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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Lim, K. L., Whitehead, J., Jia, D., Zheng, Z., 2021. State of data platforms for connected vehicles and infrastructures. Commun Transport Res, 1, 100013.
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.
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.
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.
Maltz, M., Shinar, D., 2004. Imperfect in-vehicle collision avoidance warning systems can aid drivers. Hum Factors, 46, 357–366.
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.
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.
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.
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.
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.
Shinar, D., Schechtman, E., 2002. Headway feedback improves intervehicular distance: A field study. Hum Factors, 44, 474–481.
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.
Sullivan, J. M., Tsimhoni, O., Bogard, S., 2008. Warning reliability and driver performance in naturalistic driving. Hum Factors, 50, 845–852.
Taieb-Maimon, M., Shinar, D., 2001. Minimum and comfortable driving headways: Reality versus perception. Hum Factors, 43, 159–172.
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.
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.
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.
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.
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.
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.
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