving in the real world: Experiences with Tesla Autopilot and Summon. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ann Arbor, MI.
9.
EngströmJ. (2010). Scenario criticality determines the effects of working memory load on brake response time. In KremsJ.PetzoldtT.HenningM. (Eds.), Proceedings of the European Conference on Human Centred Design for Intelligent Transport Systems (pp. 25–36). Lyon, France: Humanist.
10.
EngströmJ.BärgmanJ.NilssonD.SeppeltB.MarkkulaG.Bianchi PiccininiG. F.VictorT. (2018). Great expectations: A predictive processing account of automobile driving. Theoretical Issues in Ergonomics Science, 19, 156–194.
11.
EngströmJ.Ljung AustM.ViströmM. (2010). Effects of working memory load and repeated scenario exposure on emergency braking performance. Human Factors, 52, 551–559.
12.
EngströmJ.MarkkulaG.MeratN. (2017, March20–22). Modeling the effect of cognitive load on driver reactions to a braking lead vehicle: A computational account of the cognitive control hypothesis. Paper presented at the 5th International Conference of Driver Distraction and Inattention Paris, France.
13.
FambroD.KoppaR.PichaD.FitzpatrickK. (1998). Driver perception-brake response in stopping sight distance situations. Transportation Research Record: Journal of the Transportation Research Board, 1628, 1–7.
14.
FancherP.ErvinR.SayerJ.HaganM.BogardS.BareketZ.HaugenJ. (1998). Intelligent cruise control field operational test (Final report, DOT HS 808 849). Retrieved from file:///C:/Documents%20and%20Settings/NTOT5191/My%20Documents/Downloads/icc1998.pdf
15.
FieldA. (2009). Discovering statistics using SPSS. London, England: SAGE.
16.
FlachJ. M.SmithM. R.StanardT.DittmanS. M. (2004). Collisions: Getting them under control. Advances in Psychology, 135, 67–91.
17.
FristonK. J. (2010). The free-energy principle: A unified brain theory?Nature Reviews Neuroscience, 11, 127–138.
GreenM. (2000). How long does it take to stop? Methodological analysis of driver perception-brake times. Transportation Human Factors, 2, 195–216.
20.
GreenleeE. T.DeLuciaP. R.NewtonD. C. (2018). Driver vigilance in automated vehicles: Hazard detection failures are a matter of time. Human Factors, 60, 465–476.
JamsonA. H.MeratN.CarstenO. M.LaiF. C. (2013). Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transportation Research Part C: Emerging Technologies, 30, 116–125.
23.
JanssonJ.SandinJ.AugustoB.FischerM.BlissingB.KällgrenL. (2014, September 4–5). Design and performance of the VTI SIM IV. Proceedings of the 2014 Driving Simulation Conference Paris, France.
24.
JepmaM.WagenmakersE.BandG. P. H.NieuwenhuisS. (2008). The effects of accessory stimuli on information processing: Evidence from electrophysiology and a diffusion model analysis. Journal of Cognitive Neuroscience, 21, 847–864.
25.
KusanoK. D.GablerH. C. (2012). Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rear-end collisions. IEEE Transactions on Intelligent Transportation Systems, 13, 1546–1555.
26.
LeeD. N. (1976). A theory of visual control of braking based on information about time-to-collision. Perception, 5, 437–459.
27.
LeeJ. D.McGeheeD. V.BrownT. L.ReyesM. L. (2002). Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Human Factors: The Journal of the Human Factors and Ergonomics Society, 44, 314–335.
28.
Ljung AustM.EngströmJ.ViströmM. (2013). Effects of forward collision warning and repeated event exposure on emergency braking. Transportation Research Part F, 18, 34–46.
29.
LouwT.KuoJ.RomanoR.RadhakrishnanV.LennéM. G.MeratN. (2019). Engaging in NDRTs affects drivers’ responses and glance patterns after silent automation failures. Transportation Research Part F, 62, 870–882.
30.
MarkkulaG. (2014, October 27–31) Modeling driver control behavior in both routine and near-accident driving. Proceedings of the 58th Annual Meeting of Human Factors and Ergonomics Society, Chicago, IL.
31.
MarkkulaG.BenderiusO.WolffK.WahdeM. (2013). Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator. Accident Analysis & Prevention, 50, 1266–1277.
32.
MarkkulaG.BoerE.RomanoR.MeratN. (2018). Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering. Biological Cybernetics, 112, 181–207.
33.
MarkkulaG.EngströmJ. (2017, March 20–23). Simulating effects of arousal on lane keeping: Are drowsiness and cognitive load opposite ends of a single spectrum?Abstract presented at the 10th International Conference on Managing Fatigue, San Diego, CA.
34.
MarkkulaG.EngströmJ.LodinJ.BärgmanJ.VictorT. (2016). A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accident Analysis & Prevention, 95, 209–226.
35.
MarkkulaG.LodinJ.WellsP. (2016). The many factors affecting near-collision driver response: A simulator study and a computational model. Unpublished manuscript.
36.
McLaughlinS. B.HankeyJ. M.DingusT. A. (2008). A method for evaluating collision avoidance systems using naturalistic driving data. Accident Analysis & Prevention, 40, 8–16.
37.
MorandoA.VictorT.DozzaM. (2016). Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses. Accident Analysis & Prevention, 97, 206–219.
MuttartJ. W. (2005). Quantifying driver response times based upon research and real life data. In Proceedings of the Third International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (pp. 9–17). Iowa City: Public Policy Center, University of Iowa.
40.
NilssonE. J.AustM. L.EngströmJ.SvanbergB.LindénP. (2018). Effects of cognitive load on response time in an unexpected lead vehicle braking scenario and the detection response task (DRT). Transportation Research Part F: Traffic Psychology and Behaviour, 59, 463–474.
41.
OlsonP. L. (1989). Driver perception response time (Society of Automotive Engineers, Technical Report 890731). Retrieved from https://saemobilus.sae.org/content/890731/
42.
OlsonP. L.SivakM. (1986). Perception-response time to unexpected roadway hazards. Human Factors, 28, 91–96.
43.
PayreW.CestacJ.DelhommeP. (2016). Fully automated driving: Impact of trust and practice on manual control recovery. Human Factors, 58, 229–241.
44.
RatcliffR.Van DongenH. P. A. (2011). Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation. Proceedings of the National Academy of Sciences, 108, 11285–11290.
45.
ReimerB.PettinatoA.FridmanL.LeeJ.MehlerB.SeppeltB.. . . IagnemmaK. (2016, October 24–26). Behavioral impact of drivers’ roles in automated driving. Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ann Arbor, MI.
46.
SaxbyD. J.MatthewsG.WarmJ. S.HitchcockE. M.NeubauerC. (2013). Active and passive fatigue in simulated driving: Discriminating styles of workload regulation and their safety impacts. Journal of Experimental Psychology: Applied, 19, 287–300.
47.
SeppeltB. D.LeeJ. D. (2015). Modeling driver response to imperfect vehicle control automation. Procedia Manufacturing, 3, 2621–2628.
48.
SkottkeE. M.DebusG.WangL.HuesteggeL. (2014). Carryover effects of highly automated convoy driving on subsequent manual driving performance. Human Factors, 56, 1272–1283.
49.
Society of Automotive Engineers. (2018, June). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (Standard J3016, Revised Version). Retrieved from https://saemobilus.sae.org/content/j3016_201806
50.
StonerH. A.FisherD. L.MollenhauerM. (2011). Simulator and scenario factors influencing simulator sickness. In FisherD. L.RizzoM.CairdJ.LeeJ. D. (Eds.), Handbook of driving simulation for engineering, medicine and psychology (pp. 14-1–14-24). Boca Raton, FL: CRC Press.
51.
StrandN.NilssonJ.KarlssonI. M.NilssonL. (2014). Semi-automated versus highly automated driving in critical situations caused by automation failures. Transportation Research Part F, 27, 218–228.
52.
SvärdM.MarkkulaG.EngströmJ.GranumF.BärgmanJ. (2017, October 9–13). A quantitative driver model of pre-crash brake onset and control. Proceedings of the 61st Human Factors and Ergonomics Society Annual Meeting, Austin, TX.
53.
TreatJ. R.TumbasN. S.McDonaldS. T.ShinarD.HumeR. D.MayerR. E.. . . CastellanN. J. (1979). Tri-level study of the causes of traffic accidents: Final report. Executive summary (No. DOTHS034353579TAC). Retrieved from https://deepblue.lib.umich.edu/handle/2027.42/64993
54.
VenkatramanV.LeeJ. D.SchwarzC. W. (2016). Steer or brake? Modeling drivers’ collision-Avoidance behavior by using perceptual cues. Transportation Research Record, 2602, 97–103.
55.
VictorT. W.BärgmanJ.BodaC. N.DozzaM.EngströmJ.FlannaganC.MarkkulaG. (2015). Analysis of naturalistic driving study data: Safer glances, driver inattention, and crash risk (SHRP 2 Report S2-S08A-RW). United States: Transportation Research Board.
56.
VictorT. W.RothoffM.CoelinghE.ÖdblomA.BurgdorfK. (2017). When autonomous vehicles are introduced on a larger scale in the road transport system: The drive me project. In WatzenigD.HornM. (Eds.), Automated driving (pp. 541–546). Switzerland: Springer International Publishing.
57.
VictorT. W.TivestenE.GustavssonP.JohanssonJ.SangbergF.Ljung AustM. (2018). Automation expectation mismatch: incorrect prediction despite eyes on threat and hands on wheel. Human Factors, 60, 1095–1116.
58.
XueQ.MarkkulaG.YanX.MeratN. (2018). Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming. Accident Analysis & Prevention, 118, 114–124.
59.
YoungM. S.StantonN. A. (2007). Back to the future: Brake reaction times for manual and automated vehicles. Ergonomics, 50, 46–58.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
