Abstract
We have designed a smart wearable device to protect actively pedestrian from impact of vehicle. This device consists of several modules, including radar sensor, transmission module, alarm module and intelligent security program module. In the dominant program module, the safety intelligent algorithm based on fuzzy comprehensive evaluation and BP neural network is proposed. From the perspective of a pedestrian, the moving data sensed by radar, combining with multiple effects of local surroundings, people and vehicles, road and transportation situation, weather, physiological and psychological situation of the pedestrian, are used as the data source for the algorithm. Based on the weight of the index determined by BP neural network, we use the fuzzy comprehensive evaluation to calculate the vehicle risk index. The smart wearable device can effectively predict and warn the situation of vehicles impacting pedestrians. The simulation has confirmed the accuracy of the prewarning algorithm under various conditions.
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Aidman E, Chadunow C, Johnson K, Reece J (2015) Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance. Accid Anal Prev 81:8–13. https://doi.org/10.1016/j.aap.2015.03.041
Dharia A, Adeli H (2003) Neural network model for rapid forecasting of freeway link travel time. Eng Appl Artif Intell 16(7):607–613. https://doi.org/10.1016/j.engappai.2003.09.011
Ding CX, Wang WH, Wang X, Baumann M (2013) A neural network model for driver’s lane-changing trajectory prediction in urban traffic flow. Math Probl EnG. https://doi.org/10.1155/2013/967358
Dombi J (1990) Membership function as an evaluation. Fuzzy Sets Syst 35(1):1–21. https://doi.org/10.1016/0165-0114(90)90014-W
Elvik R (2019) A comprehensive and unified framework for analysing the effects on injuries of measures influencing speed. Accid Anal Prev 125:63–69. https://doi.org/10.1016/j.aap.2019.01.033
Ghasemzadeh A, Hammit BE, Ahmed MM, Eldeeb H (2019) Unlocking new potentials of SHRP2 naturalistic driving study data: complementary methodologies to supplement real-time weather conditions and effective frameworks for data reduction and preparation. Saf Sci. https://doi.org/10.1016/j.ssci.2019.01.006
Girbés V, Armesto L, Dols J, Tornero J (2017) An active safety system for low-speed bus braking assistance. IEEE Trans Intell Transp Syst 18(2):377–387. https://doi.org/10.1109/TITS.2016.2573921
Haynes R, Lake IR, Kingham S, Sabel CE, Pearce J, Barnett R (2008) The influence of road curvature on fatal crashes in New Zealand. Accid Anal Prev 40(3):843–850. https://doi.org/10.1016/j.aap.2007.09.013
Hu Y, Zhang Y, Shelton KS (2018) Where are the dangerous intersections for pedestrians and cyclists: a colocation-based approach. Transp Res Part C Emerg Technol 95:431–441. https://doi.org/10.1016/j.trc.2018.07.030
Huang HT, Zhou JY, Di Q, Zhou JW, Li JW (2019) Robust neural network-based tracking control and stabilization of a wheeled mobile robot with input saturation. Int J Robust Nonlinear Control 29(2):375–392. https://doi.org/10.1002/rnc.4396
Hummer JE, Rasdorf W, Findley DJ, Zegeer CV, Sundstrom CA (2010) Curve collisions: road and collision characteristics and countermeasures. J Transp Saf Secur 2(3):203–220. https://doi.org/10.1080/19439961003734880
Jeong H, Liu Y (2019) Effects of non-driving-related-task modality and road geometry on eye movements, lane-keeping performance, and workload while driving. Transp Rese Part F Traffic Psychol Behav 60:157–171. https://doi.org/10.1016/j.trf.2018.10.015
Jiang Y, Zhang J, Wang Y, Wang W (2018) Drivers’ behavioral responses to driving risk diagnosis and real-time warning information provision on expressways: a smartphone app–based driving experiment. J Transp Saf Secur. https://doi.org/10.1080/19439962.2018.1483988
Joubert JW, de Beer D, de Koker N (2016) Combining accelerometer data and contextual variables to evaluate the risk of driver behaviour. Transp Res Part F Traffic Psychol Behav 41:80–96. https://doi.org/10.1016/j.trf.2016.06.006
Kanarachos S, Christopoulos S-RG, Chroneos A (2018) Smartphones as an integrated platform for monitoring driver behaviour: the role of sensor fusion and connectivity. Transp Res Part C Emerg Technol 95:867–882. https://doi.org/10.1016/j.trc.2018.03.023
Katsis CD, Goletsis Y, Rigas G, Fotiadis DI (2011) A wearable system for the affective monitoring of car racing drivers during simulated conditions. Transp Res Part C Emerg Technol 19(3):541–551. https://doi.org/10.1016/j.trc.2010.09.004
Kim S, Ulfarsson GF (2018) Traffic safety in an aging society: analysis of older pedestrian crashes. J Transp Saf Secur. https://doi.org/10.1080/19439962.2018.1430087
Kishida M, Ohguchi K, Shono M (2015) 79 GHz-band high-resolution millimeter-wave radar. Fujitsu Sci Tech J 51(4):55–59
Kusano KD, Chen R, Montgomery J, Gabler HC (2015) Population distributions of time to collision at brake application during car following from naturalistic driving data. J Saf Res 54:95.e29-104. https://doi.org/10.1016/j.jsr.2015.06.011
Leekwijck WV, Kerre EE (1999) Defuzzification: criteria and classification. Fuzzy Sets Syst 108(2):159–178. https://doi.org/10.1016/S0165-0114(97)00337-0
Lefèvre S, Vasquez D, Laugier C (2014) A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J 1(1):1. https://doi.org/10.1186/s40648-014-0001-z
Lenard J, Welsh R, Danton R (2018) Time-to-collision analysis of pedestrian and pedal-cycle accidents for the development of autonomous emergency braking systems. Accid Anal Prev 115:128–136. https://doi.org/10.1016/j.aap.2018.02.028
Li D, Ranjitkar P, Zhao Y, Yi H, Rashidi S (2017a) Analyzing pedestrian crash injury severity under different weather conditions. Traffic Inj Prev 18(4):427–430. https://doi.org/10.1080/15389588.2016.1207762
Li G, Yang J, Simms C (2017b) Safer passenger car front shapes for pedestrians: a computational approach to reduce overall pedestrian injury risk in realistic impact scenarios. Accid Anal Prev 100:97–110. https://doi.org/10.1016/j.aap.2017.01.006
Liu MY, Shi J (2019) A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model. J Intell Transp Syst 23(4):309–318. https://doi.org/10.1080/15472450.2018.1462176
Liu Y-C, Tung Y-C (2014) Risk analysis of pedestrians’ road-crossing decisions: effects of age, time gap, time of day, and vehicle speed. Saf Sci 63:77–82. https://doi.org/10.1016/j.ssci.2013.11.002
Masoumi A, Shojaeefard MH, Najibi A (2011) Comparison of steel, aluminum and composite bonnet in terms of pedestrian head impact. Saf Sci 49(10):1371–1380. https://doi.org/10.1016/j.ssci.2011.05.008
McDonald AD, Lee JD, Schwarz C, Brown TL (2018) A contextual and temporal algorithm for driver drowsiness detection. Accid Anal Prev 113:25–37. https://doi.org/10.1016/j.aap.2018.01.005
Morando A, Victor T, Dozza M (2016) Drivers anticipate lead-vehicle conflicts during automated longitudinal control: sensory cues capture driver attention and promote appropriate and timely responses. Accid Anal Prev 97:206–219. https://doi.org/10.1016/j.aap.2016.08.025
Oikawa S, Matsui Y, Doi T, Sakurai T (2016) Relation between vehicle travel velocity and pedestrian injury risk in different age groups for the design of a pedestrian detection system. Saf Sci 82:361–367. https://doi.org/10.1016/j.ssci.2015.10.003
Schrum KD, De Albuquerque FDB, Sicking DL, Falle RK, Reid JD (2014) Correlation between crash severity and embankment geometry. J Transp SaF Secur 6(4):321–334. https://doi.org/10.1080/19439962.2013.877548
Shi J, Tao L, Li X, Xiao Y, Atchley P (2014) A survey of taxi drivers’ aberrant driving behavior in Beijing. J Transp SaF Secur 6(1):34–43. https://doi.org/10.1080/19439962.2013.799624
Strawderman L, King K, Carruth D (2018) Improving safety of vulnerable road users: effectiveness of environment and in-vehicle warning systems at intermodal interchanges. J Transp SaF Secur 10(3):177–192. https://doi.org/10.1080/19439962.2016.1237598
Sun J, Zhu S, Lv Z (2003) Study on the mechanical impedance of the vibration isolator. Ship Ocean Eng 4:25–29
Toran Pour A, Moridpour S, Tay R, Rajabifard A (2018) Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes. Traffic Inj Prev 19(1):81–87. https://doi.org/10.1080/15389588.2017.1341630
Tulu GS, Washington S, Haque MM, King MJ (2017) Injury severity of pedestrians involved in road traffic crashes in Addis Ababa, Ethiopia. Journal of Transportation Safety & Security 9(sup1):47–66. https://doi.org/10.1080/19439962.2016.1199622
Tzung-Pei H, Jyh-Bin C (1999) Finding relevant attributes and membership functions. Fuzzy Sets Syst 103(3):389–404. https://doi.org/10.1016/S0165-0114(97)00187-5
Varga D, Szirányi T (2017) Robust real-time pedestrian detection in surveillance videos. J Ambient Intell Hum Comput 8(1):79–85. https://doi.org/10.1007/s12652-016-0369-0
Wang C, Quddus MA, Ison SG (2013) The effect of traffic and road characteristics on road safety: a review and future research direction. Saf Sci 57:264–275. https://doi.org/10.1016/j.ssci.2013.02.012
Wang J, Wu J, Zheng X, Ni D, Li K (2016) Driving safety field theory modeling and its application in pre-collision warning system. Transp Res Part C Emerg Technol 72:306–324. https://doi.org/10.1016/j.trc.2016.10.003
Wu J, Xu H, Zheng Y, Tian Z (2018) A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data. Accid Anal Prev 121:238–249. https://doi.org/10.1016/j.aap.2018.09.001
Xiao Z, Wang Z, Wan Q, Qin Y, Zeng J, Fan S (2018) An intelligent wearable device for actively detecting and identifying dangers and warning. Chinese patent NO. 201810227610.3., filed March 20, 2018, and issued July 23, 2018
Yang YF, Zhang MH, Dai YW (2019) A fuzzy comprehensive CS-SVR Model-based health status evaluation of radar. PLoS One 14(3):20. https://doi.org/10.1371/journal.pone.0213833
Zaki MH, Sayed T (2014) Using automated walking gait analysis for the identification of pedestrian attributes. Transp Res Part C Emerg Technol 48:16–36. https://doi.org/10.1016/j.trc.2014.08.004
Zhao Y, Ito D, Mizuno K (2019) AEB effectiveness evaluation based on car-to-cyclist accident reconstructions using video of drive recorder. Traffic Injury Prev 20(1):100–106. https://doi.org/10.1080/15389588.2018.1533247
Acknowledgements
The present work was supported by Xichang Mine (open-air) intrinsically safe mine research project (Granted no. scaqjg stp 2016011).
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Wang, Z., Wan, Q., Qin, Y. et al. Intelligent algorithm in a smart wearable device for predicting and alerting in the danger of vehicle collision. J Ambient Intell Human Comput 11, 3841–3852 (2020). https://doi.org/10.1007/s12652-019-01609-3
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DOI: https://doi.org/10.1007/s12652-019-01609-3