پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 422 |
| تعداد مقالات | 5,535 |
| تعداد مشاهده مقاله | 6,413,867 |
| تعداد دریافت فایل اصل مقاله | 5,508,841 |
Traffic Lane Change Detection using mobile phone sensors and geolocation | ||
| AUT Journal of Civil Engineering | ||
| مقاله 2، دوره 7، شماره 1، 2023، صفحه 13-30 اصل مقاله (1.34 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2023.22422.5829 | ||
| نویسندگان | ||
| Masoud Ghasemi1؛ amir Golroo* 2؛ Mohammad Javad Amani2؛ Mehdi Rasti3 | ||
| 1Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran. | ||
| 2Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran. | ||
| چکیده | ||
| Driver behavior is a critical factor in traffic safety. Detecting abnormal driver behaviors through appropriate indicators and enforcing driving regulations will reduce high-risk driving behaviors and increase traffic safety. Detecting dangerous driver behavior is beneficial for developing warning systems and preventing accidents. Some high-risk driving behaviors, such as sudden lane changes, are dependent on determining the movement direction of the vehicle which has not received enough attention. The objective of this study is to determine the direction of movement of the vehicle and lane changes, using the sensors in the smartphone mounted on a vehicle. To achieve this goal, first, by using the Samsung Galaxy S6 smartphone and an accurate Global Positioning System (GPS), longitudinal and angular accelerometer data and GPS data are sampled as a dataset and combined by different types of neural networks. Then, combined data is fed into a suggested neural network, and lane changes are detected. Finally, the GPS data is used as the ground truth for the training of the neural network. If the GPS is not accessible, this neural network, just by receiving smartphone accelerometer data, can estimate the vehicle's direction of movement with an accuracy of 0.5 to 4.8 meters compared to the ground truth up to 8 seconds after the GPS is shut down. Using the vehicle travel path, an algorithm is proposed that can correctly detect the change of driving lane in the sample data set with 94% accuracy, 93.62% precision, 88.00% recall, and F1-score of 90.72% which are acceptable values. | ||
| کلیدواژه‌ها | ||
| Driver Behavior؛ Lane Change Detection؛ smartphone؛ Transportation Safety؛ Artificial Intelligence | ||
| مراجع | ||
|
[1] A. Bener, E. Yildirim, T. Özkan, T. Lajunen, Driver sleepiness, fatigue, careless behavior and risk of motor vehicle crash and injury: Population based case and control study, Journal of Traffic and Transportation engineering (English edition), 4(5) (2017) 496-502.
[2] S. Reinhold, C. Laesser, D. Bazzi, The intellectual structure of transportation management research: A review of the literature, in, University of St. Gallen St. Gallen, 2015.
[3] R.D. Guide, American Association of State Highway and Transportation Officials, Washington, DC, (1996).
[4] S. Al-Sultan, A.H. Al-Bayatti, H. Zedan, Context-aware driver behavior detection system in intelligent transportation systems, IEEE transactions on vehicular technology, 62(9) (2013) 4264-4275.
[5] E.E. Galarza, F.D. Egas, F.M. Silva, P.M. Velasco, E.D. Galarza, Real time driver drowsiness detection based on driver’s face image behavior using a system of human computer interaction implemented in a smartphone, in: Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), Springer, 2018, pp. 563-572.
[6] M. Shahverdy, M. Fathy, R. Berangi, M. Sabokrou, Driver behavior detection and classification using deep convolutional neural networks, Expert Systems with Applications, 149 (2020) 113240.
[7] J. Wang, K. Li, X.-Y. Lu, Effect of human factors on driver behavior, in: Advances in Intelligent Vehicles, Elsevier, 2014, pp. 111-157.
[8] Z. Xiao, F. Li, R. Wu, H. Jiang, Y. Hu, J. Ren, C. Cai, A. Iyengar, TrajData: On vehicle trajectory collection with commodity plug-and-play OBU devices, IEEE Internet of Things Journal, 7(9) (2020) 9066-9079.
[9] X. Liu, J. Biagioni, J. Eriksson, Y. Wang, G. Forman, Y. Zhu, Mining large-scale, sparse GPS traces for map inference: comparison of approaches, in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp. 669-677.
[10] S. Tanwar, S. Tyagi, S. Kumar, The role of internet of things and smart grid for the development of a smart city, in: Intelligent Communication and Computational Technologies: Proceedings of Internet of Things for Technological Development, IoT4TD 2017, Springer, 2018, pp. 23-33.
[11] V. Chauhan, M. Patel, S. Tanwar, S. Tyagi, N. Kumar, IoT Enabled real-Time urban transport management system, Computers & Electrical Engineering, 86 (2020) 106746.
[12] J. Masino, J. Thumm, M. Frey, F. Gauterin, Learning from the crowd: Road infrastructure monitoring system, Journal of Traffic and Transportation Engineering (English Edition), 4(5) (2017) 451-463.
[13] K.W. Wevers, M.L. Lu, R. van der Heijden Heijden, Technical feasibility of advanced driver assistance systems (ADAS) for road traffic safety, (2005).
[14] Q. Li, F. Qiao, X. Wang, L. Yu, Drivers' smart advisory system improves driving performance at STOP sign intersections, Journal of traffic and transportation engineering (English edition), 4(3) (2017) 262-271.
[15] D. Sunehra, K. Jhansi, Implementation of microcontroller based driver assistance and vehicle safety monitoring system, in: 2015 International Conference on Information Processing (ICIP), IEEE, 2015, pp. 423-428.
[16] A. Ziebinski, R. Cupek, D. Grzechca, L. Chruszczyk, Review of advanced driver assistance systems (ADAS), in: AIP Conference Proceedings, AIP Publishing, 2017.
[17] M.J. Carrillo, Robotic cars test platform for connected and automated vehicles, University of California, Irvine, 2015.
[18] A. Shaout, D. Colella, S. Awad, Advanced driver assistance systems-past, present and future, in: 2011 Seventh International Computer Engineering Conference (ICENCO'2011), IEEE, 2011, pp. 72-82.
[19] U. Hofmann, A. Rieder, E.D. Dickmanns, Radar and vision data fusion for hybrid adaptive cruise control on highways, Machine Vision and Applications, 14 (2003) 42-49.
[20] A. Kesting, M. Treiber, M. Schönhof, D. Helbing, Adaptive cruise control design for active congestion avoidance, Transportation Research Part C: Emerging Technologies, 16(6) (2008) 668-683.
[21] S. Cafiso, A. Di Graziano, G. Pappalardo, In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian, Journal of traffic and transportation engineering (English edition), 4(1) (2017) 3-13.
[22] C. Ilas, Electronic sensing technologies for autonomous ground vehicles: A review, in: 2013 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE), IEEE, 2013, pp. 1-6.
[23] B.-G. Lee, W.-Y. Chung, A smartphone-based driver safety monitoring system using data fusion, Sensors, 12(12) (2012) 17536-17552.
[24] J. Carmona, F. García, D. Martín, A. de la Escalera, J.M. Armingol, Data fusion for driver behaviour analysis, Sensors, 15(10) (2015) 25968-25991.
[25] J. Yu, Z. Chen, Y. Zhu, Y. Chen, L. Kong, M. Li, Fine-grained abnormal driving behaviors detection and identification with smartphones, IEEE transactions on mobile computing, 16(8) (2016) 2198-2212.
[26] S. Siuhi, J. Mwakalonge, Opportunities and challenges of smart mobile applications in transportation, Journal of traffic and transportation engineering (english edition), 3(6) (2016) 582-592.
[27] G.M. Fitch, K. Grove, R.J. Hanowski, M.A. Perez, Compensatory behavior of drivers when conversing on a cell phone: Investigation with naturalistic driving data, Transportation research record, 2434(1) (2014) 1-8.
[28] M.M. Haque, S. Washington, The impact of mobile phone distraction on the braking behaviour of young drivers: a hazard-based duration model, Transportation research part C: emerging technologies, 50 (2015) 13-27.
[29] W.J. Horrey, M.F. Lesch, M.J. Dainoff, M.M. Robertson, Y.I. Noy, On-board safety monitoring systems for driving: review, knowledge gaps, and framework, Journal of safety research, 43(1) (2012) 49-58.
[30] W. Consiglio, P. Driscoll, M. Witte, W.P. Berg, Effect of cellular telephone conversations and other potential interference on reaction time in a braking response, Accident Analysis & Prevention, 35(4) (2003) 495-500.
[31] S.G. Klauer, F. Guo, B.G. Simons-Morton, M.C. Ouimet, S.E. Lee, T.A. Dingus, Distracted driving and risk of road crashes among novice and experienced drivers, New England journal of medicine, 370(1) (2014) 54-59.
[32] T.A. Dingus, F. Guo, S. Lee, J.F. Antin, M. Perez, M. Buchanan-King, J. Hankey, Driver crash risk factors and prevalence evaluation using naturalistic driving data, Proceedings of the National Academy of Sciences, 113(10) (2016) 2636-2641.
[33] V. Havyarimana, D. Hanyurwimfura, P. Nsengiyumva, Z. Xiao, A novel hybrid approach based-SRG model for vehicle position prediction in multi-GPS outage conditions, Information Fusion, 41 (2018) 1-8.
[34] H. Ahmed, I. Ullah, U. Khan, M.B. Qureshi, S. Manzoor, N. Muhammad, M.U. Shahid Khan, R. Nawaz, Adaptive filtering on GPS-aided MEMS-IMU for optimal estimation of ground vehicle trajectory, Sensors, 19(24) (2019) 5357.
[35] T. Driessen, L.L.B. Prasad, P. Bazilinskyy, J. de Winter, Identifying Lane Changes Automatically using the GPS Sensors of Portable Devices, Human Factors in Transportation, 60 (2022) 50.
[36] S. Stieger, D. Lewetz, U.-D. Reips, Can smartphones be used to bring computer-based tasks from the lab to the field? A mobile experience-sampling method study about the pace of life, Behavior Research Methods, 50 (2018) 2267-2275.
[37] A. Fani, H. Naseri, Travel Time Modelling of Urban Roads By Application of Coyote Optimization-based Machine Learning Method, Amirkabir Journal of Civil Engineering, 53(9) (2021) 3649-3664.
[38] S. Ranjbar, F. Moghadas Nejad, H. Zakeri, Asphalt Pavement Bleeding Evaluation using Deep Learning and Wavelet Transform, Amirkabir Journal of Civil Engineering, 53(11) (2022) 4577-4598.
[39] P.S. Bokare, A.K. Maurya, Acceleration-deceleration behaviour of various vehicle types, Transportation research procedia, 25 (2017) 4733-4749. | ||
|
آمار تعداد مشاهده مقاله: 436 تعداد دریافت فایل اصل مقاله: 442 |
||