Contingent negative variation during a modified cueing task in simulated driving
Autoři:
Zizheng Guo aff001; Xi Tan aff001; Yufan Pan aff001; Xian Liu aff001; Guozhen Zhao aff004; Lin Wang aff001; Zhen Peng aff005
Působiště autorů:
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
aff001; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China
aff002; National Engineering Laboratory for Comprehensive Transportation Big Date Application Technology, National Development and Reform Commission, Beijing, China
aff003; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
aff004; School of Arts and Sciences, Arizona State University, Tempe, Arizona, United States of America
aff005
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224966
Souhrn
The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.
Klíčová slova:
Brakes – Cognition – Electrode potentials – Electroencephalography – Event-related potentials – Roads – Scalp – Sensory cues
Zdroje
1. Jermakian JS, Zuby DS. Primary pedestrian crash scenarios: factors relevant to the design of pedestrian detection systems. Insurance Institute for Highway Safety: Arlington, VA, USA: 2011.
2. Sheng P, Wang G. Design of blind spot accident prevention system based on micro-controllers. Electric Engineering. 2018;9:118–9.
3. Life Morning News. Taiyuan: pedestrian-motor vehicle crash has claimed 103 lives within nine months SINA: Shanxi, China. 2015 [Access Date: 2018/09/29]. Available from: http://shanxi.sina.com.cn/news/report/2015-10-19/detail-ifxivscc0165904.shtml.
4. Snyder MB, Knoblauch RL. Pedestrian safety: the identification of precipitating factors and possible countermeasures. Department of Transportation, National Highway Traffic Safety Administration, Washington, D.C.: 1971.
5. Zhu X, Dai Z, Chen F, Pan X, Xu M. Using the visual intervention influence of pavement marking for rutting mitigation–Part II: visual intervention timing based on the finite element simulation. International Journal of Pavement Engineering. 2019;20(5):573–84.
6. Wu G, Chen F, Pan X, Xu M, Zhu X. Using the visual intervention influence of pavement markings for rutting mitigation–part I: preliminary experiments and field tests. International Journal of Pavement Engineering. 2019;20(6):734–46.
7. Park HS, Park MW, Won KH, Kim K-H, Jung SK. In-Vehicle AR-HUD System to Provide Driving-Safety Information. ETRI Journal. 2013;35(6):1038–47. doi: 10.4218/etrij.13.2013.0041 WOS:000328300800011.
8. Doecke S, Anderson R, Mackenzie J, Ponte G, editors. The potential of autonomous emergency braking systems to mitigate passenger vehicle crashes. Proceedings of the Australasian Road Safety Research, Policing and Education Conference; 2012; Wellington, New Zealand: Australasian College of Road Safety.
9. Retting RA, Ferguson SA, McCartt AT. Review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes. American Journal of Public Health. 2003;93(9):1456–63. doi: 10.2105/ajph.93.9.1456 WOS:000185027300026. 12948963
10. Pradhan AK, Hammel KR, DeRamus R, Pollatsek A, Noyce DA, Fisher DL. Using eye movements to evaluate effects of driver age on risk perception in a driving simulator. Human Factors. 2005;47(4):840–52. doi: 10.1518/001872005775570961 WOS:000235422600012. 16553070
11. Shahar A, Alberti CF, Clarke D, Crundall D. Hazard perception as a function of target location and the field of view. Accident Analysis & Prevention. 2010;42(6):1577–84. doi: 10.1016/j.aap.2010.03.016 WOS:000282240500009. 20728606
12. Chan E, Pradhan AK, Pollatsek A, Knodler MA, Fisher DL. Are driving simulators effective tools for evaluating novice drivers' hazard anticipation, speed management, and attention maintenance skills? Transportation Research Part F-Traffic Psychology and Behaviour. 2010;13(5):343–53. doi: 10.1016/j.trf.2010.04.001 WOS:000281176000006. 20729986
13. Fisher DL, Pollatsek AP, Pradhan A. Can novice drivers be trained to scan for information that will reduce their likelihood of a crash? Injury Prevention. 2006;12 Suppl 1 (Suppl 1): i25–i29. doi: 10.1136/ip.2006.012021 WOS:000239015400006. 16788108
14. Pradhan AK, Pollatsek A, Knodler M, Fisher DL. Can younger drivers be trained to scan for information that will reduce their risk in roadway traffic scenarios that are hard to identify as hazardous? Ergonomics. 2009;52(6):657–73. doi: 10.1080/00140130802550232 WOS:000266441200003. 19296315
15. Chen F, Peng H, Ma X, Liang J, Hao W, Pan X. Examining the safety of trucks under crosswind at bridge-tunnel section: A driving simulator study. Tunnelling and Underground Space Technology. 2019;92:103034. https://doi.org/10.1016/j.tust.2019.
16. van Boxtel GJM, Bocker KBE. Cortical measures of anticipation. Journal of Psychophysiology. 2004;18(2–3):61–76. doi: 10.1027/0269-8803.18.23.61 WOS:000223540100002.
17. Walter WG, Winter AL, Cooper R, McCallum WC, Aldridge VJ. Contingent negative variation: An electric sign of sensorimotor association expectancy in human brain. Nature. 1964;203(494):380–4. doi: 10.1038/203380a0 WOS:A19648821B00707. 14197376
18. Gaillard AW. Effects of warning-signal modality on the contingent negative variation (CNV). Biological Psychology. 1976;4(2):139–54. doi: 10.1016/0301-0511(76)90013-2 MEDLINE:1276304. 1276304
19. Leynes PA, Allen JD, Marsh RL. Topographic differences in CNV amplitude reflect different preparatory processes. International Journal of Psychophysiology. 1998;31(1):33–44. doi: 10.1016/s0167-8760(98)00032-4 WOS:000078372900003. 9934619
20. Ruchkin DS, Sutton S, Mahaffey D, Glaser J. Terminal CNV in the absence of motor response. Electroencephalography and Clinical Neurophysiology. 1986;63(5):445–63. doi: 10.1016/0013-4694(86)90127-6 WOS:A1986A989500007. 2420561
21. Tecce JJ. Contingent negative variation (CNV) and psychological processes in man. Psychological Bulletin. 1972;77(2):73–108. doi: 10.1037/h0032177 4621420
22. Brunia CHM, van Boxtel GJM. Wait and see. International Journal of Psychophysiology. 2001;43(1):59–75. doi: 10.1016/s0167-8760(01)00179-9 WOS:000173069100005. 11742685
23. Nagai Y, Critchley HD, Featherstone E, Fenwick PBC, Trimble MR, Dolan RJ. Brain activity relating to the contingent negative variation: an fMRI investigation. Neuroimage. 2004;21(4):1232–41. doi: 10.1016/j.neuroimage.2003.10.036 WOS:000220723900005. 15050551
24. Irwin DA, Knott JR, McAdam DW, Rebert CS. Motivational determinants of the "contingent negative variation". Electroencephalography and Clinical Neurophysiology. 1966;21(6):538–43. doi: 10.1016/0013-4694(66)90172-6 WOS:A19668659300002. 4162883
25. Arjona A, Escudero M, Gómez CM. Updating of Attentional and Premotor Allocation Resources as function of previous trial outcome. Scientific Reports. 2014;4:4526. doi: 10.1038/srep04526 24681570
26. Connor WH, Lang PJ. Cortical slow-wave and cardiac rate responses in stimulus orientation and reaction time conditions. Journal of Experimental Psychology. 1969;82(2):310–20. doi: 10.1037/h0028181 WOS:A1969E712800019. 5378048
27. Weerts TC, Lang PJ. The effects of eye fixation and stimulus and response location on the contingent negative variation (CNV). Biological Psychology. 1973;1(1):1–19. doi: 10.1016/0301-0511(73)90010-0 MEDLINE:4804295. 4804295
28. Bluschke A, Schuster J, Roessner V, Beste C. Neurophysiological mechanisms of interval timing dissociate inattentive and combined ADHD subtypes. Scientific Reports. 2018;8:2033. doi: 10.1038/s41598-018-20484-0 29391481
29. Broyd SJ, Richards HJ, Helps SK, Chronaki G, Bamford S, Sonuga-Barke EJS. An electrophysiological monetary incentive delay (e-MID) task: A way to decompose the different components of neural response to positive and negative monetary reinforcement. Journal of Neuroscience Methods. 2012;209(1):40–9. doi: 10.1016/j.jneumeth.2012.05.015 WOS:000307132700005. 22659003
30. Brunia CHM, Hackley SA, van Boxtel GJM, Kotani Y, Ohgami Y. Waiting to perceive: Reward or punishment? Clinical Neurophysiology. 2011;122(5):858–68. doi: 10.1016/j.clinph.2010.12.039 WOS:000290098700004. 21215692
31. Yasuda K, Ray LB, Cote KA. Anticipatory attention during the sleep onset period. Consciousness and Cognition. 2011;20(3):912–9. doi: 10.1016/j.concog.2010.12.016 WOS:000294515400040. 21269842
32. Khaliliardali Z, Chavarriaga R, Gheorghe LA, Millán JDR, editors. Detection of anticipatory brain potentials during car driving. International Conference of the IEEE Engineering in Medicine & Biology Society; 2012; San Diego, CA, USA: IEEE.
33. Khaliliardali Z, Chavarriaga R, Gheorghe LA, Millan JdR. Action prediction based on anticipatory brain potentials during simulated driving. Journal of Neural Engineering. 2015;12:6. doi: 10.1088/1741-2560/12/6/066006 WOS:000374884100006. 26401885
34. Duma GM, Mento G, Manari T, Martinelli M, Tressoldi P. Driving with Intuition: A Preregistered Study about the EEG Anticipation of Simulated Random Car Accidents. Plos One. 2017;12(1): e0170370. doi: 10.1371/journal.pone.0170370 WOS:000392381100059. 28103303
35. Zhao G, Wu C. Effectiveness and acceptance of the intelligent speeding prediction system (ISPS). Accident Analysis & Prevention. 2013;52:19–28.
36. Chai J, Zhao G. Effect of exposure to aggressive stimuli on aggressive driving behavior at pedestrian crossings at unmarked roadways. Accident Analysis & Prevention. 2016;88:159–68.
37. Schevernels H, Krebs RM, Santens P, Woldorff MG, Boehler CN. Task preparation processes related to reward prediction precede those related to task-difficulty expectation. Neuroimage. 2014;84:639–47. doi: 10.1016/j.neuroimage.2013.09.039 WOS:000328868600058. 24064071
38. Mai M, Wang L, Prokop G. Advancement of the car following model of Wiedemann on lower velocity ranges for urban traffic simulation. Transportation Research Part F-Traffic Psychology and Behaviour. 2017;61:30–7.
39. Niazi I, Jiang N, Tiberghien O, Nielsen J, Dremstrup K, Farina D. Detection of movement intention from single-trial movement-related cortical potentials. Journal of Neural Engineering. 2011;8:6. https://doi.org/10.1088/1741-2560/8/6/066009.
40. Lew E, Chavarriaga R, Silvoni S, Millan JdR. Detection of self-paced reaching movement intention from EEG signals. Frontiers in Neuroengineering. 2012;5:13. doi: 10.3389/fneng.2012.00013 23055968
41. Gheorghe L, Chavarriaga R, Milian JdR, editors. Steering Timing Prediction in a Driving Simulator Task. International Conference of the IEEE Engineering in Medicine and Biology Society; 2013; Osaka, Japan: IEEE.
42. Kropp P, Kiewitt A, Gbel H, Vetter P, Gerber W. Reliability and stability of contingent negative variation. Applied Psychophysiology and Biofeedback. 2000;25(1):33–41. 10832508
43. Kirsch W, Hennighausen E. ERP correlates of linear hand movements: Distance dependent changes. Clinical Neurophysiology. 2010;121(8):1285–92. doi: 10.1016/j.clinph.2010.02.151 20227915
44. Garipelli G, Chavarriaga R, Millan JdR. Single trial analysis of slow cortical potentials: a study on anticipation related potentials. Journal of Neural Engineering. 2013;10:3. https://doi.org/10.1088/1741-2560/10/3/036014.
45. Ritter W, Rotkin L, Vaughan HG. The modality specificity of the slow negative wave. Psychophysiology. 1980;17(3):222–7. doi: 10.1111/j.1469-8986.1980.tb00138.x WOS:A1980JR29500002. 7384371
46. Rohrbaugh JW, Syndulko K, Lindsley DB. Brain wave components of the contingent negative variation in humans. Science. 1976;191:1055–7. doi: 10.1126/science.1251217 1251217
47. van Boxtel GJ, Brunia CH. Motor and non-motor aspects of slow brain potentials. Biological Psychology. 1994;38(1):37–51. doi: 10.1016/0301-0511(94)90048-5 7999929
48. Muhrer E, Vollrath M, editors. Anticipation in car following: Modelling expectations for the development of driver assistance systems. 6th IFAC Symposium on Advances in Automotive Control; 2010; Munlch, Germany: IFAC.
49. Mvd Hulst. Anticipation and the adaptive control of safety margins in driving. Ergonomics. 1999;42(2):336–45. doi: 10.1080/001401399185694 WOS:000078341200006.
Článek vyšel v časopise
PLOS One
2019 Číslo 11
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy