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 Journal of Intelligent and Connected Vehicles Article
PDF (2.6 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
Research Article | Open Access

Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test

Kasi Nayana Badweeti1, Vinayak Devendra Malaghan1,2, Digvijay Sampatrao Pawar1( ), Said Easa3
Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Medak, 502285, India
Department of Civil Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382355, India
Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON, M5B 2K3, Canada
Show Author Information

Abstract

A large number of reported road collisions are caused by driver inattention, and inappropriate driving behaviour. This study investigated the effectiveness and acceptance of Advanced Driving Assistance Systems (ADAS) for driver age groups, gender, occupation (professional/non-professional), and road type (expressway, urban roads, and semi-urban road) based on the Field Operational Test (FOT). The ADAS is provided with assistance features, such as Lane Departure Warning (LDW), Forward Collision Warning (FCW), and Traffic Speed Recognition Warning (TSRW). In total, the FOT involved 30 participants who drove the test vehicle twice (once in the stealth phase and once in the active phase). The FOT included three sections: expressway (20.60 km), urban road (7.2 km), and semi-urban road (13.35 km). A questionnaire was used to determine user acceptance of the ADAS technology. In addition, parametric and non-parametric statistical tests were carried out to determine ADAS's significant effects. The FOT results showed statistically significant differences in the LDW’s acceptance and effectiveness for gender, age group, occupation, and road type before and after exposure to ADAS. Male participants showed significant lateral behavior improvement compared to female participants. Old-aged drivers scored the highest acceptance score for the technology compared to middle and young-aged drivers. The subjective ratings ranked the assistance features in descending order as TSRW, LDW, and FCW. This study’s findings can support policy development and induce trust in the public for the technology adoption to improve road traffic safety.

Keywords

driving assistance systemforward collisionlane departure warningtraffic speed recognitionroad safety

References

[1]

Adell, E., Várhelyi, A., Fontana, M. D., 2011. The effects of a driver assistance system for safe speed and safe distance–A real-life field study. Transp Res C Emerg Technol, 19, 145–155.
Crossref Google Scholar
[2]
Alkim, T. P., Bootsma, G., Hoogendoorn, S. P., 2007. Field operational test “the assisted driver”. In: IEEE Intelligent Vehicles Symposium. June 13-15, 2007, Istanbul, Turkey. IEEE, 1198–1203.
Crossref
[3]

Atombo, C., Wu, C., Zhong, M., Zhang, H., 2016. Investigating the motivational factors influencing drivers intentions to unsafe driving behaviours: Speeding and overtaking violations. Transp Res F Traffic Psychol Behav, 43, 104–121.
Crossref Google Scholar
[4]

Ben-Yaacov, A., Maltz, M., Shinar, D., 2002. Effects of an in-vehicle collision avoidance warning system on short- and long-term driving performance. Hum Factors, 44, 335–342.
Crossref Google Scholar
[5]

Birrell, S. A., Fowkes, M., Jennings, P. A., 2014. Effect of using an In-vehicle smart driving aid on real-world driver performance. IEEE Trans Intell Transp Syst, 15, 1801–1810.
Crossref Google Scholar
[6]

Blaschke, C., Breyer, F., Färber, B., Freyer, J., Limbacher, R., 2009. Driver distraction based lane-keeping assistance. Transp Res F Traffic Psychol Behav, 12, 288–299.
Crossref Google Scholar
[7]

Chen, H., Cao, L., Logan, D. B., 2011. Investigation into the effect of an intersection crash warning system on driving performance in a simulator. Traffic Inj Prev, 12, 529–537.
Crossref Google Scholar
[8]

Davis, F. D., Bagozzi, R. P., Warshaw, P. R., 1989. User acceptance of computer technology: A comparison of two theoretical models. Manag Sci, 35, 982–1003.
Crossref Google Scholar
[9]

Feng, Z., Lei, Y., Liu, H., Kumfer, W. J., Zhang, W., Wang, K. et al., 2016. Driving anger in China: A case study on professional drivers. Transp Res F Traffic Psychol Behav, 42, 255–266.
Crossref Google Scholar
[10]
Gaspar, J., Brown, T., Schwarz, C., Chrysler, S., Gunaratne, P., 2016. Driver behavior in forward collision and lane departure scenarios. In: SAE Technical Paper Series. 400 Commonwealth Drive, No. 2016-01-1455.
Crossref
[11]
Green, M., Senders, J., 2004. Human error in road accidents. Visual Expert. https://www.visualexpert.com/Resources/roadaccidents.html
[12]
Knipling, R. R., Wang, J. S., Yin, H. M., 1993. Rear-end Crashes: Problem Size Assessment and Statistical Description. Technical Report, No. HS-807 994.
[13]

Kusano, K. D., Chen, R., Montgomery, J., Gabler, H. C., 2015. Population distributions of time to collision at brake application during car following from naturalistic driving data. J Saf Res, 54, 95.e29–104.
Crossref Google Scholar
[14]

LeBlanc, D. J., Bao, S., Sayer, J. R., Bogard, S., 2013. Longitudinal driving behavior with integrated crash-warning system. Transportation Research Record, 2365, 17–21.
Crossref Google Scholar
[15]

Li, G., Cheng, B., 2015. Field operational test of advanced driver assistance systems in typical Chinese Road conditions: The influence of driver gender, age and aggression. Int J Automot Technol, 16, 739–750.
Crossref Google Scholar
[16]

Lim, K. L., Whitehead, J., Jia, D., Zheng, Z., 2021. State of data platforms for connected vehicles and infrastructures. Commun Transport Res, 1, 100013.
Crossref Google Scholar
[17]

Lyu, N., Deng, C., Xie, L., Wu, C., Duan, Z., 2019. A field operational test in China: Exploring the effect of an advanced driver assistance system on driving performance and braking behavior. Transp Res F Traffic Psychol Behav, 65, 730–747.
Crossref Google Scholar
[18]

Malaghan, V., Pawar, D. S., 2022. A short-term naturalistic driving study onD predicting comfort thresholds for horizontal curves on two-lane rural highways. J Transp Eng A Syst, 148: 04022045.
Crossref Google Scholar
[19]

Malaghan, V., Pawar, D. S., Dia, H., 2020. Modeling operating speed using continuous speed profiles on two-lane rural highways in India. J Transp Eng A Syst, 146: 04020124.
Crossref Google Scholar
[20]

Maltz, M., Shinar, D., 2004. Imperfect in-vehicle collision avoidance warning systems can aid drivers. Hum Factors, 46, 357–366.
Crossref Google Scholar
[21]

Montgomery, J., Kusano, K. D., Gabler, H. C., 2014. Age and gender differences in time to collision at braking from the 100-car naturalistic driving study. Traffic Inj Prev, 15, S15–S20.
Crossref Google Scholar
[22]
Ministry of Road Transport and Highways (MORTH), 2019. Road accidents in India 2019. New Delhi, India: MORTH.
[23]
Mosedale, J., Purdy, A., Clarkson, E., 2004. Contributory factors to road accidents. https://trid.trb.org/view/864416
[24]
Najm, W., Stearns, M., Howarth, H., Koopmann, J., Hitz, J. S., 2006. Evaluation of an automotive rear-end collision avoidance system (No. FHWA-JPO-06-055). Department of Transportation, National Highway Traffic Safety Administration, Washington, DC, USA.
[25]
Oxley, P. R., Mitchell, C. G. B., 1995. Final report on elderly and disabled drivers’ information telematics (Project EDDIT). Commission of the European Communities DG XIII, R & D Programme Telematics Systems in the Area of Transport (DRIVE II), Brussels, Belgium.
[26]

Pan, C., Xu, J., Fu, J., 2021. Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation. J Intell Connect Veh, 4, 28–37.
Crossref Google Scholar
[27]

Rezaei, M., Yazdani, M., Jafari, M., Saadati, M., 2021. Gender differences in the use of ADAS technologies: A systematic review. Transp Res F Traffic Psychol Behav, 78, 1–15.
Crossref Google Scholar
[28]

Saito, Y., Itoh, M., Inagaki, T., 2016. Driver assistance system with a dual control scheme: Effectiveness of identifying driver drowsiness and preventing lane departure accidents. IEEE Trans Hum Mach Syst, 46, 660–671.
Crossref Google Scholar
[29]
Sayer, J. R., Bogard, S. E., Buonarosa, M. L., LeBlanc, D. J., Funkhouser, D. S., Bao, S., et al., 2011. Integrated vehicle-based safety systems light-vehicle field operational test, key findings report. The University of Michigan Transportation Research Institute, Ann Arbor, Michigan, USA.
Crossref
[30]

Scott, J. J., Gray, R., 2008. A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Hum Factors, 50, 264–275.
Crossref Google Scholar
[31]

Shinar, D., Schechtman, E., 2002. Headway feedback improves intervehicular distance: A field study. Hum Factors, 44, 474–481.
Crossref Google Scholar
[32]

Son, J., Park, M., Park, B. B., 2015. The effect of age, gender and roadway environment on the acceptance and effectiveness of Advanced Driver Assistance Systems. Transp Res F Traffic Psychol Behav, 31, 12–24.
Crossref Google Scholar
[33]
Stevens, S., 2012. The relationship between driver acceptance and system effectiveness in car-based collision warning systems: Evidence of an overreliance effect in older drivers? SAE Int J Passeng Cars-Electron Electr Syst, 5, 114–124.
Crossref
[34]

Sullivan, J. M., Tsimhoni, O., Bogard, S., 2008. Warning reliability and driver performance in naturalistic driving. Hum Factors, 50, 845–852.
Crossref Google Scholar
[35]

Taieb-Maimon, M., Shinar, D., 2001. Minimum and comfortable driving headways: Reality versus perception. Hum Factors, 43, 159–172.
Crossref Google Scholar
[36]

Van Der Laan, J. D., Heino, A., De Waard, D., 1997. A simple procedure for the assessment of acceptance of advanced transport telematics. Transp Res C Emerg Technol, 5, 1–10.
Crossref Google Scholar
[37]

Venkatesh, V., Morris, M. G., 2000. Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Q, 24, 115–139.
Crossref Google Scholar
[38]
Viborg, N., 1999. Older and younger drivers attitudes toward in-car ITS. Bulletin, 181, 3000.
[39]

Wang, J., Zhang, L., Zhang, D., Li, K., 2013. An adaptive longitudinal driving assistance system based on driver characteristics. IEEE Trans Intell Transp Syst, 14, 1–12.
Crossref Google Scholar
[40]
World Health Organization (WHO), 2018. Global status report on road safety. Geneva: World Health Organization.
[41]

Xu, Y., Ye, Z., Wang, C., 2021. Modeling commercial vehicle drivers’ acceptance of advanced driving assistance system (ADAS). J Intell Connect Veh, 4, 125–135.
Crossref Google Scholar
[42]

Yan, X., Wang, J., Wu, J., 2016. Effect of In-vehicle audio warning system on driver’s speed control performance in transition zones from rural areas to urban areas. Int J Environ Res Public Health, 13, 634.
Crossref Google Scholar
[43]
Yang, X., Kim, J. H., 2018. Acceptance and Effectiveness of Collision Avoidance System in Public Transportation. In: International Conference of Design, User Experience, and Usability, 424−434.
Crossref
[44]

Zhang, H., Wu, C., Yan, X., Qiu, T. Z., 2016. The effect of fatigue driving on car following behavior. Transp Res F Traffic Psychol Behav, 43, 80–89.
Crossref Google Scholar
Journal of Intelligent and Connected Vehicles
Volume 6 Issue 2,
June 2023
Pages 65-78
DOI: 10.26599/JICV.2023.9210005
Cite this article:
Badweeti KN, Malaghan VD, Pawar DS, et al. Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test. Journal of Intelligent and Connected Vehicles, 2023, 6(2): 65-78. https://doi.org/10.26599/JICV.2023.9210005

314

Views

13

Downloads

5

Crossref

5

Scopus

Altmetrics
Received: 12 December 2022
Revised: 09 January 2023
Accepted: 10 February 2023
Published: 11 May 2023
© The author(s) 2023.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return