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A Review on Existing Technologies for the Identification and Measurement of Abnormal Driving

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Abstract

Driving error is one of the crucial contributing factors to the increasing number of traffic deaths all over the world. Both external and internal stimuli significantly affect the driving performance of individuals, irrespective of their mild, moderate, or aggressive driving styles. Continued research is being performed to increase the efficiency of vehicle safety systems and improvise existing autonomous and semi-autonomous vehicles. This paper reviews the existing state-of-the-art technologies for different types of abnormal driving detection. The review is categorized into three sections i.e., abnormal driving detection using i) vehicular features, ii) physiological features, and iii) hybrid features. Various approaches have been compared for abnormal driving detection and areas for improvement are distilled. The research gaps identified lie in the lack of i) consideration of environmental data, ii) non-invasive physiological data, and iii) comparative studies among different types of driving abnormalities.

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References

  1. Aronsson, K.: Female and Male Driving Behaviour on Swedish Urban Roads and Streets. KTH, Department of Transport and Logistics, Stockholm (2006)

    Google Scholar 

  2. Atchley, P., Shi, J., Yamamoto, T.: Cultural foundations of safety culture: A comparison of traffic safety culture in China, Japan and the United States. Transport. Res. F: Traffic Psychol. Behav. 26, 317–325 (2014)

    Article  Google Scholar 

  3. Brookhuis, K., de Waard, D.: The consequences of automation for driver behaviour and acceptance. Proceedings of the International Ergonomics Association (IEA), 10–14 (2006)

  4. Casey, B.J., Jones, R.M., Somerville, L.H.: Braking and accelerating of the adolescent brain. J. Res. Adolesc. 21, 21–33 (2011)

    Article  Google Scholar 

  5. Chen, Y., Lu, F., Zhang, J.: Social comparisons, status and driving behavior. J. Public Econ. 155, 11–20 (2017)

    Article  Google Scholar 

  6. Dahlen, E.R., White, R.P.: The Big Five factors, sensation seeking, and driving anger in the prediction of unsafe driving. Personality Individ. Differ. 41(5), 903–915 (2006)

    Article  Google Scholar 

  7. Hjälmdahl, M., Krupenia, S., Thorslund, B.: Driver behaviour and driver experience of partial and fully automated truck platooning–a simulator study. Eur. Transp. Res. Rev. 9(1), 8 (2017)

    Article  Google Scholar 

  8. Jamson, A.H., Merat, N., Carsten, O.M., Lai, F.C.: Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. C: Emerg. Technol. 30, 116–125 (2013)

    Article  Google Scholar 

  9. Merat, N., Jamson, A.H., Lai, F.C., Daly, M., Carsten, O.M.: Transition to manual: Driver behaviour when resuming control from a highly automated vehicle. Transport. Res. F: Traffic Psychol. Behav. 27, 274–282 (2014)

    Article  Google Scholar 

  10. Özkan, T., Lajunen, T., Chliaoutakis, J.E., Parker, D., Summala, H.: Cross-cultural differences in driving behaviours: A comparison of six countries. Transport. Res. F: Traffic Psychol. Behav. 9(3), 227–242 (2006)

    Article  Google Scholar 

  11. Venezian, E., Squires, D.B.S.R.: Good and bad drivers-a markov model of accident proneness. PCAS 68(198), 1 (1981)

    Google Scholar 

  12. Zhang, M., Chen, C., Wo, T., Xie, T., Bhuiyan, M.Z.A., Lin, X.: SafeDrive: online driving anomaly detection from large-scale vehicle data. IEEE Trans. Industr. Inf. 13(4), 2087–2096 (2017)

    Article  Google Scholar 

  13. Huang, W., Liu, X., Luo, M., Zhang, P., Wang, W., Wang, J.: Video-based abnormal driving behavior detection via deep learning fusions. IEEE Access 7, 64571–64582 (2019)

    Article  Google Scholar 

  14. Abe, E., Fujiwara, K., Hiraoka, T., Yamakawa, T., Kano, M.: Development of drowsiness detection method by integrating heart rate variability analysis and multivariate statistical process control. SICE J. Control Meas. Syst. Integration 9(1), 10–17 (2016)

    Article  Google Scholar 

  15. Wu, C.K., Tsang, K.F., Chi, H.R., Hung, F.H.: A precise drunk driving detection using weighted kernel based on electrocardiogram. Sensors 16(5), 659 (2016)

    Article  Google Scholar 

  16. Murugan Ezhumalai, V.S., Pitchaikannu, V.: Drowsy driver detection and accident prevention system using bio-medical electronics. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol, 4. (2007)

  17. Xie, Y., Li, F., Wu, Y., Yang, S., Wang, Y.: D 3-Guard: Acoustic-based drowsy driving detection using smartphones. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications (pp. 1225–1233). IEEE (2019)

  18. AfWåhlberg, A.E., Barraclough, P., Freeman, J.: The Driver Behaviour Questionnaire as accident predictor; a methodological re-meta-analysis. J. Saf. Res. 55, 185–212 (2015)

    Article  Google Scholar 

  19. Xu, L., Hu, J., Jiang, H., Meng, W.: Establishing style-oriented driver models by imitating human driving behaviors. IEEE Trans. Intell. Transp. Syst. 16(5), 2522–2530 (2015)

    Article  Google Scholar 

  20. Sathyanarayana, A., Nageswaren, S., Ghasemzadeh, H., Jafari, R., Hansen, J.H.: Body sensor networks for driver distraction identification. In 2008 IEEE international conference on vehicular electronics and safety (pp. 120–125). IEEE (2008)

  21. Jamal, S., Zeid, H., Malli, M., Yaacoub, E.: Safe driving: A mobile application for detecting traffic accidents. In 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM) (pp. 1–6). IEEE (2018)

  22. Jia, S., Hui, F., Li, S., Zhao, X., Khattak, A.J.: Long short-term memory and convolutional neural network for abnormal driving behaviour recognition. IET Intel. Transport Syst. 14(5), 306–312 (2019)

    Article  Google Scholar 

  23. Dhar, P., Shinde, S., Jadav, N., Bhaduri, A.: Unsafe driving detection system using smartphone as sensor platform. Int. J. Enhanc. Res. Manag. Comput. Appl. 3, 65–70 (2014)

    Google Scholar 

  24. Castignani, G., Derrmann, T., Frank, R., Engel, T.: Driver behavior profiling using smartphones: A low-cost platform for driver monitoring. IEEE Intell. Transp. Syst. Mag. 7(1), 91–102 (2015)

    Article  Google Scholar 

  25. Karaduman, O., Eren, H., Kurum, H., Celenk, M.: An effective variable selection algorithm for Aggressive/Calm Driving detection via CAN bus. In Connected Vehicles and Expo (ICCVE), 2013 International Conference on (pp. 586–591). IEEE (2013)

  26. Zhang, H., Qu, W., Ge, Y., Zhang, K.: Effect of personality traits, age and sex on aggressive driving: Psychometric adaptation of the Driver Aggression Indicators Scale in China. Accid. Anal. Prev. 103, 29–36 (2017)

    Article  Google Scholar 

  27. Carmona, J., García, F., Martín, D., Escalera, A., Armingol, J.: Data fusion for driver behaviour analysis. Sensors 15(10), 25968–25991 (2015)

    Article  Google Scholar 

  28. Burton, A., Parikh, T., Mascarenhas, S., Zhang, J., Voris, J., Artan, N.S., Li, W.: Driver identification and authentication with active behavior modeling. In Network and Service Management (CNSM), 2016 12th Internationa Conference on (pp. 388–393). IEEE (2016).

  29. Lee, B.G., Lee, B.L., Chung, W.Y.: Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6126–6129). IEEE (2015)

  30. Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on (pp. 1609–1615). IEEE (2011)

  31. Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behavior by a smartphone. In 2012 IEEE Intelligent Vehicles Symposium (pp. 234–239). IEEE (2012)

  32. Munla, N., Khalil, M., Shahin, A., & Mourad, A.: Driver stress level detection using HRV analysis. In 2015 International Conference on Advances in Biomedical Engineering (ICABME) (pp. 61–64). IEEE (2015)

  33. Chakraborty, N., Singh, H.: A comparative study of psychomotor performance of drivers with and without usages of alcohol. National symposium on alcohol revisiting and current situation and planning ahead, National drug dependence treatment centre, AIIMS Oct, 2008 (2008)

  34. Awais, M., Badruddin, N., Drieberg, M.: A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17(9), 1991 (2017)

    Article  Google Scholar 

  35. Vijay, J., Saritha, B., Priyadharshini, B., Deepeka, S., Laxmi, R.: Drunken drive protection system. Int. J. Sci. Eng. Res. 2(12) (2011)

  36. WHO statistics (2020): Road traffic injuries (Online), Available: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries (2020)

  37. Oxford Martin School Statistics (Online), Available: https://ourworldindata.org/grapher/road-traffic-deaths-sdgs?time=earliest..2019 (2019)

  38. WHO: Department of Violence and Injury Prevention and Disability (VIP): “Global Status Report on Road Safety”, Available:https://www.afro.who.int/sites/default/files/2017-06/vid_global_status_report_en.pdf (2017)

  39. Ministry of Road Transport and Highways: Road Accidents in India 2019 (Online), Available: https://morth.nic.in/road-accident-in-india (2020)

  40. Mudgal, A., Hallmark, S., Carriquiry, A., Gkritza, K.: Bayesian regression analysis. Transp. Res. Part D: Transp. Environ. 26, 20–26 (2014)

    Article  Google Scholar 

  41. Sanguinetti, A., Kurani, K., Davies, J.: The many reasons your mileage may vary: Toward a unifying typology of eco-driving behaviors. Transp. Res. Part D: Transp. Environ. 52, 73–84 (2017)

    Article  Google Scholar 

  42. Christmas, S.: The good, the bad and the talented: Young drivers' perspectives on good driving and learning to drive. Department for Transport (2007)

  43. De Winter, J.C.F., Dodou, D.: The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis. J. Saf. Res. 41(6), 463–470 (2010)

    Article  Google Scholar 

  44. Helman, S., Reed, N.: Validation of the driver behaviour questionnaire using behavioural data from an instrumented vehicle and high-fidelity driving simulator. Accid. Anal. Prev. 75, 245–251 (2015)

    Article  Google Scholar 

  45. Li, Z., Jin, X., Zhao, X.: Drunk driving detection based on classification of multivariate time series. J. Safety Res. 54, 61-e29 (2015)

    Article  Google Scholar 

  46. Chen, H., Chen, L.: Support vector machine classification of drunk driving behaviour. Int. J. Environ. Res. Public Health 14(1), 108 (2017)

    Article  Google Scholar 

  47. Lim, S., Yang, J.H.: Driver state estimation by convolutional neural network using multimodal sensor data. Electron. Lett. 52(17), 1495–1497 (2016)

    Article  Google Scholar 

  48. Mohamad, I., Ali, M. A. M., Ismail, M.: Abnormal driving detection using real time global positioning system data. In Proceeding of the 2011 IEEE international conference on space science and communication (IconSpace) (pp. 1–6). IEEE (2011)

  49. Rigas, G., Goletsis, Y., Bougia, P., Fotiadis, D. I.: Towards driver's state recognition on real driving conditions. Int. J. Vehicular Technol. 2011 (2011)

  50. Ali, A.H., Atia, A., Mostafa, M.S.M.: Recognizing driving behavior and road anomaly using smartphone sensors. Int. J. Ambient Comput. Intell. 8(3), 22–37 (2017)

    Article  Google Scholar 

  51. Zhai, Y., Wo, T., Lin, X., Huang, Z., Chen, J.: A context-aware evaluation method of driving behavior. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 462–474). Springer, Cham (2018)

  52. Barua, S., Ahmed, M.U., Ahlström, C., Begum, S.: Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Syst. Appl. 115, 121–135 (2019)

    Article  Google Scholar 

  53. Manzoni, V., Corti, A., De Luca, P., Savaresi, S.M.: Driving style estimation via inertial measurements. In 13th International IEEE Conference on Intelligent Transportation Systems (pp. 777–782). IEEE (2010)

  54. Dai, J., Teng, J., Bai, X., Shen, Z., Xuan, D.: Mobile phone based drunk driving detection. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS (pp. 1–8). IEEE (2010)

  55. Saruwatari, K., Sakaue, F., Sato, J.: Detection of abnormal driving using multiple view geometry in space-time. In 2012 IEEE Intelligent Vehicles Symposium (pp. 1102–1107). IEEE (2012)

  56. Van Ly, M., Martin, S., Trivedi, M. M.: Driver classification and driving style recognition using inertial sensors. In Intelligent Vehicles Symposium (IV), 2013 IEEE (pp. 1040–1045). IEEE (2013)

  57. Vaiana, R., Iuele, T., Astarita, V., Caruso, M.V., Tassitani, A., Zaffino, C., Giofrè, V.P.: Driving behavior and traffic safety: an acceleration-based safety evaluation procedure for smartphones. Mod. Appl. Sci. 8(1), 88 (2014)

    Article  Google Scholar 

  58. Chen, Z., Yu, J., Zhu, Y., Chen, Y., Li, M.: D3: Abnormal driving behaviors detection and identification using smartphone sensors. In Sensing, Communication, and Networking (SECON), 2015 12th Annual IEEE International Conference on (pp. 524–532). IEEE (2015)

  59. Ma, C., Dai, X., Zhu, J., Liu, N., Sun, H., Liu, M.: Drivingsense: Dangerous driving behavior identification based on smartphone autocalibration. Mobile Information Systems (2017)

  60. Romera, E., Bergasa, L.M., Arroyo, R.: Need data for driver behaviour analysis? Presenting the public UAH-DriveSet. In Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on (pp. 387–392). IEEE (2016)

  61. Shi, B., Xu, L., Hu, J., Tang, Y., Jiang, H., Meng, W., Liu, H.: Evaluating driving styles by normalizing driving behavior based on personalized driver modeling. IEEE Trans. Syst. Man Cybernetics: Syst. 45(12), 1502–1508 (2015)

    Article  Google Scholar 

  62. Hu, J., Xu, L., He, X., Meng, W.: Abnormal driving detection based on normalized driving behavior. IEEE Trans. Veh. Technol. 66(8), 6645–6652 (2017)

    Article  Google Scholar 

  63. Hu, J., Zhang, X., Maybank, S.: Abnormal driving detection with normalized driving behavior data: a deep learning approach. IEEE Transactions on Vehicular Technology (2020)

  64. Júnior, J.F., Carvalho, E., Ferreira, B.V., de Souza, C., Suhara, Y., Pentland, A., Pessin, G.: Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE 12(4), e0174959 (2017)

    Article  Google Scholar 

  65. Wang, W., Xi, J.: A rapid pattern-recognition method for driving styles using clustering-based support vector machines. In American Control Conference (ACC), 2016 (pp. 5270–5275). IEEE (2016)

  66. Carmona, J., de Miguel, M.A., Martin, D., Garcia, F., de la Escalera, A.: Embedded system for driver behavior analysis based on GMM. In 2016 IEEE Intelligent Vehicles Symposium (IV) (pp. 61–65). IEEE (2016)

  67. Choudhary, P., Velaga, N.R.: Analysis of vehicle-based lateral performance measures during distracted driving due to phone use. Transport. Res. F: Traffic Psychol. Behav. 44, 120–133 (2017)

    Article  Google Scholar 

  68. Choudhary, P., Velaga, N.R.: Effects of phone use on driving performance: a comparative analysis of young and professional drivers. Saf. Sci. 111, 179–187 (2019)

    Article  Google Scholar 

  69. Saab Alcokey: "Saab Alco Key Helps Drivers", (Online) Available: Saab AlcoKey (saabplanet.com). (2014)

  70. Wakana, H., Yamada, M., Sakairi, M.: Portable Alcohol Detection System with Breath-Recognition Function. In 2018 IEEE SENSORS (pp. 1–4). IEEE (2018)

  71. Zhu, Z., Ji, Q.: Real time and non-intrusive driver fatigue monitoring. In Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on (pp. 657–662). IEEE (2004)

  72. Gupta, R., Aman, K., Shiva, N., Singh, Y.: An improved fatigue detection system based on behavioral characteristics of driver. In Intelligent Transportation Engineering (ICITE), 2017 2nd IEEE International Conference on (pp. 227–230). IEEE (2017)

  73. Lee, K.W., Yoon, H.S., Song, J.M., Park, K.R.: Convolutional neural network-based classification of driver’s emotion during aggressive and smooth driving using multi-modal camera sensors. Sensors 18(4), 957 (2018)

    Article  Google Scholar 

  74. Lang, L., Qi, H.: The study of driver fatigue monitor algorithm combined PERCLOS and AECS. In Computer Science and Software Engineering, 2008 International Conference on (Vol. 1, pp. 349–352). IEEE (2008)

  75. Dasgupta, A., George, A., Happy, S.L., Routray, A.: A vision-based system for monitoring the loss of attention in automotive drivers. IEEE Trans. Intell. Transp. Syst. 14(4), 1825–1838 (2013)

    Article  Google Scholar 

  76. Dasgupta, A., George, A., Happy, S.L., Routray, A., Shanker, T.: An on-board vision based system for drowsiness detection in automotive drivers. Int. J. Adv. Eng. Sci. Appl. Math. 5(2–3), 94–103 (2013)

    Article  Google Scholar 

  77. Dasgupta, A., Rahman, D., Routray, A.: A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers. IEEE Transactions on Intelligent Transportation Systems (2018)

  78. Lee, B.G., Chung, W.Y.: Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sens. J. 12(7), 2416–2422 (2012)

    Article  Google Scholar 

  79. Catalbas, M.C., Cegovnik, T., Sodnik, J., Gulten, A.: Driver fatigue detection based on saccadic eye movements. In Electrical and Electronics Engineering (ELECO), 2017 10th International Conference on (pp. 913–917). IEEE (2017)

  80. Rahman, H., Barua, S., Ahmed, M. U., Begum, S., Hök, B.: A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals. In International Conference on IoT Technologies for HealthCare (pp. 22–29). Springer, Cham (2016)

  81. Gupta, S., Kar, S., Gupta, S., & Routray, A.: Fatigue in human drivers: A study using ocular, Psychometric, physiological signals. In 2010 IEEE Students Technology Symposium (TechSym) (pp. 234–240). IEEE (2010).

  82. Kar, S., Bhagat, M., Routray, A.: EEG signal analysis for the assessment and quantification of driver’s fatigue. Transport. Res. F: Traffic Psychol. Behav. 13(5), 297–306 (2010)

    Article  Google Scholar 

  83. Kar, S., Routray, A., Nayak, B.P.: Functional network changes associated with sleep deprivation and fatigue during simulated driving: validation using blood biomarkers. Clin. Neurophysiol. 122(5), 966–974 (2011)

    Article  Google Scholar 

  84. Lee, B.G., Lee, B.L., Chung, W.Y.: Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals. Sensors 14(10), 17915–17936 (2014)

    Article  Google Scholar 

  85. Sengupta, A., Kar, S., & Routray, A.: Study of loss of alertness and driver fatigue using visibility graph synchronization. In Innovative Research in Attention Modeling and Computer Vision Applications (pp. 171–193). IGI Global (2016)

  86. Chaudhuri, A., & Routray, A.: Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals. IEEE Transactions on Intelligent Transportation Systems (2019)

  87. Lee, H.B., Kim, J.S., Kim, Y.S., Baek, H.J., Ryu, M.S., & Park, K.S.: The relationship between HRV parameters and stressful driving situation in the real road. In 2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine (pp. 198–200). IEEE (2007)

  88. Li, G., Chung, W.Y.: Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier. Sensors 13(12), 16494–16511 (2013)

    Article  Google Scholar 

  89. Fujiwara, K., Abe, E., Kamata, K., Nakayama, C., Suzuki, Y., Yamakawa, T., … Matsuo, M.: Heart rate variability-based driver drowsiness detection and its validation with EEG. IEEE Trans. Biomed. Eng. 66(6), 1769–1778 (2018)

  90. Xu, X., Yu, J., Chen, Y., Zhu, Y., Qian, S., Li, M.: Leveraging audio signals for early recognition of inattentive driving with smartphones. IEEE Transactions on Mobile international conference on vehicular electronics and safety (pp. 120–125). IEEE (2017)

  91. Jakkar, R.K., Pahuja, R., Saini, R.K., Sahu, B.: Drunk-Driver Detection and Alert System (DDDAS) for smart vehicles. Am. J. Traffic Transp. Eng. 2(4), 45–58 (2017)

    Google Scholar 

  92. Murata, K., Fujita, E., Kojima, S., Maeda, S., Ogura, Y., Kamei, T., … Suzuki, N.: Noninvasive biological sensor system for detection of drunk driving. IEEE trans. Inf. Technol. Biomed. 15(1), 19–25 (2011)

  93. Sober Steering (Online) Available: https://www.fastcompany.com/company/sober-steering (2016)

  94. VerSteeg, B., Treese, D., Adelante, R., Kraintz, A., Laaksonen, B., Ridder, T., … Koeth, J.: Development of a Solid State, Non-Invasive, Human Touch Based Blood Alcohol Sensor. In 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration (2017)

  95. Cech, L. et al.: Introduction of a Solid State Noninvasive Human Touch Based Alcohol Sensor. Paper No. 15–0380. 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV). Gothenburg, Sweden, June 8–11, 2015 (2015)

  96. Opeyemi, A.E.: Alcohol detection of drunk drivers with automatic car engine locking system Dada Emmanuel Gbenga, Hamit Isseini Hamed, Adebimpe Adekunle Lateef 2. Nova 6(1), 1–15 (2017)

    Google Scholar 

  97. Du, R., Qiu, G., Gao, K., Hu, L., Liu, L.: Abnormal road surface recognition based on smartphone acceleration sensor. Sensors 20(2), 451 (2020)

    Article  Google Scholar 

  98. Seraj, F., van der Zwaag, B. J., Dilo, A., Luarasi, T., Havinga, P.: RoADS: A road pavement monitoring system for anomaly detection using smart phones. In Big data analytics in the social and ubiquitous context (pp. 128–146). Springer, Cham (2015)

  99. Sharma, H., Naik, S., Jain, A., Raman, R.K., Reddy, R.K., Shet, R.B.: S-road assist: Road surface conditions and driving behavior analysis using smartphones. In 2015 International Conference on Connected Vehicles and Expo (ICCVE) (pp. 291–296). IEEE (2015)

  100. Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on Embedded network sensor systems (pp. 323–336) (2008)

  101. Riveiro, M., Lebram, M., Elmer, M.: Anomaly detection for road traffic: A visual analytics framework. IEEE Trans. Intell. Transp. Syst. 18(8), 2260–2270 (2017)

    Article  Google Scholar 

  102. Yuan, Y., Wang, D., Wang, Q.: Anomaly detection in traffic scenes via spatial-aware motion reconstruction. IEEE Trans. Intell. Transp. Syst. 18(5), 1198–1209 (2016)

    Article  Google Scholar 

  103. Li, Y., Guo, T., Xia, R., Xie, W.: Road traffic anomaly detection based on fuzzy theory. IEEE Access 6, 40281–40288 (2018)

    Article  Google Scholar 

  104. Yoneda, K., Suganuma, N., Yanase, R., Aldibaja, M.: Automated driving recognition technologies for adverse weather conditions. IATSS Res. 43(4), 253–262 (2019)

    Article  Google Scholar 

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Sar, I., Routray, A. & Mahanty, B. A Review on Existing Technologies for the Identification and Measurement of Abnormal Driving. Int. J. ITS Res. 21, 159–177 (2023). https://doi.org/10.1007/s13177-023-00343-7

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