ABSTRACT
Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator.
In this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated.
Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.
- Ahangari, S., Pour, A. H., Khadem, N., & Banerjee, S. (2019). Investigating the Impact of Distracted Driving among Different Socio-Demographic Groups.Google Scholar
- Ahangari, S., Rashidi Moghaddam, Z., Jeihani, M., Chavis, C., Chen, H., Rakha, H., & Kang, K. (2019). Investigating the Effectiveness of an Eco-Speed Control System in the Vicinity of Signalized Intersections Using a Driving Simulator (No. 19-00716).Google Scholar
- Ben-Gal, I., Shani, A., Gohr, A., Grau, J., Arviv, S., Shmilovici, A., & Grosse, I. (2005). Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics, 21(11), 2657--2666.Google Scholar
- C. J. D. Patten, A. Kircher, J. Östlund, and L. Nilsson, "Using mobile telephones: cognitive workload and attention resource allocation," Accid. Anal. Prev., vol. 36, no. 3, pp. 341--350, May 2004.Google ScholarCross Ref
- D. L. Strayer and F. A. Drews, "Cell-Phone-Induced Driver Distraction," Curr. Dir. Psychol. Sci., vol. 16, no. 3, pp. 128--131, Jun. 2007.Google ScholarCross Ref
- D. L. Strayer, F. A. Drews, and D. J. Crouch, "A Comparison of the Cell Phone Driver and the Drunk Driver," Hum. Factors, p. 11, 2006.Google Scholar
- D. M. Neyens and L. N. Boyle, "The effect of distractions on the crash types of teenage drivers," Accid. Anal. Prev., vol. 39, no. 1, pp. 206--212, Jan. 2007.Google ScholarCross Ref
- D. M. Neyens and L. N. Boyle, "The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers," Accid. Anal. Prev., vol. 40, no. 1, pp. 254--259, Jan. 2008.Google ScholarCross Ref
- D. Mayhew, R. Robertson, S. Brown, and W. Vanlaar, "Driver Distraction and Hands-Free Texting While Driving," p. 10.Google Scholar
- D. Shinar, N. Tractinsky, and R. Compton, "Effects of practice, age, and task demands, on interference from a phone task while driving," Accid. Anal. Prev., vol. 37, no. 2, pp. 315--326, Mar. 2005.Google ScholarCross Ref
- D. Stavrinos et al., "Impact of distracted driving on safety and traffic flow," Accid. Anal. Prev., vol. 61, pp. 63--70, Dec. 2013.Google ScholarCross Ref
- D. Whitley, "A genetic algorithm tutorial," Stat. Comput., vol. 4, no. 2, pp. 65--85, 1994.Google ScholarCross Ref
- E. Aarts and J. Korst, "Simulated annealing and Boltzmann machines," 1988.Google Scholar
- F. A. Wilson and J. P. Stimpson, "Trends in Fatalities From Distracted Driving in the United States, 1999 to 2008," Am. J. Public Health, vol. 100, no. 11, pp. 2213--2219, Nov. 2010.Google ScholarCross Ref
- F. Glover and M. Laguna, "Tabu search," in Handbook of combinatorial optimization, Springer, 1998, pp. 2093--2229.Google Scholar
- Fitch, G. M., Hanowski, R. J., & Guo, F. (2015). The risk of a safety-critical event associated with mobile device use in specific driving contexts. Traffic injury prevention, 16(2), 124--132.Google Scholar
- J. K. Caird, C. R. Willness, P. Steel, and C. Scialfa, "A metaanalysis of the effects of cell phones on driver performance," Accid. Anal. Prev., vol. 40, no. 4, pp. 1282--1293, Jul. 2008.Google ScholarCross Ref
- J. L. Harbluk, Y. I. Noy, P. L. Trbovich, and M. Eizenman, "An on-road assessment of cognitive distraction: Impacts on drivers' visual behavior and braking performance," Accid. Anal. Prev., vol. 39, no. 2, pp. 372--379, Mar. 2007.Google ScholarCross Ref
- J. M. Cooper and D. L. Strayer, "Effects of Simulator Practice and Real-World Experience on Cell-Phone---Related Driver Distraction," Hum. Factors J. Hum. Factors Ergon. Soc., vol. 50, no. 6, pp. 893--902, Dec. 2008.Google ScholarCross Ref
- J. M. Owens, S. B. McLaughlin, and J. Sudweeks, "Driver performance while text messaging using handheld and invehicle systems," Accid. Anal. Prev., vol. 43, no. 3, pp. 939--947, May 2011.Google ScholarCross Ref
- J. M. Watson and D. L. Strayer, "Supertaskers: Profiles in extraordinary multitasking ability," Psychon. Bull. Rev., vol. 17, no. 4, pp. 479--485, Aug. 2010.Google ScholarCross Ref
- J. Salmon, M. S. Tremblay, S. J. Marshall, and C. Hume, "Health Risks, Correlates, and Interventions to Reduce Sedentary Behavior in Young People," Am. J. Prev. Med., vol. 41, no. 2, pp. 197--206, Aug. 2011.Google ScholarCross Ref
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182--197, 2002.Google ScholarDigital Library
- L. Pöysti, S. Rajalin, and H. Summala, "Factors influencing the use of cellular (mobile) phone during driving and hazards while using it," Accid. Anal. Prev., vol. 37, no. 1, pp. 47--51, Jan. 2005.Google ScholarCross Ref
- Lee, J. D. (2009). Can technology get your eyes back on the road?. Science, 324(5925), 344--346.Google Scholar
- M. A. Just, T. A. Keller, and J. Cynkar, "A decrease in brain activation associated with driving when listening to someone speak," Brain Res., vol. 1205, pp. 70--80, Apr. 2008.Google ScholarCross Ref
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD Explor. Newsl., vol. 11, no. 1, pp. 10--18, 2009.Google ScholarDigital Library
- M. L. Reyes and J. D. Lee, "Effects of cognitive load presence and duration on driver eye movements and event detection performance," Transp. Res. Part F Traffic Psychol. Behav., vol. 11, no. 6, pp. 391--402, Nov. 2008.Google ScholarCross Ref
- M. Mitchell, J. H. Holland, and S. Forrest, "When will a Genetic Algorithm Outperform Hill Climbing," in Advances in Neural Information Processing Systems 6, J. D. Cowan, G. Tesauro, and J. Alspector, Eds. Morgan-Kaufmann, 1994, pp. 51--58.Google Scholar
- M. N. Lees and J. D. Lee, "The influence of distraction and driving context on driver response to imperfect collision warning systems," Ergonomics, vol. 50, no. 8, pp. 1264--1286, Aug. 2007.Google ScholarCross Ref
- N. A. Stanton and M. S. Young, "Vehicle automation and driving performance," Ergonomics, vol. 41, no. 7, pp. 1014--1028, Jul. 1998.Google ScholarCross Ref
- Narad, M., Garner, A. A., Brassell, A. A., Saxby, D., Antonini, T. N., O'Brien, K. M., & Epstein, J. N. (2013). Impact of distraction on the driving performance of adolescents with and without attention-deficit/hyperactivity disorder. JAMA pediatrics, 167(10), 933--938.Google Scholar
- P. Atchley, C. Hadlock, and S. Lane, "Stuck in the 70s: The role of social norms in distracted driving," Accid. Anal. Prev., vol. 48, pp. 279--284, Sep. 2012.Google ScholarCross Ref
- R. Dechter and J. Pearl, "Network-based heuristics for constraint-satisfaction problems," in Search in artificial intelligence, Springer, 1988, pp. 370--425.Google Scholar
- R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, vol. 14, pp. 1137--1145.Google ScholarDigital Library
- S. E. Lee et al., "Detection of Road Hazards by Novice Teen and Experienced Adult Drivers," Transp. Res. Rec. J. Transp. Res. Board, vol. 2078, no. 1, pp. 26--32, Jan. 2008.Google ScholarCross Ref
- S. G. Hosking, K. L. Young, and M. A. Regan, "The Effects of Text Messaging on Young Drivers," Hum. Factors J. Hum. Factors Ergon. Soc., vol. 51, no. 4, pp. 582--592, Aug. 2009.Google ScholarCross Ref
- S. G. Klauer, F. Guo, B. G. Simons-Morton, M. C. Ouimet, S. E. Lee, and T. A. Dingus, "Distracted Driving and Risk of Road Crashes among Novice and Experienced Drivers," N. Engl. J. Med., vol. 370, no. 1, pp. 54--59, Jan. 2014.Google ScholarCross Ref
- S. P. McEvoy et al., "Role of mobile phones in motor vehicle crashes resulting in hospital attendance: a case-crossover study," BMJ, vol. 331, no. 7514, p. 428, Aug. 2005.Google ScholarCross Ref
- S. V. Masten, R. D. Foss, and S. W. Marshall, "Graduated driver licensing program component calibrations and their association with fatal crash involvement," Accid. Anal. Prev., vol. 57, pp. 105--113, Aug. 2013.Google ScholarCross Ref
- Schroeder, P., Wilbur, M., & Peña, R. (2018, March). National survey on distracted driving attitudes and behaviors - 2015 (Report No. DOT HS 812 461). Washington, DCGoogle Scholar
- Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone-induced failures of visual attention during simulated driving. Journal of experimental psychology: Applied, 9(1), 23.Google Scholar
- T. Horberry, J. Anderson, M. A. Regan, T. J. Triggs, and J. Brown, "Driver distraction: The effects of concurrent invehicle tasks, road environment complexity and age on driving performance," Accid. Anal. Prev., vol. 38, no. 1, pp. 185--191, Jan. 2006.Google ScholarCross Ref
- T. Koski and J. Noble, Bayesian networks: an introduction, vol. 924. John Wiley & Sons, 2011.Google Scholar
- T. W. Victor, J. L. Harbluk, and J. A. Engström, "Sensitivity of eye-movement measures to in-vehicle task difficulty," Transp. Res. Part F Traffic Psychol. Behav., vol. 8, no. 2, pp. 167--190, Mar. 2005.Google ScholarCross Ref
- Tippey, K. G., Sivaraj, E., Ardoin, W. J., Roady, T., & Ferris, T. K. (2014, September). Texting while driving using Google Glass: Investigating the combined effect of heads-up display and hands-free input on driving safety and performance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 58, No. 1, pp. 2023--2027). Sage CA: Los Angeles, CA: SAGE Publications.Google Scholar
- Victor, T., & Johansson, E. (2005). Gaze concentration in visual and cognitive tasks: Using eye movements to measure driving information loss.Google Scholar
- W. J. Horrey, M. F. Lesch, and A. Garabet, "Assessing the awareness of performance decrements in distracted drivers," Accid. Anal. Prev., vol. 40, no. 2, pp. 675--682, Mar. 2008.Google ScholarCross Ref
- Y. Christine E., "Driver safety impacts of voice-to-text mobile applications." In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 57, no. 1, pp. 1869--1873. Sage CA: Los Angeles, CA: SAGE Publications, 2013.Google Scholar
- Y. K. Joo and J.-E. R. Lee, "Can 'The Voices in the Car' Persuade Drivers to Go Green?: Effects of Benefit Appeals from In-Vehicle Voice Agents and the Role of Drivers ' Affective States on Eco-Driving," Cyberpsychology Behav. Soc. Netw., vol. 17, no. 4, pp. 255--261, 2014.Google ScholarCross Ref
- Y. K. Joo and J.-E. R. Lee, "Can 'The Voices in the Car' Persuade Drivers to Go Green?: Effects of Benefit Appeals from In-Vehicle Voice Agents and the Role of Drivers' Affective States on Eco-Driving," Cyberpsychology Behav. Soc. Netw., vol. 17, no. 4, pp. 255--261, 2014.Google ScholarCross Ref
- Y. Liang and J. D. Lee, "A hybrid Bayesian Network approach to detect driver cognitive distraction," Transp. Res. Part C Emerg. Technol., vol. 38, pp. 146--155, Jan. 2014.Google ScholarCross Ref
- Y. Liang, M. L. Reyes, and J. D. Lee, "Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines," IEEE Trans. Intell. Transp. Syst., vol. 8, no. 2, pp. 340--350, Jun. 2007.Google ScholarDigital Library
Index Terms
- A Machine Learning Distracted Driving Prediction Model
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