AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (10.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Study of Driver’s Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement

Department of Engineering Physics, Tsinghua University, Beijing 100084, China
Department of Automation and also with BNRist, Tsinghua University, Beijing 100084, China
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
Show Author Information

Abstract

Understanding how drivers perceive and respond to external stimuli in driving tasks is important for the development of advanced driving technologies and human-computer interaction. In this paper, we conducted a temporal response analysis between driving data and cortical activation data measured by functional near-infrared spectroscopy (fNIRS), based on a naturalistic driving experiment. Temporal response function analysis indicates that stimuli, which elicit significant responses of drivers include distance, acceleration, time headway, and the velocity of the preceding vehicle. For these stimuli, the time lags and response patterns were further discussed. The influencing factors on drivers’ perception were also studied based on various driver characteristics. These conclusions can provide guidance for the construction of car-following models, the safety assessment of drivers and the improvement of advanced driving technologies.

Keywords

naturalistic driving experimentdriving safetyperceptionresponsetemporal response functionfNIRS

References

[1]

A. Martinho, N. Herber, M. Kroesen, and C. Chorus, Ethical issues in focus by the autonomous vehicles industry, Transp. Rev., vol. 41, no. 5, pp. 556–577, 2021.
Crossref Google Scholar
[2]

S. Sreetharan and M. Schutz, Improving human–computer interface design through application of basic research on audiovisual integration and amplitude envelope, Multimodal Technol. Interact., vol. 3, no. 1, p. 4, 2019.
Crossref Google Scholar
[3]

Q. Wang, H. Chen, J. Gong, X. Zhao, and Z. Li, Studying driver’s perception arousal and takeover performance in autonomous driving, Sustainability, vol. 15, no. 1, p. 445, 2022.
Crossref Google Scholar
[4]
M. Kumar, A. Kant, P. Kaktan, R. Bishnoi, and K. Upadhyay, Arduino based system to prevent vehicle accidents, in Proc. Int. Conf. Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India, 2021.
Crossref
[5]
I. Kim, E. Pakdamanian, and V. Hiremath, Fundamentals and emerging trends of neuroergonomic applications to driving and navigation. Neuroergonomics, Cham, Switzland: Springer, 2020, pp. 389–406.
Crossref
[6]

A. Ali Saleem, H. U. R. Siddiqui, M. A. Raza, F. Rustam, S. Dudley, and I. Ashraf, A systematic review of physiological signals based driver drowsiness detection systems, Cogn. Neurodyn., vol. 17, no. 5, pp. 1229–1259, 2023.
Crossref Google Scholar
[7]
R. Zheng, Z. Wang, Y. He, and J. Zhang, EEG-based brain functional connectivity representation using amplitude locking value for fatigue-driving recognition, Cogn. Neurodyn., vol. 16, no. 2, pp. 325–336, 2022.
Crossref
[8]

Y. Sabahi, S. K. Setarehdan, and A. M. Nasrabadi, Dynamic causal modeling of evoked responses during emergency braking: An ERP study, Cogn. Neurodyn., vol. 16, no. 2, pp. 353–363, 2022.
Crossref Google Scholar
[9]

G. Hou and G. Lu, Semantic processing and emotional evaluation in the traffic sign understanding process: Evidence from an event-related potential study, Transp. Res. Part F Traffic Psychol. Behav., vol. 59, pp. 236–243, 2018.
Crossref Google Scholar
[10]

M. Karthaus, E. Wascher, and S. Getzmann, Distraction in the driving simulator: An event-related potential (ERP) study with young, middle-aged, and older drivers, Safety, vol. 7, no. 2, p. 36, 2021.
Crossref Google Scholar
[11]

Z. Guo, R. Chen, X. Liu, G. Zhao, Y. Zheng, M. Gong, and J. Zhang, The impairing effects of mental fatigue on response inhibition: An ERP study, PLoS One, vol. 13, no. 6, p. e0198206, 2018.
Crossref Google Scholar
[12]

G. Lu and G. Hou, Effects of semantic congruence on sign identification: An ERP study, Hum. Factors, vol. 62, no. 5, pp. 800–811, 2020.
Crossref Google Scholar
[13]
M. Lahmer, C. Glatz, V. C. Seibold, and L. L. Chuang, Looming auditory collision warnings for semi-automated driving: An ERP study, in Proc. 10th Int. Conf. on Automotive User Interfaces and Interactive Vehicular Applications, Toronto, Canada, 2018, pp. 310–319.
Crossref
[14]

F. Rashid Izullah, M. Koivisto, V. Nieminen, M. Luimula, and H. Hämäläinen, Aging and sleep deprivation affect different neurocognitive stages of spatial information processing during a virtual driving task–An ERP study, Transp. Res. Part F Traffic Psychol. Behav., vol. 89, pp. 399–406, 2022.
Crossref Google Scholar
[15]
Z. Ren, R. Li, B. Chen, H. Zhang, Y. Ma, C. Wang, Y. Lin, and Y. Zhang, EEG-based driving fatigue detection using a two-level learning hierarchy radial basis function, Front. Neurorobot., vol. 15, p. 618408, 2021.
Crossref
[16]

Y. Ma, B. Chen, R. Li, C. Wang, J. Wang, Q. She, Z. Luo, and Y. Zhang, Driving fatigue detection from EEG using a modified PCANet method, Comput. Intell. Neurosci., vol. 2019, p. 4721863, 2019.
Crossref Google Scholar
[17]

D. Jing, D. Liu, S. Zhang, and Z. Guo, Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment, Int. J. Transp. Sci. Technol., vol. 9, no. 4, pp. 366–376, 2020.
Crossref Google Scholar
[18]

N. Sciaraffa, G. Di Flumeri, D. Germano, A. Giorgi, A. Di Florio, G. Borghini, A. Vozzi, V. Ronca, R. Varga, M. van Gasteren et al., Validation of a light EEG-based measure for real-time stress monitoring during realistic driving, Brain Sci., vol. 12, no. 3, p. 304, 2022.
Crossref Google Scholar
[19]

T. Tuncer, S. Dogan, F. Ertam, and A. Subasi, A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals, Cogn. Neurodyn., vol. 15, no. 2, pp. 223–237, 2021.
Crossref Google Scholar
[20]

M. Seet, A. Dragomir, J. Harvy, N. V. Thakor, and A. Bezerianos, Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation, Accid. Anal. Prev., vol. 168, p. 106588, 2022.
Crossref Google Scholar
[21]
G. Gaurav, R. S. Anand, and V. Kumar, EEG based cognitive task classification using multifractal detrended fluctuation analysis, Cogn. Neurodyn., vol. 15, no. 6, pp. 999–1013, 2021.
Crossref
[22]

I. Hussain, S. Young, and S.-J. Park, Driving-induced neurological biomarkers in an advanced driver-assistance system, Sensors, vol. 21, no. 21, p. 6985, 2021.
Crossref Google Scholar
[23]
A. Affanni, T. A. Najafi, and S. Guerci, Design of a low cost EEG sensor for the measurement of stress-related brain activity during driving, in Proc. 2021 IEEE Int. Workshop on Metrology for Automotive (MetroAutomotive), Bologna, Italy, 2021, pp. 152–156.
Crossref
[24]

E. Wascher, S. Arnau, J. E. Reiser, G. Rudinger, M. Karthaus, G. Rinkenauer, F. Dreger, and S. Getzmann, Evaluating mental load during realistic driving simulations by means of round the ear electrodes, Front. Neurosci., vol. 13, p. 940, 2019.
Crossref Google Scholar
[25]
X. Zhang and X. Yan, Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform, Accident Analysis & Prevention, vol. 180, p. 106910, 2023.
Crossref
[26]
Y.-C. Liu, N. Figalova, M. Baumann, and K. Bengler, Human-machine interface evaluation using EEG in driving simulator, arXiv preprint arXiv: 2406.09608, 2023.
Crossref
[27]

G. Li, W. Yan, S. Li, X. Qu, W. Chu, and D. Cao, A temporal–spatial deep learning approach for driver distraction detection based on EEG signals, IEEE Trans. Automat. Sci. Eng., vol. 19, no. 4, pp. 2665–2677, 2022.
Crossref Google Scholar
[28]

G.-D. Voinea, R. G. Boboc, I.-D. Buzdugan, C. Antonya, and G. Yannis, Texting while driving: A literature review on driving simulator studies, Int. J. Environ. Res. Public Health, vol. 20, no. 5, p. 4354, 2023.
Crossref Google Scholar
[29]

S. Soares, S. Ferreira, and A. Couto, Driving simulator experiments to study drowsiness: A systematic review, Traffic Inj. Prev., vol. 21, no. 1, pp. 29–37, 2020.
Crossref Google Scholar
[30]

R. A. Wynne, V. Beanland, and P. M. Salmon, Systematic review of driving simulator validation studies, Saf. Sci., vol. 117, pp. 138–151, 2019.
Crossref Google Scholar
[31]

A. O. Caffò, L. Tinella, A. Lopez, G. Spano, Y. Massaro, A. Lisi, F. Stasolla, R. Catanesi, F. Nardulli, I. Grattagliano et al., The drives for driving simulation: A scientometric analysis and a selective review of reviews on simulated driving research, Front. Psychol., vol. 11, p. 917, 2020.
Crossref Google Scholar
[32]

G. Casutt, N. Theill, M. Martin, M. Keller, and L. Jäncke, The drive-wise project: Driving simulator training increases real driving performance in healthy older drivers, Front. Aging Neurosci., vol. 6, p. 85, 2014.
Crossref Google Scholar
[33]

K. Yamamoto, H. Takahashi, T. Sugimachi, K. Nakano, and Y. Suda, The study of driver’s brain activity and behaviour on DS test using fNIRS, IFAC-PapersOnLine, vol. 51, no. 34, pp. 244–249, 2019.
Crossref Google Scholar
[34]
J. P. Ehsani, J. L. Harbluk, J. Bärgman, A. Williamson, J. P. Michael, R. Grzebieta, J. Olivier, J. Eusebio, J. Charlton, S. Koppel, et al., Naturalistic driving studies: An overview and international perspective, in International Encyclopedia of Transportation, R. Vickerman, ed. Amsterdam, The Netherlands: Elsevier, 2021, pp. 20–38.
Crossref
[35]

I. van Schagen and F. Sagberg, The potential benefits of naturalistic driving for road safety research: Theoretical and empirical considerations and challenges for the future, Procedia Soc. Behav. Sci., vol. 48, pp. 692–701, 2012.
Crossref Google Scholar
[36]
L. Fridman, D. E. Brown, M. Glazer, W. Angell, S. Dodd, B. Jenik, J. Terwilliger, A. Patsekin, J. Kindelsberger, L. Ding, et al., MIT advanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automation, IEEE Access, vol. 7, pp. 102021–102038, 2019.
Crossref
[37]

A. Zyner, S. Worrall, and E. Nebot, Naturalistic driver intention and path prediction using recurrent neural networks, IEEE Trans. Intell. Transport. Syst., vol. 21, no. 4, pp. 1584–1594, 2020.
Crossref Google Scholar
[38]

V. L. Neale, T. A. Dingus, S. G. Klauer, J. Sudweeks, and M. Goodman, An overview of the 100-car naturalistic study and findings, National Highway Traffic Safety Administration, vol. 5, p. 0400, 2005.
Google Scholar
[39]

J. Wu and H. Xu, Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data, J. Safety Res., vol. 63, pp. 177–185, 2017.
Crossref Google Scholar
[40]
M. Benmimoun, A. Pütz, A. Zlocki, and L. Eckstein, eurofot: Field operational test and impact assessment of advanced driver assistance systems: Final results, in Proc. FISITA 2012 World Automotive Congress, Berlin, Germany, 2012, pp. 537–547.
Crossref
[41]
X. Sun, X. Zhu, K. Zhang, L. Li, Z. Ma, and D. Wang, Automatic detection method research of incidents in China-FOT database, in Proc. 2016 IEEE 19th Int. Conf. on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 2016, pp. 754–759.
Crossref
[42]

M. M. Ahmed, M. N. Khan, A. Das, and S. E. Dadvar, Global lessons learned from naturalistic driving studies to advance traffic safety and operation research: A systematic review, Accid. Anal. Prev., vol. 167, p. 106568, 2022.
Crossref Google Scholar
[43]

X. Wang, Q. Liu, F. Guo, X. Xu, and X. Chen, Causation analysis of crashes and near crashes using naturalistic driving data, Accident Analysis & Prevention, vol. 177, p. 106821, 2022.
Crossref Google Scholar
[44]

A. Barnwal, P. Chakraborty, A. Sharma, L. Riera-Garcia, K. Ozcan, S. Davami, S. Sarkar, M. Rizzo, and J. Merickel, Sugar and stops in drivers with insulin-dependent type 1 diabetes, Accid. Anal. Prev., vol. 173, p. 106692, 2022.
Crossref Google Scholar
[45]

J. Wang, H. Huang, Y. Li, H. Zhou, J. Liu, and Q. Xu, Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis, Accid. Anal. Prev., vol. 145, p. 105680, 2020.
Crossref Google Scholar
[46]
S. Brigadoi, L. Ceccherini, S. Cutini, F. Scarpa, P. Scatturin, J. Selb, L. Gagnon, D. A. Boas, and R. J. Cooper, Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data, NeuroImage, vol. 85, pp. 181–191, 2014.
Crossref
[47]

M. Ferrari and V. Quaresima, A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application, NeuroImage, vol. 63, no. 2, pp. 921–935, 2012.
Crossref Google Scholar
[48]

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology, NeuroImage, vol. 85, pp. 6–27, 2014.
Crossref Google Scholar
[49]

X. Cui, S. Bray, D. M. Bryant, G. H. Glover, and A. L. Reiss, A quantitative comparison of NIRS and fMRI across multiple cognitive tasks, NeuroImage, vol. 54, no. 4, pp. 2808–2821, 2011.
Crossref Google Scholar
[50]

M. A. Yücel, A. V. Lühmann, F. Scholkmann, J. Gervain, I. Dan, H. Ayaz, D. Boas, R. J. Cooper, J. Culver, C. E. Elwell, et al., Best practices for fNIRS publications, Neurophotonics, vol. 8, no. 1, p. 012101, 2021.
Crossref Google Scholar
[51]

N. P. Friedman and T. W. Robbins, The role of prefrontal cortex in cognitive control and executive function, Neuropsychopharmacology, vol. 47, no. 1, pp. 72–89, 2022.
Crossref Google Scholar
[52]

A. Collins and E. Koechlin, Reasoning, learning, and creativity: Frontal lobe function and human decision-making, PLoS Biol., vol. 10, no. 3, p. e1001293, 2012.
Crossref Google Scholar
[53]
Z. Wu, S. Wu, H. Li, J. Zhao, S. Hou, J. Fan, and Y. Wang, Access to smartness: An intelligent behaviour monitoring and safeguard system for drivers, in Proc. Fifth Int. Congress on Information and Communication Technology, Singapore, 2020, pp. 536–544.
Crossref
[54]
W. Zhang, L. Guo, and D. Liu, Transcerebral information coordination in directional hippocampus-prefrontal cortex network during working memory based on bimodal neural electrical signals, Cogn. Neurodyn., vol. 16, no. 6, pp. 1409–1425, 2022.
Crossref
[55]

S. Balters, J. M. Baker, J. W. Geeseman, and A. L. Reiss, A methodological review of fNIRS in driving research: Relevance to the future of autonomous vehicles, Front. Hum. Neurosci., vol. 15, p. 637589, 2021.
Crossref Google Scholar
[56]

J. L. Bruno, J. M. Baker, A. Gundran, L. K. Harbott, Z. Stuart, A. M. Piccirilli, S. M. Hadi Hosseini, J. C. Gerdes, and A. L. Reiss, Mind over motor mapping: Driver response to changing vehicle dynamics, Hum. Brain Mapp., vol. 39, no. 10, pp. 3915–3927, 2018.
Crossref Google Scholar
[57]

Y.-O. Li, T. Eichele, V. D. Calhoun, and T. Adali, Group study of simulated driving fMRI data by multiset canonical correlation analysis, J. Signal Process. Syst., vol. 68, no. 1, pp. 31–48, 2012.
Crossref Google Scholar
[58]

H. J. Spiers and E. A. Maguire, Neural substrates of driving behaviour, NeuroImage, vol. 36, no. 1, pp. 245–255, 2007.
Crossref Google Scholar
[59]

D. A. Boas, A. M. Dale, and M. A. Franceschini, Diffuse optical imaging of brain activation: Approaches to optimizing image sensitivity, resolution, and accuracy, NeuroImage, vol. 23, pp. S275–S288, 2004.
Crossref Google Scholar
[60]
S. Hansun, A new approach of moving average method in time series analysis, in Proc. 2013 Conf. on New Media Studies (CoNMedia ), Tangerang, Indonesia, 2013, pp. 1–4.
Crossref
[61]

L. Evans and R. Rothery, Perceptual thresholds in car-following: A comparison of recent measurements with earlier results, Transp. Sci., vol. 11, no. 1, pp. 60–72, 1977.
Crossref Google Scholar
[62]

S. Jin, D.-H. Wang, Z.-Y. Huang, and P.-F. Tao, Visual angle model for car-following theory, Phys. A Stat. Mech. Appl., vol. 390, no. 11, pp. 1931–1940, 2011.
Crossref Google Scholar
[63]

P. Reddy, M. Izzetoglu, P. A. Shewokis, M. Sangobowale, R. Diaz-Arrastia, and K. Izzetoglu, Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli, Sci. Rep., vol. 11, p. 23457, 2021.
Crossref Google Scholar
[64]

X. Cui, S. Bray, and A. L. Reiss, Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics, NeuroImage, vol. 49, no. 4, pp. 3039–3046, 2010.
Crossref Google Scholar
[65]

M. J. Crosse, G. M. Di Liberto, A. Bednar, and E. C. Lalor, The multivariate temporal response function (mTRF) toolbox: A MATLAB toolbox for relating neural signals to continuous stimuli, Front. Hum. Neurosci., vol. 10, p. 604, 2016.
Crossref Google Scholar
[66]

X. Zhang, J. Li, Z. Li, B. Hong, T. Diao, X. Ma, G. Nolte, A. K. Engel, and D. Zhang, Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension, NeuroImage, vol. 282, p. 120404, 2023.
Crossref Google Scholar
[67]

J. Li, B. Hong, G. Nolte, A. K. Engel, and D. Zhang, Preparatory delta phase response is correlated with naturalistic speech comprehension performance, Cogn. Neurodyn., vol. 16, no. 2, pp. 337–352, 2022.
Crossref Google Scholar
[68]
J. Li, B. Hong, G. Nolte, A. K. Engel, and D. Zhang, EEG-based speaker–listener neural coupling reflects speech-selective attentional mechanisms beyond the speech stimulus, Cereb. Cortex, vol. 33, no. 22, pp. 11080–11091, 2023.
Crossref
[69]

A. G. Asuero, A. Sayago, and A. González, The correlation coefficient: An overview, Critical reviews in analytical chemistry, vol. 36, no. 1, pp. 41–59, 2006.
Crossref Google Scholar
[70]

P. Schober, C. Boer, and L. A. Schwarte, Correlation coefficients: Appropriate use and interpretation, Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, 2018.
Crossref Google Scholar
[71]

R. H. Gracely, M. E. Geisser, T. Giesecke, M. A. B. Grant, F. Petzke, D. A. Williams, and D. J. Clauw, Pain catastrophizing and neural responses to pain among persons with fibromyalgia, Brain, vol. 127, no. 4, pp. 835–843, 2004.
Crossref Google Scholar
[72]

A. Zafar and K.-S. Hong, Reduction of onset delay in functional near-infrared spectroscopy: Prediction of HbO/HbR signals, Front. Neurorobot., vol. 14, p. 10, 2020.
Crossref Google Scholar
[73]

Y. Chen, J. Tang, Y. Chen, J. Farrand, M. A. Craft, B. W. Carlson, and H. Yuan, Amplitude of fNIRS resting-state global signal is related to EEG vigilance measures: A simultaneous fNIRS and EEG study, Front. Neurosci., vol. 14, p. 560878, 2020.
Crossref Google Scholar
[74]

M. Risto and M. H. Martens, Time and space: The difference between following time headway and distance headway instructions, Transp. Res. Part F Traffic Psychol. Behav., vol. 17, pp. 45–51, 2013.
Crossref Google Scholar
[75]

R. E. Chandler, R. Herman, and E. W. Montroll, Traffic dynamics: Studies in car following, Oper. Res., vol. 6, no. 2, pp. 165–184, 1958.
Crossref Google Scholar
[76]
G. F. Newell A simplified car-following theory: A lower order model, Transp. Res. Part B Methodol., vol. 36, no. 3, pp. 195–205, 2002.
Crossref
[77]

M. Bando, K. Hasebe, K. Nakanishi, and A. Nakayama, Analysis of optimal velocity model with explicit delay, Phys. Rev. E, vol. 58, no. 5, pp. 5429–5435, 1998.
Crossref Google Scholar
[78]

R. Jiang, Q. Wu, and Z. Zhu, Full velocity difference model for a car-following theory, Phys. Rev. E, vol. 64, no. 1, p. 017101, 2001.
Crossref Google Scholar
[79]

L. Evans and R. Rothery, Detection of the sign of relative motion when following a vehicle, Hum. Factors, vol. 16, no. 2, pp. 161–173, 1974.
Crossref Google Scholar
[80]

Y. Jia, D. Qu, H. Song, T. Wang, and Z. Zhao, Car-following characteristics and model of connected autonomous vehicles based on safe potential field, Phys. A Stat. Mech. Appl., vol. 586, p. 126502, 2022.
Crossref Google Scholar
[81]

B. Bae, H. Lim, and J. So, Calibration of car-following models using a dual genetic algorithm with central composite design, J. Korea Inst. Intelligent. Transp. Syst., vol. 18, no. 2, pp. 29–43, 2019.
Crossref Google Scholar
[82]

R. Chumsamutr and T. Fujioka, Development of car-following model with parameter identification by genetic algorithm, JSME Int. J., Ser. C, vol. 46, no. 1, pp. 188–196, 2003.
Crossref Google Scholar
[83]

P. Sun, X. Wang, and M. Zhu, Modeling car-following behavior on freeways considering driving style, J. Transp. Eng. Part A Syst., vol. 147, no. 12, p. 04021083, 2021.
Crossref Google Scholar
[84]

J. Sangster, H. Rakha, and J. Du, Application of naturalistic driving data to modeling of driver car-following behavior, Transp. Res. Rec. J. Transp. Res. Board, vol. 2390, no. 1, pp. 20–33, 2013.
Crossref Google Scholar
[85]

M. Hagl and D. R. Kouabenan, Safe on the road–Does Advanced Driver-Assistance Systems Use affect Road Risk Perception, Transp. Res. Part F Traffic Psychol. Behav., vol. 73, pp. 488–498, 2020.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 2,
April 2025
Pages 796-812
DOI: 10.26599/TST.2024.9010002
Cite this article:
Li B, Pei X, Zhang D, et al. Study of Driver’s Perception in Driving Tasks Based on Naturalistic Driving Experiments and fNIRS Measurement. Tsinghua Science and Technology, 2025, 30(2): 796-812. https://doi.org/10.26599/TST.2024.9010002

91

Views

5

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 06 November 2023
Revised: 28 December 2023
Accepted: 02 January 2024
Published: 09 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return