UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author?s predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future. ? 2021. Ahmed Al-Hussein et al. |
References |
Alkinani, M. H., Khan, W. Z., & Arshad, Q. (2020). Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges. IEEE Access, 8, 105008-105030. doi:10.1109/ACCESS.2020.2999829 Alsrehin, N. O., Klaib, A. F., & Magableh, A. (2019). Intelligent transportation and control systems using data mining and machine learning techniques: A comprehensive study. IEEE Access, 7, 49830-49857. doi:10.1109/ACCESS.2019.2909114 Andria, G., Attivissimo, F., Di Nisio, A., Lanzolla, A. M. L., & Pellegrino, A. (2016). Development of an automotive data acquisition platform for analysis of driving behavior. Measurement: Journal of the International Measurement Confederation, 93, 278-287. doi:10.1016/j.measurement.2016.07.035 Asaithambi, G., Kanagaraj, V., & Toledo, T. (2016). Driving behaviors: Models and challenges for non-lane based mixed traffic. Transportation in Developing Economies, 2(2) Retrieved from www.scopus.com Babić, D., Fiolić, M., Babić, D., & Gates, T. (2020). Road markings and their impact on driver behaviour and road safety: A systematic review of current findings. Journal of Advanced Transportation, 2020 doi:10.1155/2020/7843743 Bastos, J. T., Dos Santos, P. A. B., Amancio, E. C., Gadda, T. M. C., Ramalho, J. A., King, M. J., & Oviedo-Trespalacios, O. (2020). Naturalistic driving study in brazil: An analysis of mobile phone use behavior while driving. International Journal of Environmental Research and Public Health, 17(17), 1-14. doi:10.3390/ijerph17176412 Bella, F., & Nobili, F. (2020). Driver-pedestrian interaction under legal and illegal pedestrian crossings. Paper presented at the Transportation Research Procedia, , 45 451-458. doi:10.1016/j.trpro.2020.03.038 Retrieved from www.scopus.com Bender, A., Agamennoni, G., Ward, J. R., Worrall, S., & Nebot, E. M. (2015). An unsupervised approach for inferring driver behavior from naturalistic driving data. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3325-3336. doi:10.1109/TITS.2015.2449837 Biçaksiz, P., & Özkan, T. (2016). Impulsivity and driver behaviors, offences and accident involvement: A systematic review. Transportation Research Part F: Traffic Psychology and Behaviour, 38, 194-223. doi:10.1016/j.trf.2015.06.001 Bichicchi, A., Belaroussi, R., Simone, A., Vignali, V., Lantieri, C., & Li, X. (2020). Analysis of road-user interaction by extraction of driver behavior features using deep learning. IEEE Access, 8, 19638-19645. doi:10.1109/ACCESS.2020.2965940 Bifulco, G. N., Galante, F., Pariota, L., Russo Spena, M., & Del Gais, P. (2014). Data collection for traffic and drivers' behaviour studies: A large-scale survey. Procedia Soc.Behav.Sci., 111, 721-730. Retrieved from www.scopus.com Butakov, V. A., & Ioannou, P. A. (2014). Driver/vehicle response diagnostic system for the vehicle-following case. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1947-1957. doi:10.1109/TITS.2014.2305735 Büyükyildiz, G., Pion, O., Hildebrandt, C., Sedlmayr, M., Henze, R., & Küçükay, F. (2017). Identification of the driving style for the adaptation of assistance systems. International Journal of Vehicle Autonomous Systems, 13(3), 244-260. doi:10.1504/IJVAS.2017.083515 Cai, H., Hu, Z., Chen, Z., & Zhu, D. (2018). A driving fingerprint map method of driving characteristic representation for driver identification. IEEE Access, 6, 71012-71019. doi:10.1109/ACCESS.2018.2881722 Carmona, J., García, F., Martín, D., de la Escalera, A., & Armingol, J. M. (2015). Data fusion for driver behaviour analysis. Sensors (Switzerland), 15(10), 25968-25991. doi:10.3390/s151025968 Cerni, G., & Bassani, M. (2017). Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour. Transportation Research Part C: Emerging Technologies, 77, 1-15. doi:10.1016/j.trc.2017.01.012 Chen, C., Zhao, X., Zhang, Y., Rong, J., & Liu, X. (2019). A graphical modeling method for individual driving behavior and its application in driving safety analysis using GPS data. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 118-134. doi:10.1016/j.trf.2019.03.017 Chen, R., Kusano, K. D., & Gabler, H. C. (2015). Driver behavior during overtaking maneuvers from the 100-car naturalistic driving study. Traffic Injury Prevention, 16, S176-S181. doi:10.1080/15389588.2015.1057281 Dabbour, E. (2015). Design gap acceptance for right-turning vehicles based on vehicle acceleration capabilities doi:10.3141/2521-02 Retrieved from www.scopus.com Dahl, J., De Campos, G. R., Olsson, C., & Fredriksson, J. (2019). Collision avoidance: A literature review on threat-assessment techniques. IEEE Transactions on Intelligent Vehicles, 4(1), 101-113. doi:10.1109/TIV.2018.2886682 Das, S., Budhkar, A., Maurya, A. K., & Maji, A. (2019). Multivariate analysis on dynamic car-following data of non-lane-based traffic environments. Transportation in Developing Economies, 5(2), 1-13. Retrieved from www.scopus.com Demir, B., Demir, S., & Özkan, T. (2016). A contextual model of driving anger: A meta-analysis. Transportation Research Part F: Traffic Psychology and Behaviour, 42, 332-349. doi:10.1016/j.trf.2016.09.020 Derbel, O., & Landry, R. J. (2018). Belief and fuzzy theories for driving behavior assessment in case of accident scenarios. International Journal of Automotive Technology, 19(1), 167-177. doi:10.1007/s12239-018-0016-1 Errampalli, M., Mallela, S. S., & Chandra, S. (2020). Calibration of car-following model for indian traffic conditions. Paper presented at the Transportation Research Procedia, , 48 829-839. doi:10.1016/j.trpro.2020.08.091 Retrieved from www.scopus.com Farah, H., Musicant, O., Shimshoni, Y., Toledo, T., Grimberg, E., Omer, H., & Lotan, T. (2014). Can providing feedback on driving behavior and training on parental vigilant care affect male teen drivers and their parents? Accident Analysis and Prevention, 69, 62-70. doi:10.1016/j.aap.2013.11.005 Fleming, J. M., Allison, C. K., Yan, X., Lot, R., & Stanton, N. A. (2019). Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data. Safety Science, 119, 76-83. doi:10.1016/j.ssci.2018.08.023 Foo, K. Y. (2015). Effects of familial climate on the adolescents’ driving habits: A recent literature. International Journal of Injury Control and Safety Promotion, 22(2), 127-135. doi:10.1080/17457300.2013.855795 Fridman, L., Brown, D. E., Angell, W., Abdić, I., Reimer, B., & Noh, H. Y. (2016). Automated synchronization of driving data using vibration and steering events. Pattern Recognition Letters, 75, 9-15. doi:10.1016/j.patrec.2016.02.011 Gadepally, V., Krishnamurthy, A., & Ozguner, U. (2014). A framework for estimating driver decisions near intersections. IEEE Transactions on Intelligent Transportation Systems, 15(2), 637-646. doi:10.1109/TITS.2013.2285159 Galarza, M., & Paradells, J. (2019). Improving road safety and user experience by employing dynamic in-vehicle information systems. IET Intelligent Transport Systems, 13(4), 738-744. doi:10.1049/iet-its.2018.5022 Gao, J., Murphey, Y. L., & Zhu, H. (2018). Detection of lane-changing behavior using collaborative representation classifier-based sensor fusion. SAE International Journal of Transportation Safety, 6(2), 147-162. doi:10.4271/09-06-02-0010 Gao, J., Murphey, Y. L., & Zhu, H. (2019). Personalized detection of lane changing behavior using multisensor data fusion. Computing, 101(12), 1837-1860. doi:10.1007/s00607-019-00712-9 Gunduz, G., Yaman, C., Peker, A. U., & Acarman, T. (2018). Prediction of risk generated by different driving patterns and their conflict redistribution. IEEE Transactions on Intelligent Vehicles, 3(1), 71-80. doi:10.1109/TIV.2017.2788203 Guo, F. (2019). Statistical methods for naturalistic driving studies. Annual Review of Statistics and its Application, 6, 309-328. doi:10.1146/annurev-statistics-030718-105153 Hassan, N., Zamzuri, H., Wahid, N., Zulkepli, K. A., & Azmi, M. Z. (2017). Driver's steering behaviour identification and modelling in near rear-end collision. Telkomnika (Telecommunication Computing Electronics and Control), 15(2), 861-868. doi:10.12928/TELKOMNIKA.v15i2.6133 Heesen, M., Dziennus, M., Hesse, T., Schieben, A., Brunken, C., Löper, C., . . . Baumann, M. (2015). Interaction design of automatic steering for collision avoidance: Challenges and potentials of driver decoupling. IET Intelligent Transport Systems, 9(1), 95-104. doi:10.1049/iet-its.2013.0119 Hildebrandt, C., Schmidt, M., Sedlmayr, M., Pion, O., Büyükyildiz, G., & Küçükay, F. (2015). Intuitive steering assistance in critical understeer situations. Traffic Injury Prevention, 16(5), 484-490. doi:10.1080/15389588.2014.969805 Hill, C., Elefteriadou, L., & Kondyli, A. (2015). Exploratory analysis of lane changing on freeways based on driver behavior. Journal of Transportation Engineering, 141(4) doi:10.1061/(ASCE)TE.1943-5436.0000758 Hong, S., Lu, J., Panigrahi, S. R., Scott, J., & Filev, D. P. (2019). An interacting multiple-model-based algorithm for driver behavior characterization using handling risk. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4308-4317. doi:10.1109/TITS.2016.2633254 Hong, S., Min, B., Doi, S., & Suzuki, K. (2016). Approaching and stopping behaviors to the intersections of aged drivers compared with young drivers. International Journal of Industrial Ergonomics, 54, 32-41. doi:10.1016/j.ergon.2015.12.002 Jermakian, J. S., Bao, S., Buonarosa, M. L., Sayer, J. R., & Farmer, C. M. (2017). Effects of an integrated collision warning system on teenage driver behavior. Journal of Safety Research, 61, 65-75. doi:10.1016/j.jsr.2017.02.013 Joubert, J. W., de Beer, D., & de Koker, N. (2016). Combining accelerometer data and contextual variables to evaluate the risk of driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 41, 80-96. doi:10.1016/j.trf.2016.06.006 Katzourakis, D. I., Abbink, D. A., Velenis, E., Holweg, E., & Happee, R. (2014). Driver's arms' time-variant neuromuscular admittance during real car test-track driving. IEEE Transactions on Instrumentation and Measurement, 63(1), 221-230. doi:10.1109/TIM.2013.2277610 Khandakar, A., Chowdhury, M. E. H., Ahmed, R., Dhib, A., Mohammed, M., Al-Emadi, N. A. M. A., & Michelson, D. (2019). Portable system for monitoring and controlling driver behavior and the use of a mobile phone while driving. Sensors (Switzerland), 19(7) doi:10.3390/s19071563 Kim, B., & Baek, Y. (2020). Sensor-based extraction approaches of in-vehicle information for driver behavior analysis. Sensors (Switzerland), 20(18), 1-20. doi:10.3390/s20185197 Koesdwiady, A., Soua, R., Karray, F., & Kamel, M. S. (2017). Recent trends in driver safety monitoring systems: State of the art and challenges. IEEE Transactions on Vehicular Technology, 66(6), 4550-4563. doi:10.1109/TVT.2016.2631604 Krasniuk, S., Classen, S., Monahan, M., Danter, T., He, W., Rosehart, H., & Morrow, S. A. (2019). A strategic driving maneuver that predicts on-road outcomes in adults with multiple sclerosis. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 147-156. doi:10.1016/j.trf.2018.10.014 Lee, J., & Jang, K. (2019). A framework for evaluating aggressive driving behaviors based on in-vehicle driving records. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 610-619. doi:10.1016/j.trf.2017.11.021 Li, A., Jiang, H., Zhou, J., & Zhou, X. (2019). Learning human-like trajectory planning on urban two-lane curved roads from experienced drivers. IEEE Access, 7, 65828-65838. doi:10.1109/ACCESS.2019.2918728 Li, Y., Miyajima, C., Kitaoka, N., & Takeda, K. (2015). Evaluation method for aggressiveness of driving behavior using drive recorders. IEEJ Journal of Industry Applications, 4(1), 59-66. doi:10.1541/ieejjia.4.59 Li, Z., Zhang, K., Chen, B., Dong, Y., & Zhang, L. (2019). Driver identification in intelligent vehicle systems using machine learning algorithms. IET Intelligent Transport Systems, 13(1), 40-47. doi:10.1049/iet-its.2017.0254 Liu, H., Li, Y., Hong, R., Li, Z., Li, M., Pan, W., . . . He, H. (2020). Knowledge graph analysis and visualization of research trends on driver behavior. Journal of Intelligent and Fuzzy Systems, 38(1), 495-511. doi:10.3233/JIFS-179424 Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., & Bando, T. (2017). Visualization of driving behavior based on hidden feature extraction by using deep learning. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2477-2489. doi:10.1109/TITS.2017.2649541 Liu, Y., & Hansen, J. (2019). Analysis of driving performance based on driver experience and vehicle familiarity: A UTDrive/Mobile-UTDrive app study. SAE International Journal of Transportation Safety, 7(2) doi:10.4271/09-07-02-0010 Llorca, C., Moreno, A. T., & Garcia, A. (2016). Modelling vehicles acceleration during overtaking manoeuvres. IET Intelligent Transport Systems, 10(3), 206-215. doi:10.1049/iet-its.2015.0035 Lubbe, N., & Rosén, E. (2014). Pedestrian crossing situations: Quantification of comfort boundaries to guide intervention timing. Accident Analysis and Prevention, 71, 261-266. doi:10.1016/j.aap.2014.05.029 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. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 730-747. doi:10.1016/j.trf.2018.01.003 Maljković, B., & Cvitanić, D. (2016). Evaluation of design consistency on horizontal curves for two-lane state roads in terms of vehicle path radius and speed. Baltic Journal of Road and Bridge Engineering, 11(2), 127-135. doi:10.3846/bjrbe.2016.15 Marina Martinez, C., Heucke, M., Wang, F. -., Gao, B., & Cao, D. (2018). Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey. IEEE Transactions on Intelligent Transportation Systems, 19(3), 666-676. doi:10.1109/TITS.2017.2706978 Massaro, E., Ahn, C., Ratti, C., Santi, P., Stahlmann, R., Lamprecht, A., . . . Huber, M. (2017). The car as an ambient sensing platform. Proceedings of the IEEE, 105(1), 3-7. doi:10.1109/JPROC.2016.2634938 McLaurin, E. J., Lee, J. D., McDonald, A. D., Aksan, N., Dawson, J., Tippin, J., & Rizzo, M. (2018). Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 25-38. doi:10.1016/j.trf.2018.05.019 Merickel, J., High, R., Dawson, J., & Rizzo, M. (2019). Real-world risk exposure in older drivers with cognitive and visual dysfunction. Traffic Injury Prevention, 20(sup2), S110-S115. doi:10.1080/15389588.2019.1688794 Montella, A., Pariota, L., Galante, F., Imbriani, L. L., & Mauriello, F. (2014). Prediction of drivers' speed behavior on rural motorways based on an instrumented vehicle study doi:10.3141/2434-07 Retrieved from www.scopus.com Mukhtar, A., Xia, L., & Tang, T. B. (2015). Vehicle detection techniques for collision avoidance systems: A review. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2318-2338. doi:10.1109/TITS.2015.2409109 Munigety, C. R., & Mathew, T. V. (2016). Towards behavioral modeling of drivers in mixed traffic conditions. Transportation in Developing Economies, 2(1) Retrieved from www.scopus.com Nguyen, H., Kieu, L. -., Wen, T., & Cai, C. (2018). Deep learning methods in transportation domain: A review. IET Intelligent Transport Systems, 12(9), 998-1004. doi:10.1049/iet-its.2018.0064 Oviedo-Trespalacios, O., Truelove, V., Watson, B., & Hinton, J. A. (2019). The impact of road advertising signs on driver behaviour and implications for road safety: A critical systematic review. Transportation Research Part A: Policy and Practice, 122, 85-98. doi:10.1016/j.tra.2019.01.012 Pamuła, T. (2016). Neural networks in transportation research-recent applications. Transport Problems, 11(2), 27-36. doi:10.20858/tp.2016.11.2.3 Park, S., Park, J., Seo, B., & Kim, B. (2016). Development of reproducible test vehicle and evaluation scenario for driver pedal behavior analysis in unexpected emergency situations. International Journal of Control and Automation, 9(11), 385-396. doi:10.14257/ijca.2016.9.11.33 Peng, L., Sotelo, M. A., He, Y., Ai, Y., & Li, Z. (2019). Rough set based method for vehicle collision risk assessment through inferring driver's braking actions in near-crash situations. IEEE Intelligent Transportation Systems Magazine, 11(2), 54-69. doi:10.1109/MITS.2019.2903539 Qi, G., Du, Y., Wu, J., & Xu, M. (2015). Leveraging longitudinal driving behaviour data with data mining techniques for driving style analysis. IET Intelligent Transport Systems, 9(8), 792-801. doi:10.1049/iet-its.2014.0139 Rodriguez Gonzalez, A. B., Wilby, M. R., Vinagre Diaz, J. J., & Sanchez Avila, C. (2014). Modeling and detecting aggressiveness from driving signals. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1419-1428. doi:10.1109/TITS.2013.2297057 Sagberg, F., Selpi, Bianchi Piccinini, G. F., & Engström, J. (2015). A review of research on driving styles and road safety. Human Factors, 57(7), 1248-1275. doi:10.1177/0018720815591313 Saifuzzaman, M., & Zheng, Z. (2014). Incorporating human-factors in car-following models: A review of recent developments and research needs. Transportation Research Part C: Emerging Technologies, 48, 379-403. doi:10.1016/j.trc.2014.09.008 Salmon, P. M., Goode, N., Spiertz, A., Thomas, M., Grant, E., & Clacy, A. (2017). Is it really good to talk? testing the impact of providing concurrent verbal protocols on driving performance. Ergonomics, 60(6), 770-779. doi:10.1080/00140139.2016.1214752 Satzoda, R. K., & Trivedi, M. M. (2015). Drive analysis using vehicle dynamics and vision-based lane semantics. IEEE Transactions on Intelligent Transportation Systems, 16(1), 9-18. doi:10.1109/TITS.2014.2331259 Schakel, W. J., Gorter, C. M., De Winter, J. C. F., & Van Arem, B. (2017). Driving characteristics and adaptive cruise control-A naturalistic driving study. IEEE Intelligent Transportation Systems Magazine, 9(2), 17-24. doi:10.1109/MITS.2017.2666582 Schorr, J., Hamdar, S. H., & Silverstein, C. (2017). Measuring the safety impact of road infrastructure systems on driver behavior: Vehicle instrumentation and real world driving experiment. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 21(5), 364-374. doi:10.1080/15472450.2016.1198699 Shinar, D., & Gurion, B. (2019). Crash causes, countermeasures, and safety policy implications. Accident Analysis and Prevention, 125, 224-231. doi:10.1016/j.aap.2019.02.015 Shirazi, M. S., & Morris, B. T. (2017). Looking at intersections: A survey of intersection monitoring, behavior and safety analysis of recent studies. IEEE Transactions on Intelligent Transportation Systems, 18(1), 4-24. doi:10.1109/TITS.2016.2568920 |
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