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Journal of Intelligent and Connected Vehicles 2021, 4(3): 125-135 https://doi.org/10.1108/JICV-07-2021-0011
Research paper | Open Access | Issue | Published: 23 November 2021

Modeling commercial vehicle drivers' acceptance of advanced driving assistance system (ADAS)

Show Author's Information Yueru Xu1( ),  Zhirui Ye2,  Chao Wang2
Intelligent Transportation System Research Center, Southeast University, Nanjing, China
School of Transportation, Southeast University, Nanjing, China
Keywords:
Driving behavior, Random forests, Commercial vehicles, Advanced driving assistance system
Cite this article:
Xu Y, Ye Z, Wang C. Modeling commercial vehicle drivers' acceptance of advanced driving assistance system (ADAS). Journal of Intelligent and Connected Vehicles, 2021, 4(3): 125-135. https://doi.org/10.1108/JICV-07-2021-0011
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Abstract Full text About this article
Purpose

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

Design/methodology/approach

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

Findings

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Originality/value

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.


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Abstract
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About this article

Modeling commercial vehicle drivers' acceptance of advanced driving assistance system (ADAS)

Show Author's information Yueru Xu1( ), Zhirui Ye2, Chao Wang2
Intelligent Transportation System Research Center, Southeast University, Nanjing, China
School of Transportation, Southeast University, Nanjing, China

Abstract

Purpose

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

Design/methodology/approach

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

Findings

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Originality/value

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

Keywords: Driving behavior, Random forests, Commercial vehicles, Advanced driving assistance system

References(42)

Birrell, S.A., Fowkes, M. and Jennings, P.A. (2014), "Effect of using an in-vehicle smart driving aid on real-world driver performance", IEEE Transactions on Intelligent Transportation Systems, Vol. 15 No. 4, pp. 1801-1810.
DOI Google Scholar

Breiman, L. (1996), "Bagging predictors", Machine Learning, Vol. 24 No. 2, pp. 123-140.
DOI Google Scholar

Breiman, L. (2001), "Random forests", Machine Learning, Vol. 45 No. 1, pp. 5-32.
DOI Google Scholar

Cao, H., Zhang, Z., Song, X., Wang, H., Li, M., Zhao, S. and Wang, J. (2020), "An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data", Journal of Intelligent and Connected Vehicles, Vol. 3 No. 1, pp. 1-16.
DOI Google Scholar

Chang, C.Y. and Chou, Y.R. (2009), "Development of fuzzy-based bus rear-end collision warning thresholds using a driving simulator", IEEE Transactions on Intelligent Transportation Systems, Vol. 10 No. 2, pp. 360-365.
DOI Google Scholar

Cicchino, J.B. (2018), "Effects of lane departure warning on police-reported crash rates", Journal of Safety Research, Vol. 66, pp. 61-70.
DOI Google Scholar

Gao, K., Yang, Y., Sun, L. and Qu, X. (2020), "Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips", Transportation Research Part F: traffic Psychology and Behaviour, Vol. 71, pp. 272-287.
DOI Google Scholar

Gao, K., Yang, Y., Li, A., Li, J. and Yu, B. (2021), "Quantifying economic benefits from free-floating bike-sharing systems: a trip-level inference approach and city-scale analysis", Transportation Research Part A: Policy and Practice, Vol. 144, pp. 89-103.
DOI Google Scholar

Gaspar, J.G. and Brown, T.L. (2020), "Matters of state: examining the effectiveness of lane departure warnings as a function of driver distraction", Transportation Research Part F: traffic Psychology and Behaviour, Vol. 71, pp. 1-7.
DOI Google Scholar

Hirose, T., Oguchi, Y. and Sawada, T. (2004), "Framework of tailormade driving support systems and neural network driver model", IATSS Research, Vol. 28 No. 1, pp. 108-114.
DOI Google Scholar

Kuang, Y., Qu, X. and Wang, S. (2015), "A tree-structured crash surrogate measure for freeways", Accident Analysis & Prevention, Vol. 77, pp. 137-148.
DOI Google Scholar

James, D.J.G., Boehringer, F., Burnham, K.J. and Copp, D.G. (2004), "Adaptive driver model using a neural network", Artificial Life and Robotics, Vol. 7 No. 4, pp. 170-176.
DOI Google Scholar

Kusano, K.D. and Gabler, H.C. (2015), "Comparison of expected crash and injury reduction from production forward collision and lane departure warning systems", Traffic Injury Prevention, Vol. 16 No. sup2, pp. 109-114.
DOI Google Scholar
LeBlanc, D. (2006), "Road departure crash warning system field operational test: methodology and results. Volume 1: technical report", University of Michigan, Ann Arbor.

Li, G., Li, S.E. and Cheng, B. (2015), "Field operational test of advanced driver assistance systems in typical Chinese road conditions: the influence of driver gender, age and aggression", International Journal of Automotive Technology, Vol. 16 No. 5, pp. 739-750.
DOI Google Scholar

Li, X., Medal, H. and Qu, X. (2019), "Connected infrastructure location design under additive service utilities", Transportation Research Part B: Methodological, Vol. 120, pp. 99-124.
DOI Google Scholar

Liu, J., Khattak, A., Han, L. and Yuan, Q. (2020), "How much information is lost when sampling driving behavior data? Indicators to quantify the extent of information loss", Journal of Intelligent and Connected Vehicles, Vol. 3 No. 1, pp. 17-29.
DOI Google Scholar

Lunetta, K.L., Hayward, L.B., Segal, J. and Van Eerdewegh, P. (2004), "Screening large-scale association study data: exploiting interactions using random forests", BMC Genetics, Vol. 5 No. 1, pp. 1-13.
DOI Google Scholar

Meng, Q. and Qu, X. (2012), "Estimation of rear-end vehicle crash frequencies in urban road tunnels", Accident Analysis & Prevention, Vol. 48, pp. 254-263.
DOI Google Scholar

Muhrer, E., Reinprecht, K. and Vollrath, M. (2012), "Driving with a partially autonomous forward collision warning system: how do drivers react?", Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 54 No. 5, pp. 698-708.
DOI Google Scholar

Ngxande, M., Tapamo, J.R. and Burke, M. (2017), "Driver drowsiness detection using behavioral measures and machine learning techniques: a review of state-of-art techniques", Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), pp. 156-161.
DOI Google Scholar

Pan, C., Xu, J. and Fu, J. (2021), "Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation", Journal of Intelligent and Connected Vehicles, Vol. 4 No. 1, pp. 28-37.
DOI Google Scholar

Puente Guillen, P. and Gohl, I. (2019), "Forward collision warning based on a driver model to increase drivers’ acceptance", Traffic Injury Prevention, Vol. 20 No. 1, pp. 21-26.
DOI Google Scholar

Qu, X., Yu, Y., Zhou, M., Lin, C.T. and Wang, X. (2020), "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach", Applied Energy, Vol. 257.
DOI Google Scholar

Quinlan, J.R. (1987), "Simplifying decision trees", International Journal of Man-Machine Studies, Vol. 27 No. 3, pp. 221-234.
DOI Google Scholar

Rajaonah, B., Tricot, N., Anceaux, F. and Millot, P. (2008), "The role of intervening variables in driver–ACC cooperation", International Journal of Human-Computer Studies, Vol. 66 No. 3, pp. 185-197.
DOI Google Scholar

Saito, Y., Itoh, M. and Inagaki, T. (2016), "Driver assistance system with a dual control scheme: effectiveness of identifying driver drowsiness and preventing lane departure accidents", IEEE Transactions on Human-Machine Systems, Vol. 46 No. 5, pp. 660-671.
DOI Google Scholar
Scanlon, J.M., Kusano, K.D., Sherony, R. and Gabler, H.C. (2015), "Potential safety benefits of lane departure warning and prevention systems in the US vehicle fleet", International Conference on Enhanced Safety of Vehicles (ESV).

Shaheen, S.A. and Niemeier, D.A. (2001), "Integrating vehicle design and human factors: minimizing elderly driving constraints", Transportation Research Part C: Emerging Technologies, Vol. 9 No. 3, pp. 155-174.
DOI Google Scholar

Shi, X., Wang, Z., Li, X. and Pei, M. (2021), "The effect of ride experience on changing opinions toward autonomous vehicle safety", Communications in Transportation Research, Vol. 1, p. 100003.
DOI Google Scholar

Shinar, D. and Schechtman, E. (2002), "Headway feedback improves intervehicular distance: a field study", Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 44 No. 3, pp. 474-481.
DOI Google Scholar
Treat, J.R. Tumbas, N.S. McDonald, S.T. Shinar, D. Hume, R.D. Mayer, R.E. … and Castellan, N.J. (1979) "Tri-level study of the causes of traffic accidents: final report", Executive summary, Institute for Research in Public Safety. Indiana University, Bloomington.

Vahidi, A. and Eskandarian, A. (2003), "Research advances in intelligent collision avoidance and adaptive cruise control", IEEE Transactions on Intelligent Transportation Systems, Vol. 4 No. 3, pp. 143-153.
DOI Google Scholar

Wang, J., Yu, C., Li, S.E. and Wang, L. (2015), "A forward collision warning algorithm with adaptation to driver behaviors", IEEE Transactions on Intelligent Transportation Systems, Vol. 17 No. 4, pp. 1157-1167.
DOI Google Scholar

Wang, K., Zhang, W., Feng, Z. and Wang, C. (2020), "Research on the classification for road traffic visibility based on the characteristics of driving behaviour – a driving simulator experiment", Journal of Intelligent and Connected Vehicles, Vol. 3 No. 1, pp. 30-36.
DOI Google Scholar

Wege, C., Will, S. and Victor, T. (2013), "Eye movement and brake reactions to real world brake-capacity forward collision warnings – a naturalistic driving study", Accident Analysis & Prevention, Vol. 58, pp. 259-270.
DOI Google Scholar
World Health Organization (2018), "Global status report on road safety 2018: summary", World Health Organization, WHO/NMH/NVI/18.20.

Wu, J., Kulcsár, B., Ahn, S. and Qu, X. (2020), "Emergency vehicle lane pre-clearing: from microscopic cooperation to routing decision making", Transportation Research Part B: Methodological, Vol. 141, pp. 223-239.
DOI Google Scholar

Xu, Y., Zheng, Y. and Yang, Y. (2021), "On the movement simulations of electric vehicles: a behavioral model-based approach", Applied Energy, Vol. 283.
DOI Google Scholar

Yang, X. and Kim, J.H. (2018), "Acceptance and effectiveness of collision avoidance system in public transportation", International Conference of Design, User Experience, and Usability, pp. 424-434.
DOI Google Scholar

Yao, Y., Zhao, X., Du, H., Zhang, Y. and Rong, J. (2018), "Classification of distracted driving based on visual features and behavior data using a random forest method", Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672 No. 45, pp. 210-221.
DOI Google Scholar

Zhao, X., Li, X., Chen, Y., Li, H. and Ding, Y. (2021), "Evaluation of fog warning system on driving under heavy fog condition based on driving simulator", Journal of Intelligent and Connected Vehicles, Vol. 4 No. 2, pp. 41-51.
DOI Google Scholar
Publication history
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Publication history

Received: 20 July 2021
Revised: 17 October 2021
Accepted: 19 October 2021
Published: 23 November 2021
Issue date: December 2021

Copyright

© 2021 Yueru Xu, Zhirui Ye and Chao Wang. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

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This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

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