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Factors Influencing Driver Behavior and Advances in Monitoring Methods

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AI-enabled Technologies for Autonomous and Connected Vehicles

Abstract

Monitoring driver behavior in real-time is a challenging task as there are several factors that can influence the driver to commit unpredictable mistakes while driving. These factors mainly involve inattentive driver state, absent mind, unreliable cornering, and speeding, resulting in fatal accidents. This chapter identifies the factors that affect driver behavior and performance, and provides an in-depth analysis of various deployed scientific monitoring methods and proposes solutions for early and efficient real-time monitoring of driver behavior. The chapter also reviews real-time smart detection algorithms deployed for the classification of driver state. In addition, the chapter proposes an unsupervised deep learning neural network model that can be deployed in classifying driver states and actions.

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References

  1. Art Galley of NSW (2021) Nsw interactive crash statistics. Accessed 18 May 2021. [Online]. Available https://roadsafety.transport.nsw.gov.au/statistics/interactivecrashstats/index.html

  2. Martinez CM, Heucke M, Wang F-Y, Gao B, Cao D (2017) Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans Intell Transp Syst 19(3):666–676

    Article  Google Scholar 

  3. Xie Z, Yu D, Yang J (2018) Analysis on lane changing trajectory for different style drivers. In: 2018 10th international conference on measuring technology and mechatronics automation (ICMTMA). IEEE, pp 198–203

    Google Scholar 

  4. Feng Y, Pickering S, Chappell E, Iravani P, Brace C (2018) Driving style modelling with adaptive neuro-fuzzy inference system and real driving data. In: International conference on applied human factors and ergonomics. Springer, Berlin, pp 481–490

    Google Scholar 

  5. Su C, Deng W, Sun H, Wu J, Sun B, Yang S (2017) Forward collision avoidance systems considering driver’s driving behavior recognized by gaussian mixture model. In: IEEE intelligent vehicles symposium (IV). IEEE 2017:535–540

    Google Scholar 

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

    Article  Google Scholar 

  7. Ansari S, Du H, Naghdy F, Stirling D (2021) Application of fully adaptive symbolic representation to driver mental fatigue detection based on body posture. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1313–1318

    Google Scholar 

  8. Balachandran A, Brown M, Erlien SM, Gerdes JC (2015) Predictive haptic feedback for obstacle avoidance based on model predictive control. IEEE Trans Autom Sci Eng 13(1):26–31

    Google Scholar 

  9. Yang C, Wang X, Mao S (2020) Unsupervised drowsy driving detection with RFID. IEEE Trans Veh Technol 69(8):8151–8163

    Google Scholar 

  10. Savaş BK, Becerikli Y (2020) Real time driver fatigue detection system based on multi-task connn. IEEE Access 8:12,491–12,498

    Google Scholar 

  11. Kaplan S, Guvensan MA, Yavuz AG, Karalurt Y (2015) Driver behavior analysis for safe driving: A survey. IEEE Trans Intell Transp Syst 16(6):3017–3032

    Google Scholar 

  12. Braitman KA, Braitman AL (2017) Patterns of distracted driving behaviors among young adult drivers: exploring relationships with personality variables. Transp Res Part F Traffic Psychol Behav 46:169–176

    Google Scholar 

  13. Sikander G, Anwar S (2018) Driver fatigue detection systems: a review. IEEE Trans Intell Transp Syst 20(6):2339–2352

    Google Scholar 

  14. May JF, Baldwin CL (2009) Driver fatigue: the importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transp Res Part F Traffic Psychol Behav 12(3):218–224

    Google Scholar 

  15. Witt M, Wang L, Fahrenkrog F, Kompaß K, Prokop G (2018) Cognitive driver behavior modeling: influence of personality and driver characteristics on driver behavior. In: International conference on applied human factors and ergonomics. Springer, Berlin, pp 751–763

    Google Scholar 

  16. Wishart D, Somoray K, Evenhuis A (2017) Thrill and adventure seeking in risky driving at work: the moderating role of safety climate. J Saf Res 63:83–89

    Google Scholar 

  17. Machin MA, Sankey KS (2008) Relationships between young drivers’ personality characteristics, risk perceptions, and driving behaviour. Accid Anal Prev 40(2):541–547

    Google Scholar 

  18. Kostyniuk LP, Molnar LJ (2008) Self-regulatory driving practices among older adults: health, age and sex effects. Accid Anal Prev 40(4):1576–1580

    Google Scholar 

  19. Ansari S, Du H, Naghdy F, Stirling D (2020) Unsupervised patterns of driver mental fatigue state based on head posture using gaussian mixture model. In: 2020 IEEE symposium series on computational intelligence (SSCI). IEEE 2020:2699–2704

    Google Scholar 

  20. Ansari S, Naghdy F, Du H, Pahnwar YN (2021) Driver mental fatigue detection based on head posture using new modified relu-bilstm deep neural network. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3098309

  21. Armstrong KA, Watling CN, Davey JD (2018) Deterrence of drug driving: the impact of the act drug driving legislation and detection techniques. Transp Res Part F Traffic Psychol Behav 54:138–147

    Google Scholar 

  22. Zakletskaia LI, Mundt MP, Balousek SL, Wilson EL, Fleming MF (2009) Alcohol-impaired driving behavior and sensation-seeking disposition in a college population receiving routine care at campus health services centers. Accid Anal Prev 41(3):380–386

    Google Scholar 

  23. Li Z, Li X, Zhao X, Zhang Q (2019) Effects of different alcohol dosages on steering behavior in curve driving. Hum Factors 61(1):139–151

    Google Scholar 

  24. Precht L, Keinath A, Krems JF (2017) Identifying the main factors contributing to driving errors and traffic violations-results from naturalistic driving data. Transp Res Part F Traffic Psychol Behav 49:49–92

    Google Scholar 

  25. Rudin-Brown CM, Edquist J, Lenné MG (2014) Effects of driving experience and sensation-seeking on drivers’ adaptation to road environment complexity. Saf Sci 62:121–129

    Google Scholar 

  26. Zhao Y, Andrey J, Deadman P (2018) Whether conversion and weather matter to roundabout safety. J Saf Res 66:151–159

    Google Scholar 

  27. Fylan F, Hughes A, Wood J, Elliott DB (2018) Why do people drive when they can’t see clearly? Transp Res Part F Traffic Psychol Behav 56:123–133

    Google Scholar 

  28. Ahmed MM, Ghasemzadeh A (2018) The impacts of heavy rain on speed and headway behaviors: an investigation using the shrp2 naturalistic driving study data. Transp Res Part C Emerg Technol 91:371–384

    Google Scholar 

  29. Zhang C, Hu J, Qiu J, Yang W, Sun H, Chen Q (2018) A novel fuzzy observer-based steering control approach for path tracking in autonomous vehicles. IEEE Trans Fuzzy Syst 27(2):278–290

    Google Scholar 

  30. Zeng X, Wang J (2017) A stochastic driver pedal behavior model incorporating road information. IEEE Trans Hum Mach Syst 47(5):614–624

    Google Scholar 

  31. Pfeffer P, Braess H-H (2017) Basics of lateral vehicle dynamics. In: Steering handbook. Springer, Berlin, pp 91–120

    Google Scholar 

  32. Ansari S, Du H, Naghdy F (2020) Driver’s foot trajectory tracking for safe maneuverability using new modified reLU-BiLSTM deep neural network. In: 2020 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 4392–4397

    Google Scholar 

  33. Hu X, Lodewijks G (2020) Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: the value of differentiation of sleepiness and mental fatigue. J Saf Res 72:173–187

    Google Scholar 

  34. Min J, Xiong C, Zhang Y, Cai M (2021) Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model. Biomed Signal Process Control 69:102857

    Google Scholar 

  35. Jung S-J, Shin H-S, Chung W-Y (2014) Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell Transp Syst 8(1):43–50

    Article  Google Scholar 

  36. Alive Technologies (2021) Alive bluetooth heart and activity monitor. Accessed 5 Nov 2021. [Online]. Available https://www.alivetec.com/pages/alive-bluetooth-heart-activity-monitor

  37. Rong Y, Dutta A, Chiriyath A, Bliss DW (2021) Motion-tolerant non-contact heart-rate measurements from radar sensor fusion. Sensors 21(5):1774

    Article  Google Scholar 

  38. Reeder B, David A (2016) Health at hand: a systematic review of smart watch uses for health and wellness. J Biomed Inform 63:269–276

    Article  Google Scholar 

  39. Azim T, Jaffar MA, Mirza AM (2014) Fully automated real time fatigue detection of drivers through fuzzy expert systems. Appl Soft Comput 18:25–38

    Article  Google Scholar 

  40. Jiang Y, Deng W, Wu J, Zhang S, He R (2018) Study on the stability of the steering torque feedback system considering the time delay and the system characteristics. Proc Inst Mech Eng Part D J Autom Eng 232(5):707–721

    Article  Google Scholar 

  41. Cole DJ (2012) A path-following driver-vehicle model with neuromuscular dynamics, including measured and simulated responses to a step in steering angle overlay. Veh Syst Dyn 50(4):573–596

    Article  Google Scholar 

  42. Kim N, Cole DJ (2011) A model of driver steering control incorporating the driver’s sensing of steering torque. Veh Syst Dyn 49(10):1575–1596

    Article  Google Scholar 

  43. Marcano M, Díaz S, Pérez J, Irigoyen E (2020) A review of shared control for automated vehicles: theory and applications. IEEE Trans Hum Mach Syst 50(6):475–491. https://doi.org/10.1109/THMS.2020.3017748

  44. Pacejka HB (2012) Semi-empirical tire models (Chapter 4). In: Pacejka HB (ed) Tire and vehicle dynamics, 3rd edn. Butterworth-Heinemann, Oxford, pp 149–209. ISBN: 9780080970165. https://doi.org/10.1016/B978-0-08-097016-5.00004-8. https://www.sciencedirect.com/science/article/pii/B9780080970165000048

  45. Li B, Du H, Li W (2014, January 3) A novel method for side slip angle estimation of omni-directional vehicles. SAE Int J Passeng Cars Electron Electr Syst 7(3):471–480

    Google Scholar 

  46. Smith DE, Starkey JM (1995) Effects of model complexity on the performance of automated vehicle steering controllers: model development, validation and comparison. Vehicle System Dynamics 24(2):163–181

    Article  Google Scholar 

  47. Dong Y, Hu Z, Uchimura K, Murayama N (2010) Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans Intell Transp Syst 12(2):596–614

    Article  Google Scholar 

  48. Fridman L (2018) Human-centered autonomous vehicle systems: principles of effective shared autonomy. arXiv preprint arXiv:1810.01835

  49. Ro JW, Roop PS, Malik A, Ranjitkar P (2017) A formal approach for modeling and simulation of human car-following behavior. IEEE Trans Intell Trans Syst 19(2):639–648

    Article  Google Scholar 

  50. Qu T, Chen H, Cao D, Guo H, Gao B (2014) Switching-based stochastic model predictive control approach for modeling driver steering skill. IEEE Trans Intell Transp Syst 16(1):365–375

    Article  Google Scholar 

  51. Plöchl M, Edelmann J (2007) Driver models in automobile dynamics application. Veh Syst Dyn 45(7–8):699–741

    Article  Google Scholar 

  52. Wang Z, Liao X, Wang C, Oswald D, Wu G, Boriboonsomsin K, Barth MJ, Han K, Kim B, Tiwari P (2020) Driver behavior modeling using game engine and real vehicle: A learning-based approach. IEEE Trans Intell Veh 5(4):738–749

    Article  Google Scholar 

  53. Khairdoost N, Shirpour M, Bauer MA, Beauchemin SS (2020) Real-time driver maneuver prediction using LSTM. IEEE Trans Intell Veh 5(4):714–724

    Article  Google Scholar 

  54. MATLAB (2021) Matlab classification learner application. Accessed 30 May 2021. [Online]. Available https://au.mathworks.com/help/stats/classificationlearner-app.html

  55. MATLAB (2021) Matlab unsupervised classification. Accessed 30 May 2021 [Online]. Available https://au.mathworks.com/discovery/unsupervised-learning.html

  56. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  57. Monner D, Reggia JA (2012) A generalized LSTM-like training algorithm for second-order recurrent neural networks. Neural Netw 25:70–83

    Article  Google Scholar 

  58. Serra J, Arcos JL (2014) An empirical evaluation of similarity measures for time series classification. Knowl Based Syst 67:305–314

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia

    Shahzeb Ansari, Haiping Du, Fazel Naghdy & David Stirling

Authors
  1. Shahzeb Ansari
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  2. Haiping Du
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  3. Fazel Naghdy
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  4. David Stirling
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Corresponding author

Correspondence to Shahzeb Ansari .

Editor information

Editors and Affiliations

  1. College of Engineering and Computer Science, University of Michigan–Dearborn, Dearborn, MI, USA

    Yi Lu Murphey

  2. Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA

    Ilya Kolmanovsky

  3. College of Engineering and Computer Science, University of Michigan–Dearborn, Dearborn, MI, USA

    Paul Watta

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Ansari, S., Du, H., Naghdy, F., Stirling, D. (2023). Factors Influencing Driver Behavior and Advances in Monitoring Methods. In: Murphey, Y.L., Kolmanovsky, I., Watta, P. (eds) AI-enabled Technologies for Autonomous and Connected Vehicles. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-031-06780-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-06780-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06779-2

  • Online ISBN: 978-3-031-06780-8

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