Ying Liang
ABSTRACT
Real-time collection of the drivers’ driving intention and situation awareness in the experimental environment is helpful to analyze the drivers’ cognitive mechanism and build a driving behavior model. Some researchers have proposed a verbal protocol analysis method requiring drivers to loudly speak out their thoughts during driving. However, the verbal protocol analysis method occupies the drivers’ language information system, which will increase the drivers’ cognitive load and affect driving behavior. Particularly, in multi-drivers driving simulator studies, the verbal cognition collection will be interfered by different participants. This paper proposes a low cognitive load real-time quiet cognition collection method based on eye-tracking. The visual analysis method collects the drivers’ thoughts by making them briefly fix their eyes on their options about the relevant cognition questions during the driving process. To validate the visual protocol analysis method, this paper conducted a driving simulation experiment. 24 drivers participated in different methods for driving cognition collection at unprotected left-turn signalized intersections. The driving intention and situation awareness of each driver were collected in three different experiments: real-time verbal protocol analysis, real-time visual protocol analysis, and the control group without any collection method. Results show that the two methods can collect the drivers’ cognition, data complete rate of the visual protocol analysis method was 95.83%, higher than 79.17% data complete rate of the verbal protocol analysis method. Besides, compared with 1.37s Latency (a cognitive load indicator, the smaller the value, the higher the cognitive load) in the verbal protocol analysis method, the Latency of the visual protocol analysis method is 2.13s, 55.47% greater value. The visual protocol analysis method is effective to collect the drivers’ cognition in real-time, and can be further used in multi-drivers driving simulation experiments.
- Siel Depestele, Veerle Ross, Stefanie Verstraelen, Kris Brijs, Tom Brijs, Kim van Dun, and Raf Meesen. 2020. The impact of cognitive functioning on driving performance of older persons in comparison to younger age groups: A systematic review. Transp. Res. Pt. F-Traffic Psychol. Behav. 73, (August 2020), 433–452. DOI:https://doi.org/10.1016/j.trf.2020.07.009Google Scholar
Cross Ref
- Rebecca Currano, So Yeon Park, Dylan James Moore, Kent Lyons, and David Sirkin. 2021. Little Road Driving HUD: Heads-Up Display Complexity Influences Drivers’ Perceptions of Automated Vehicles. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), Association for Computing Machinery, New York, NY, USA. DOI:https://doi.org/10.1145/3411764.3445575Google ScholarDigital Library
- Dario D. Salvucci. 2013. Distraction beyond the Driver: Predicting the Effects of in-Vehicle Interaction on Surrounding Traffic. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13), Association for Computing Machinery, New York, NY, USA, 3131–3134. DOI:https://doi.org/10.1145/2470654.2466427Google ScholarDigital Library
- M Bassani, L Catani, A Hazoor, A Hoxha, A Lioi, A Portera, and L Tefa. 2023. Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance. TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR 95, (May 2023), 239–250. DOI:https://doi.org/10.1016/j.trf.2023.04.013Google ScholarCross Ref
- Sina Sheikholeslami, Mahmoud Saffarzadeh, Amir Reza Mamdoohi, and Morteza Asadamraji. 2023. How does a driver feel behind the wheel? An exploratory study of drivers’ emotions and the effect of their sociodemographic background. Accid. Anal. Prev. 183, (April 2023), 106974. DOI:https://doi.org/10.1016/j.aap.2023.106974Google ScholarCross Ref
- HM Zhang, YS Guo, W Yuan, and KC Li. 2023. On the importance of working memory in the driving safety field: A systematic review. ACCIDENT ANALYSIS AND PREVENTION 187, (July 2023). DOI:https://doi.org/10.1016/j.aap.2023.107071Google ScholarCross Ref
- Yang Xing, Chen Lv, Huaji Wang, Hong Wang, Yunfeng Ai, Dongpu Cao, Efstathios Velenis, and Fei-Yue Wang. 2019. Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges. IEEE Trans. Veh. Technol. 68, 5 (May 2019), 4377–4390. DOI:https://doi.org/10.1109/TVT.2019.2903299Google ScholarCross Ref
- Mica Endsley. 1995. Measurement of Situation Awareness in Dynamic Systems. Hum. Factors 37, (March 1995), 65-. DOI:https://doi.org/10.1518/001872095779049499Google ScholarCross Ref
- Paul Salmon, Neville Stanton, Guy Walker, and Damian Green. 2006. Situation awareness measurement: A review of applicability for C4i environments. Appl. Ergon. 37, 2 (March 2006), 225–238. DOI:https://doi.org/10.1016/j.apergo.2005.02.001Google ScholarCross Ref
- David Sirkin, Nikolas Martelaro, Mishel Johns, and Wendy Ju. 2017. Toward Measurement of Situation Awareness in Autonomous Vehicles. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17), Association for Computing Machinery, New York, NY, USA, 405–415. DOI:https://doi.org/10.1145/3025453.3025822Google ScholarDigital Library
- Thanh Nguyen, Chee Peng Lim, Ngoc Duy Nguyen, Lee Gordon-Brown, and Saeid Nahavandi. 2019. A Review of Situation Awareness Assessment Approaches in Aviation Environments. IEEE Syst. J. 13, 3 (2019), 3590–3603. DOI:https://doi.org/10.1109/JSYST.2019.2918283Google ScholarCross Ref
- Mica R. Endsley. 2019. Situation Awareness in Future Autonomous Vehicles: Beware of the Unexpected. In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Springer International Publishing, Cham, 303–309.Google ScholarCross Ref
- Nade Liang, Jing Yang, Denny Yu, Kwaku O. Prakah-Asante, Reates Curry, Mike Blommer, Radhakrishnan Swaminathan, and Brandon J. Pitts. 2021. Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving. Accid. Anal. Prev. 157, (2021). DOI:https://doi.org/10.1016/j.aap.2021.106143Google ScholarCross Ref
- Yang Xing, Chen Lv, Dongpu Cao, and Peng Hang. 2021. Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving. Transp. Res. Pt. C-Emerg. Technol. 128, (2021). DOI:https://doi.org/10.1016/j.trc.2021.103199Google ScholarCross Ref
- Uijong Ju, Lewis L. Chuang, and Christian Wallraven. 2022. Acoustic Cues Increase Situational Awareness in Accident Situations: A VR Car-Driving Study. IEEE Trans. Intell. Transp. Syst 23, 4 (April 2022), 3281–3291. DOI:https://doi.org/10.1109/TITS.2020.3035374Google ScholarDigital Library
- K. Anders Ericsson and Herbert A. Simon. 1980. Verbal reports as data. Psychol. Rev. 87, 3 (1980), 215–251. DOI:https://doi.org/10.1037/0033-295X.87.3.215Google ScholarCross Ref
- Rich C. McIlroy, Katherine L. Plant, and Neville A. Stanton. 2021. Thinking aloud on the road: Thematic differences in the experiences of drivers, cyclists, and motorcyclists. Transp. Res. Part F: Traffic Psychol. Behav. 83, (November 2021), 192–209. DOI:https://doi.org/10.1016/j.trf.2021.09.014Google ScholarCross Ref
- Miles Thomas, Natassia Goode, Eryn Grant, Natalie Z. Taylor, and Paul M. Salmon. 2015. Can we talk about Speed? The Effect of Verbal Protocols on Driver Speed and Perceived Workload. In 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, 2629–2634. DOI:https://doi.org/10.1016/j.promfg.2015.07.608Google ScholarCross Ref
- Youngjun Cho. 2021. Rethinking Eye-Blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), Association for Computing Machinery, New York, NY, USA. DOI:https://doi.org/10.1145/3411764.3445577Google ScholarDigital Library
- Jork Stapel, Mounir EI Hassnaoui, and Riender Happee. 2022. Measuring Driver Perception: Combining Eye-Tracking and Automated Road Scene Perception. Hum. Factors 64, 4 (2022), 714–731. DOI:https://doi.org/10.1177/0018720820959958Google ScholarCross Ref
- Feng Zhou, X. Jessie Yang, and Joost C. F. De Winter. 2022. Using Eye-Tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving. IEEE Trans. Intell. Transp. Syst. 23, 3 (2022), 2284–2295. DOI:https://doi.org/10.1109/TITS.2021.3069776Google ScholarCross Ref
- David P. Broadbent, Giorgia D'Innocenzo, Toby J. Ellmers, Justin Parsler, Andre J. Szameitat, and Daniel T. Bishop. 2023. Cognitive load, working memory capacity and driving performance: A preliminary fNIRS and eye tracking study. Transp. Res. Pt. F-Traffic Psychol. Behav. 92, (January 2023), 121–132. DOI:https://doi.org/10.1016/j.trf.2022.11.013Google ScholarCross Ref
- Jin Chen, Dihua Sun, and Min Zhao. 2023. Human-Like Control for Automated Vehicles and Avoiding “Vehicle Face-Off” in Unprotected Left Turn Scenarios. IEEE Trans. Intell. Transp. Syst. 24, 2 (February 2023), 1609–1618. DOI:https://doi.org/10.1109/TITS.2022.3222492Google ScholarCross Ref
- Steve Lee, Ramin Arvin, and Asad J. Khattak. 2023. Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis. Accid. Anal. Prev. 181, (March 2023), 106932. DOI:https://doi.org/10.1016/j.aap.2022.106932Google ScholarCross Ref
- Junda Zhai, Guangquan Lu, Facheng Chen, and Miaomiao Liu. 2022. Effects of Information from Connected Vehicles and Infrastructure on Driving Behavior of Young Drivers at Urban Intersections. J. Transp. Saf. Secur. 40, 01 (2022), 126–1134. DOI:https://doi.org/10. 3963/j. jssn. 1674-4861. 2022. 01. 015Google Scholar
- Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Advances in Psychology, Peter A. Hancock and Najmedin Meshkati (eds.). North-Holland, 139–183. DOI:https://doi.org/10.1016/S0166-4115(08)62386-9Google ScholarCross Ref
- Glen Jocher, Kurtis Nishimura, T. Mineeva, and ET AL. 2022. ultralytics/yolov5. Retrieved November 28, 2022 from https://github.com/ultralytics/yolov5Google Scholar
- Mathias Benedek and Christian Kaernbach. 2010. A continuous measure of phasic electrodermal activity. J Neurosci Methods 190, 1 (June 2010), 80–91. DOI:https://doi.org/10.1016/j.jneumeth.2010.04.028Google ScholarCross Ref
Index Terms
- A Visual Protocol Analysis Method for Collecting Driving Cognitions in Multi-user Driving Simulator Studies
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