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Published in: Journal of Reliable Intelligent Environments 4/2021

25-01-2021 | Original Article

Internet of things based intelligent accident avoidance system for adverse weather and road conditions

Authors: J. Andrew Onesimu, Abhishikt Kadam, K. Martin Sagayam, Ahmed A. Elngar

Published in: Journal of Reliable Intelligent Environments | Issue 4/2021

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Abstract

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application.

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Literature
3.
go back to reference Bucsuházy K, Matuchová E, Zůvala R, Moravcová P, Kostíková M, Mikulec R (2020) Human factors contributing to the road traffic accident occurrence. Trans Res Proc 45:555–561 Bucsuházy K, Matuchová E, Zůvala R, Moravcová P, Kostíková M, Mikulec R (2020) Human factors contributing to the road traffic accident occurrence. Trans Res Proc 45:555–561
4.
go back to reference Corno F, Guercio E, De Russis L, Gargiulo E (2015) Designing for user confidence in intelligent environments. J Reliab Intell Environ 1(1):11–21CrossRef Corno F, Guercio E, De Russis L, Gargiulo E (2015) Designing for user confidence in intelligent environments. J Reliab Intell Environ 1(1):11–21CrossRef
5.
go back to reference Schmidtke HR (2018) A survey on verification strategies for intelligent transportation systems. J Reliab Intell Environ 4(4):211–224CrossRef Schmidtke HR (2018) A survey on verification strategies for intelligent transportation systems. J Reliab Intell Environ 4(4):211–224CrossRef
6.
go back to reference Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y (2019) An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw 90:1–16CrossRef Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y (2019) An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw 90:1–16CrossRef
7.
go back to reference Butt TA, Iqbal R, Shah SC, Umar T (2018) Social internet of vehicles: architecture and enabling technologies. Comput Electr Eng 69:68–84CrossRef Butt TA, Iqbal R, Shah SC, Umar T (2018) Social internet of vehicles: architecture and enabling technologies. Comput Electr Eng 69:68–84CrossRef
8.
go back to reference Chowdhury DN, Agarwal N, Laha AB, Mukherjee A (2018) A vehicle-to-vehicle communication system using Iot approach. Electron Commun Aerosp Technol ICECA 1:915–919 Chowdhury DN, Agarwal N, Laha AB, Mukherjee A (2018) A vehicle-to-vehicle communication system using Iot approach. Electron Commun Aerosp Technol ICECA 1:915–919
9.
go back to reference Gipps PG (1981) A behavioural car-following model for computer simulation. Transp Res Part B Methodol 15(2):105–111CrossRef Gipps PG (1981) A behavioural car-following model for computer simulation. Transp Res Part B Methodol 15(2):105–111CrossRef
10.
go back to reference Ge HX, Dai SQ, Dong LY (2006) An extended car-following model based on intelligent transportation system application. Phys A Stat Mech its Appl 365(2):543–548CrossRef Ge HX, Dai SQ, Dong LY (2006) An extended car-following model based on intelligent transportation system application. Phys A Stat Mech its Appl 365(2):543–548CrossRef
11.
go back to reference Askari H, Khajepour A, Khamesee MB, Wang ZL (2019) Embedded self-powered sensing systems for smart vehicles and intelligent transportation. Nano Energy 66:104103CrossRef Askari H, Khajepour A, Khamesee MB, Wang ZL (2019) Embedded self-powered sensing systems for smart vehicles and intelligent transportation. Nano Energy 66:104103CrossRef
12.
go back to reference Cao BG (2020) A car-following dynamic model with headway memory and evolution trend. Phys A Stat Mech Appl 539:122903MathSciNetCrossRef Cao BG (2020) A car-following dynamic model with headway memory and evolution trend. Phys A Stat Mech Appl 539:122903MathSciNetCrossRef
13.
go back to reference S. Tsugawa, T. Yatabe, T. Hirose, and S. Matsumoto, “An automobile with artificial intelligence,” in Proceedings of the 6th international joint conference on Artificial intelligence-Volume 2, 1979, pp. 893–895. S. Tsugawa, T. Yatabe, T. Hirose, and S. Matsumoto, “An automobile with artificial intelligence,” in Proceedings of the 6th international joint conference on Artificial intelligence-Volume 2, 1979, pp. 893–895.
14.
go back to reference Star SL (1989) The structure of Ill-structured solutions: boundary objects and heterogeneous distributed problem solving. Distributed artificial intelligence. Elsevier, London, pp 37–54 Star SL (1989) The structure of Ill-structured solutions: boundary objects and heterogeneous distributed problem solving. Distributed artificial intelligence. Elsevier, London, pp 37–54
15.
go back to reference Lloyd S, Mohseni M, Rebentrost P (2013) Quantum algorithms for supervised and unsupervised machine learning. arXiv Preprint arxiv3:13070411 Lloyd S, Mohseni M, Rebentrost P (2013) Quantum algorithms for supervised and unsupervised machine learning. arXiv Preprint arxiv3:13070411
16.
go back to reference Chui M, Löffler M, Roberts R (2010) The internet of things. McKinsey Q 2(2010):1–9 Chui M, Löffler M, Roberts R (2010) The internet of things. McKinsey Q 2(2010):1–9
17.
go back to reference Sadiku MNO, Tembely M, Musa SM (2018) Internet of vehicles: an introduction. Int J Adv Res Comput Sci Softw Eng 8(1):11CrossRef Sadiku MNO, Tembely M, Musa SM (2018) Internet of vehicles: an introduction. Int J Adv Res Comput Sci Softw Eng 8(1):11CrossRef
18.
go back to reference Nikhat I, Shilpa M (2016) Road accidents: overview of its causes, avoidance scheme and a new proposed technique for avoidance. IEEE 3805:497–499 Nikhat I, Shilpa M (2016) Road accidents: overview of its causes, avoidance scheme and a new proposed technique for avoidance. IEEE 3805:497–499
19.
go back to reference Jagannathan R, Petrovic S, Powell G, Roberts M (2013) Predicting road accidents based on current and historical spatio-temporal traffic flow data. Lect Notes Comput Sci including Subser Lect Notes Artif Intell Lect Notes Bioinformatics 8197:83–97 Jagannathan R, Petrovic S, Powell G, Roberts M (2013) Predicting road accidents based on current and historical spatio-temporal traffic flow data. Lect Notes Comput Sci including Subser Lect Notes Artif Intell Lect Notes Bioinformatics 8197:83–97
20.
go back to reference N. Kattukkaran, A. George, and T. P. M. Haridas, “Intelligent accident detection and alert system for emergency medical assistance,” in 2017 International Conference on Computer Communication and Informatics, ICCCI 2017, 2017. N. Kattukkaran, A. George, and T. P. M. Haridas, “Intelligent accident detection and alert system for emergency medical assistance,” in 2017 International Conference on Computer Communication and Informatics, ICCCI 2017, 2017.
21.
go back to reference Hjelkrem OA, Ryeng EO (2017) Driver behaviour data linked with vehicle, weather, road surface, and daylight data. Data Br 10:511–514CrossRef Hjelkrem OA, Ryeng EO (2017) Driver behaviour data linked with vehicle, weather, road surface, and daylight data. Data Br 10:511–514CrossRef
22.
go back to reference Kadam A, Andrew J, Martin Sagayam K, Hien DT (2020) Vehicle automation and car-following models for accident avoidance. Prz Elektrotechniczny 96(1):118–123 Kadam A, Andrew J, Martin Sagayam K, Hien DT (2020) Vehicle automation and car-following models for accident avoidance. Prz Elektrotechniczny 96(1):118–123
23.
go back to reference Karaduman M, Eren H (2017) Smart driving in smart city. Int Istanbul Smart Grids Cities Congr Fair 2:115–119 Karaduman M, Eren H (2017) Smart driving in smart city. Int Istanbul Smart Grids Cities Congr Fair 2:115–119
24.
go back to reference Singh SK (2017) Road traffic accidents in india: issues and challenges. Transp Res Procedia 25:4708–4719CrossRef Singh SK (2017) Road traffic accidents in india: issues and challenges. Transp Res Procedia 25:4708–4719CrossRef
25.
go back to reference Nwizege KS, Bottero M, Mmeah S, Nwiwure ED (2014) Vehicles-to-infrastructure communication safety messaging in DSRC. Procedia Comput Sci 34:59–564CrossRef Nwizege KS, Bottero M, Mmeah S, Nwiwure ED (2014) Vehicles-to-infrastructure communication safety messaging in DSRC. Procedia Comput Sci 34:59–564CrossRef
26.
go back to reference Yang D, Zhu L, Liu Y, Wu D, Ran B (2019) A Novel car-following control model combining machine learning and kinematics models for automated vehicles. IEEE Trans Intell Transp Syst 20(6):1991–2000CrossRef Yang D, Zhu L, Liu Y, Wu D, Ran B (2019) A Novel car-following control model combining machine learning and kinematics models for automated vehicles. IEEE Trans Intell Transp Syst 20(6):1991–2000CrossRef
27.
go back to reference Andrew J, Karthikeyan J (2019) Privacy-preserving internet of things: techniques and applications. Int J Eng Adv Technol 8(6):3229–3234CrossRef Andrew J, Karthikeyan J (2019) Privacy-preserving internet of things: techniques and applications. Int J Eng Adv Technol 8(6):3229–3234CrossRef
28.
go back to reference Singh D, Singh M (2016) Internet of vehicles for smart and safe driving. Int Conf Connect Veh Expo ICCVE Proc 1:328–329 Singh D, Singh M (2016) Internet of vehicles for smart and safe driving. Int Conf Connect Veh Expo ICCVE Proc 1:328–329
29.
go back to reference Sarikan SS, Ozbayoglu AM (2018) Anomaly detection in vehicle traffic with image processing and machine learning. Procedia Comput Sci 140:64–69CrossRef Sarikan SS, Ozbayoglu AM (2018) Anomaly detection in vehicle traffic with image processing and machine learning. Procedia Comput Sci 140:64–69CrossRef
30.
go back to reference V. Nyamati, T. Chaudhuri, and K. Jayavel, “Intelligent collision avoidance and safety warning system for car driving,” Proc. 2017 Int. Conf. Intell. Comput. Control Syst. ICICCS 2017, vol. 2018-Janua, pp. 791–796, 2018. V. Nyamati, T. Chaudhuri, and K. Jayavel, “Intelligent collision avoidance and safety warning system for car driving,” Proc. 2017 Int. Conf. Intell. Comput. Control Syst. ICICCS 2017, vol. 2018-Janua, pp. 791–796, 2018.
31.
go back to reference Eshghi M, Schmidtke HR (2018) An approach for safer navigation under severe hurricane damage. J Reliab Intell Environ 4(3):161–185CrossRef Eshghi M, Schmidtke HR (2018) An approach for safer navigation under severe hurricane damage. J Reliab Intell Environ 4(3):161–185CrossRef
32.
go back to reference Kumar SAP, Madhumathi R, Chelliah PR, Tao L, Wang S (2018) A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. J Reliab Intell Environ 4(4):199–209CrossRef Kumar SAP, Madhumathi R, Chelliah PR, Tao L, Wang S (2018) A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. J Reliab Intell Environ 4(4):199–209CrossRef
33.
go back to reference Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222CrossRef Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222CrossRef
34.
go back to reference Jia D, Ngoduy D (2016) Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication. Transp Res Part B Methodol 90:172–191CrossRef Jia D, Ngoduy D (2016) Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication. Transp Res Part B Methodol 90:172–191CrossRef
35.
go back to reference Hecht-Nielsen R (1992) Theory of the backpropagation neural network in Neural networks for perception. Elsevier, London, pp 65–93CrossRef Hecht-Nielsen R (1992) Theory of the backpropagation neural network in Neural networks for perception. Elsevier, London, pp 65–93CrossRef
36.
go back to reference Chen C, Xiang H, Qiu T, Wang C, Zhou Y, Chang V (2018) A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles. J Parallel Distrib Comput 117:192–204CrossRef Chen C, Xiang H, Qiu T, Wang C, Zhou Y, Chang V (2018) A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles. J Parallel Distrib Comput 117:192–204CrossRef
37.
go back to reference Czubenko M, Kowalczuk Z, Ordys A (2015) Autonomous driver based on an intelligent system of decision-making. Cognit Comput 7(5):569–581CrossRef Czubenko M, Kowalczuk Z, Ordys A (2015) Autonomous driver based on an intelligent system of decision-making. Cognit Comput 7(5):569–581CrossRef
38.
go back to reference Guo Y, Sun Q, Fu R, Wang C (2019) Improved car-following strategy based on merging behavior prediction of adjacent vehicle from naturalistic driving data. IEEE Access 7:44258–44268CrossRef Guo Y, Sun Q, Fu R, Wang C (2019) Improved car-following strategy based on merging behavior prediction of adjacent vehicle from naturalistic driving data. IEEE Access 7:44258–44268CrossRef
39.
go back to reference Ateeq Alanezi M (2018) A proposed system for vehicle-to-vehicle communication: low cost and network free approach. Indian J Sci Technol 11(12):1–10CrossRef Ateeq Alanezi M (2018) A proposed system for vehicle-to-vehicle communication: low cost and network free approach. Indian J Sci Technol 11(12):1–10CrossRef
40.
go back to reference Hjelkrem OA, Ryeng EO (2016) Chosen risk level during car-following in adverse weather conditions. Accid Anal Prev 95:227–235CrossRef Hjelkrem OA, Ryeng EO (2016) Chosen risk level during car-following in adverse weather conditions. Accid Anal Prev 95:227–235CrossRef
41.
go back to reference Cheng L, Zhang X, Shen J (2019) Road surface condition classification using deep learning. J Vis Commun Image Represent 64:102638CrossRef Cheng L, Zhang X, Shen J (2019) Road surface condition classification using deep learning. J Vis Commun Image Represent 64:102638CrossRef
42.
go back to reference Mondal P, Sharma N, Kumar A, Bhangale UD, Tyagi D, Singh R (2011) Effect of rainfall and wet road condition on road crashes: A critical analysis. SAE Tech Paper 10:123 Mondal P, Sharma N, Kumar A, Bhangale UD, Tyagi D, Singh R (2011) Effect of rainfall and wet road condition on road crashes: A critical analysis. SAE Tech Paper 10:123
43.
go back to reference Zhao YQ, Li HQ, Lin F, Wang J, Ji XW (2017) Estimation of road friction coefficient in different road conditions based on vehicle braking dynamics. Chinese J Mech Eng 30(4):982–990CrossRef Zhao YQ, Li HQ, Lin F, Wang J, Ji XW (2017) Estimation of road friction coefficient in different road conditions based on vehicle braking dynamics. Chinese J Mech Eng 30(4):982–990CrossRef
44.
go back to reference Xu D, Zhao H, Guillemard F, Geronimi S, Aioun F (2019) Aware of scene vehicles - probabilistic modeling of car-following behaviors in real-world traffic. IEEE Trans Intell Transp Syst 20(6):2136–2148CrossRef Xu D, Zhao H, Guillemard F, Geronimi S, Aioun F (2019) Aware of scene vehicles - probabilistic modeling of car-following behaviors in real-world traffic. IEEE Trans Intell Transp Syst 20(6):2136–2148CrossRef
45.
go back to reference Galanis I, Gurunathan P, Burkard D, Anagnostopoulos I (2018) Weather-based road condition estimation in the era of Internet-of-Vehicles (IoV). Proc - IEEE Int Symp Circuits Syst 10:19–16 Galanis I, Gurunathan P, Burkard D, Anagnostopoulos I (2018) Weather-based road condition estimation in the era of Internet-of-Vehicles (IoV). Proc - IEEE Int Symp Circuits Syst 10:19–16
46.
go back to reference Zhai X, Huang H, Sze NN, Song Z, Hon KK (2019) Diagnostic analysis of the effects of weather condition on pedestrian crash severity. Accid Anal Prev 122:318–324CrossRef Zhai X, Huang H, Sze NN, Song Z, Hon KK (2019) Diagnostic analysis of the effects of weather condition on pedestrian crash severity. Accid Anal Prev 122:318–324CrossRef
47.
go back to reference Wang Y, Liang L, Evans L (2017) Fatal crashes involving large numbers of vehicles and weather. J Safety Res 63:1–7CrossRef Wang Y, Liang L, Evans L (2017) Fatal crashes involving large numbers of vehicles and weather. J Safety Res 63:1–7CrossRef
48.
go back to reference Punzo V, Borzacchiello MT, Ciuffo B (2011) On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (NGSIM) program data. Transp Res Part C Emerg Technol 19(6):1243–1262CrossRef Punzo V, Borzacchiello MT, Ciuffo B (2011) On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (NGSIM) program data. Transp Res Part C Emerg Technol 19(6):1243–1262CrossRef
49.
go back to reference Tkachenko R, Izonin I, Kryvinska N, Dronyuk I, Zub K (2020) An approach towards increasing prediction accuracy for the recovery of missing IoT data based on the GRNN-SGTM ensemble. Sensors 20(9):2625CrossRef Tkachenko R, Izonin I, Kryvinska N, Dronyuk I, Zub K (2020) An approach towards increasing prediction accuracy for the recovery of missing IoT data based on the GRNN-SGTM ensemble. Sensors 20(9):2625CrossRef
50.
go back to reference E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrão, G. L. Pappa, and M. Mattoso, “Adaptive Normalization: A novel data normalization approach for non-stationary time series,” in Proceedings of the International Joint Conference on Neural Networks, 2010. E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrão, G. L. Pappa, and M. Mattoso, “Adaptive Normalization: A novel data normalization approach for non-stationary time series,” in Proceedings of the International Joint Conference on Neural Networks, 2010.
51.
go back to reference Azar J, Makhoul A, Barhamgi M, Couturier R (2019) An energy efficient IoT data compression approach for edge machine learning. Futur Gener Comput Syst 96:168–175CrossRef Azar J, Makhoul A, Barhamgi M, Couturier R (2019) An energy efficient IoT data compression approach for edge machine learning. Futur Gener Comput Syst 96:168–175CrossRef
52.
go back to reference Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons, NewyorkMATHCrossRef Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons, NewyorkMATHCrossRef
53.
go back to reference Rish I et al (2001) An empirical study of the naive Bayes classifier in IJCAI 200 workshop on empirical methods in artificial intelligence. Nat Intel 3(22):41–46 Rish I et al (2001) An empirical study of the naive Bayes classifier in IJCAI 200 workshop on empirical methods in artificial intelligence. Nat Intel 3(22):41–46
54.
go back to reference Denœux T (2008) A k-nearest neighbor classification rule based on Dempster-Shafer theory. Stud Fuzziness Soft Comput 219:737–760CrossRef Denœux T (2008) A k-nearest neighbor classification rule based on Dempster-Shafer theory. Stud Fuzziness Soft Comput 219:737–760CrossRef
55.
go back to reference Ke G et al (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inform Proc Syst 19:10 Ke G et al (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inform Proc Syst 19:10
56.
go back to reference Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis Mechanical Systems and Signal Processing. Academic Press, London Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis Mechanical Systems and Signal Processing. Academic Press, London
57.
go back to reference Izonin I, Trostianchyn A, Duriagina Z, Tkachenko R, Tepla T, Lotoshynska N (2018) The combined use of the wiener polynomial and SVM for material classification task in medical implants production. Int J Intell Syst Appl 10(9):40–47 Izonin I, Trostianchyn A, Duriagina Z, Tkachenko R, Tepla T, Lotoshynska N (2018) The combined use of the wiener polynomial and SVM for material classification task in medical implants production. Int J Intell Syst Appl 10(9):40–47
58.
go back to reference Yuan Z, Zhou X, Yang T, Tamerius J, Mantilla R (2017) Predicting traffic accidents through heterogeneous urban data : a case study. Urban Comput 3:1–9 Yuan Z, Zhou X, Yang T, Tamerius J, Mantilla R (2017) Predicting traffic accidents through heterogeneous urban data : a case study. Urban Comput 3:1–9
Metadata
Title
Internet of things based intelligent accident avoidance system for adverse weather and road conditions
Authors
J. Andrew Onesimu
Abhishikt Kadam
K. Martin Sagayam
Ahmed A. Elngar
Publication date
25-01-2021
Publisher
Springer International Publishing
Published in
Journal of Reliable Intelligent Environments / Issue 4/2021
Print ISSN: 2199-4668
Electronic ISSN: 2199-4676
DOI
https://doi.org/10.1007/s40860-021-00132-7

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