Evaluating the Effect of Audio Feedback on the Behavior of Automotive Fatigue and Distraction Detection System Users
Resumo
Vehicular fatigue and distraction detection systems are important tools to avoid traffic accidents. Furthermore, there are many related papers proposing compositions using these techniques. Nevertheless, most of the validation tests performed with these devices happen in simulated conditions or environments, without a test with actual users on a real situation. Also, few works analyze behavioral features using these systems. Inthis work, we analyze the behavioral aspects of users from afatigue and distraction detection system with and without audiofeedback. Our results indicate that this feature has a positive effect on the drivers behavior.
Palavras-chave:
Applications, Verification, Validation and Test of Systems
Referências
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J. Koo D. Shin M. Steinert L. Leifer "Understanding driver responses to voice alerts of autonomous car operations" International journal of vehicle design vol. 70 no. 4 pp. 377-392 2016.
R. C. C. d. M. Santos R. A. O. Oliveira "Análise de desempenho de sistema embarcado de detecção de fadiga de condutores" 2014 IV Brazilian Symposium on Computing Systems Engineering (SBESC) 2014.
N. Dalal B. Triggs Histograms of oriented gradients for human detection 2005.
V. Kazemi J. Sullivan "One millisecond face alignment with an ensemble of regression trees" Proceedings of the IEEE conference on computer vision and pattern recognition pp. 1867-1874 2014.
M. Topi O. Timo P. Matti S. Maricor "Robust texture classification by subsets of local binary patterns" Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 vol. 3 pp. 935-938 2000.
W.-H. Liao "Region description using extended local ternary patterns" 2010 20th International Conference on Pattern Recognition pp. 1003-1006 2010.
D. E. King "Dlib-ml: A machine learning toolkit" Journal of Machine Learning Research vol. 10 pp. 1755-1758 2009.
F. V. Narciso M. T. d. Mello "Segurança e saúde dos motoristas profissionais que trafegam nas rodovias do brasil" Rev. Saúde Pública vol. 51 2017.
S. Nordbakke F. Sagberg "Sleepy at the wheel: Knowledge symptoms and behaviour among car drivers" Transportation Research Part F: Traffic Psychology and Behaviour vol. 10 no. 1 pp. 1-10 2007.
S. Smith M. Carrington J. Trinder "Subjective and predicted sleepiness while driving in young adults" Accident analysis & prevention vol. 37 no. 6 pp. 1066-1073 2005.
S. K. Lal A. Craig P. Boord L. Kirkup H. Nguyen "Development of an algorithm for an eeg-based driver fatigue countermeasure" Journal of safety Research vol. 34 no. 3 pp. 321-328 2003.
A. Kokonozi E. Michail I. Chouvarda N. Maglaveras "A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects" 2008 Computers in Cardiology pp. 969-971 2008.
R. Sayed A. Eskandarian "Unobtrusive drowsiness detection by neural network learning of driver steering" Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering vol. 215 no. 9 pp. 969-975 2001.
A. Eskandarian A. Mortazavi "Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection" 2007 IEEE Intelligent Vehicles Symposium pp. 553-559 2007.
F. Song X. Tan X. Liu S. Chen "Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients" Pattern Recognition vol. 47 no. 9 pp. 2825-2838 2014.
T. Wang P. Shi "Yawning detection for determining driver drowsiness" Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology 2005 pp. 373-376 2005.
P. Smith M. Shah N. da Vitoria Lobo "Determining driver visual attention with one camera" IEEE transactions on intelligent transportation systems vol. 4 no. 4 pp. 205-218 2003.
S. Abtahi B. Hariri S. Shirmohammadi "Driver drowsiness monitoring based on yawning detection" 2011 IEEE International Instrumentation and Measurement Technology Conference pp. 1-4 2011.
J. G. Gaspar T. L. Brown C. W. Schwarz J. D. Lee J. Kang J. S. Higgins "Evaluating driver drowsiness countermeasures" Traffic injury prevention vol. 18 no. sup1 pp. S58-S63 2017.
R. C. C. d. M. Santos R. A. O. Oliveira V. J. P. Amorim "Sistema de detecção de fadiga e desvio de atenção de condutores de veículos" 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC) 2018.
S. R. Gunn et al. "Support vector machines for classification and regression" ISIS technical report vol. 14 no. 1 pp. 5-16 1998.
S. A. Ahmed S. Dey K. K. Sarma "Image texture classification using artificial neural network (ann)" 2011 2nd National Conference on Emerging Trends and Applications in Computer Science pp. 1-4 2011.
D. Sommer M. Golz "Evaluation of perclos based current fatigue monitoring technologies" 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology pp. 4456-4459 2010.
J.-J. Yan H.-H. Kuo Y.-F. Lin T.-L. Liao "Real-time driver drowsiness detection system based on perclos and grayscale image processing" 2016 International Symposium on Computer Consumer and Control (IS3C) pp. 243-246 2016.
S. Park F. Pan S. Kang C. D. Yoo "Driver drowsiness detection system based on feature representation learning using various deep networks" Asian Conference on Computer Vision pp. 154-164 2016.
B. Mandal L. Li G. S. Wang J. Lin "Towards detection of bus driver fatigue based on robust visual analysis of eye state" IEEE Transactions on Intelligent Transportation Systems vol. 18 no. 3 pp. 545-557 2016.
E. E. Galarza F. D. Egas F. M. Silva P. M. Velasco E. D. Galarza "Real time driver drowsiness detection based on driver’s face image behavior using a system of human computer interaction implemented in a smartphone" International Conference on Information Theoretic Security pp. 563-572 2018.
K. Li S. Wang C. Du Y. Huang X. Feng F. Zhou "Accurate fatigue detection based on multiple facial morphological features" Journal of Sensors vol. 2019 2019.
F. Zhang J. Su L. Geng Z. Xiao "Driver fatigue detection based on eye state recognition" 2017 International Conference on Machine Vision and Information Technology (CMVIT) pp. 105-110 2017.
X. Liu T. Blaschke B. Thomas S. De Geest S. Jiang Y. Gao X. Li E. Buono S. Buchanan Z. Zhang et al. "Usability of a medication event reminder monitor system (merm) by providers and patients to improve adherence in the management of tuberculosis" International journal of environmental research and public health vol. 14 no. 10 pp. 1115 2017.
J. S. Jermakian S. Bao M. L. Buonarosa J. R. Sayer C. M. Farmer "Effects of an integrated collision warning system on teenage driver behavior" Journal of safety research vol. 61 pp. 65-75 2017.
J. Koo D. Shin M. Steinert L. Leifer "Understanding driver responses to voice alerts of autonomous car operations" International journal of vehicle design vol. 70 no. 4 pp. 377-392 2016.
R. C. C. d. M. Santos R. A. O. Oliveira "Análise de desempenho de sistema embarcado de detecção de fadiga de condutores" 2014 IV Brazilian Symposium on Computing Systems Engineering (SBESC) 2014.
N. Dalal B. Triggs Histograms of oriented gradients for human detection 2005.
V. Kazemi J. Sullivan "One millisecond face alignment with an ensemble of regression trees" Proceedings of the IEEE conference on computer vision and pattern recognition pp. 1867-1874 2014.
M. Topi O. Timo P. Matti S. Maricor "Robust texture classification by subsets of local binary patterns" Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 vol. 3 pp. 935-938 2000.
W.-H. Liao "Region description using extended local ternary patterns" 2010 20th International Conference on Pattern Recognition pp. 1003-1006 2010.
D. E. King "Dlib-ml: A machine learning toolkit" Journal of Machine Learning Research vol. 10 pp. 1755-1758 2009.
Publicado
19/11/2019
Como Citar
SANTOS, Ricardo; SILVA, Mateus; OLIVEIRA, Ricardo R..
Evaluating the Effect of Audio Feedback on the Behavior of Automotive Fatigue and Distraction Detection System Users. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 9. , 2019, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 97-104.
ISSN 2237-5430.
