Haluk EREN, Murat KARABATAK, Berat KARABULUTER, Özgür KARADUMAN

CAN-bus Verileri Kullanarak Agresif Sürüş Tespiti için Temel Sınıflandırma Algoritmalarının Performans Değerlendirmesi

Uykulu, dikkati dağılmış, dikkatli, sakin ya da agresif sürüş gibi sürüş tarzıyla ilişkili olan sürücü ruh hallerinin tespiti İleri SürücüDestek Sistemlerinin (İSDS) ana problemlerinden biridir ve trafik kazalarının önlenmesinde hayati rol oynamaktadır. Bu çalışmanıntemel amacı, CAN-bus sensör verilerini kullanarak sürücü ruh halini veya sürüş tarzını anlamanın temel problemlerinden birisi olan agresif sürüş tespiti yapmak için Eğiticili Öğrenme Tabanlı Sınıflandırma Algoritmalarının (EÖSAs) performanslarını karşılaştırmaktır.Bu algoritmalar, aracın OBDII soketinden elde edilen CAN-bus verilerini kullanır. Deneylerde, referans verilerini elde etmek için agresifve sakin sürüşe ilişkin bir çok deneme sürüşü farklı sürücüler tarafından gerçekleştirilmiştir. Elde edilen veriler “agresif” ve “sakin” olarak etiketlenmiştir. Ardından, EÖSA’ ların performansını değerlendirmek üzere eğitim verilerine dönüştürülmüştür. Yapılanperformans değerlendirmesi sonucunda, Naïve Bayes sınıflandırıcısının diğerlerinden daha başarılı olduğu görülmüştür.

Performance Evaluation of Major Classification Algorithms forAggressive Driving Detection using CAN-bus Data

Detection of driver moods associated to driving style such as drowsy, distracted, vigilant, calm, or aggressive driving is one of the mainproblems of Advanced Driver Assistance Systems and it obviously plays vital role in the prevention of traffic accidents. The main goalof this study is to compare the performances of major Supervised Learning based Classification Algorithms (SLCAs) for aggressivedriving detection, which is one of the fundamental problems for understanding driver mood or driving style through CAN (Control AreaNetwork) bus sensor data. These algorithms utilize CAN-bus data acquired by OBDII (On-board Diagnostics) socket of the vehicle. Inour experiments, to get ground truth data, many trials referring to aggressive and calm driving have been conducted by different subjectdrivers and these sensor data have been labeled as “aggressive” and “calm”. Afterwards, these transformed into training data to assessperformances of SLCAs. As a result, the Naïve Bayes Classifier has been found to be more successful than the others.

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https://www.safemotorist.com/articles/road_rage.aspx (Last access 06.12.2020)
https://www.iii.org/fact-statistic/facts-statistics-aggressivedriving (06.12.2020)
https://aaafoundation.org/2015-traffic-safety-culture-index/ (06.12.2020)
http://www.tuik.gov.tr/ (06.12.2020)
Kumtepe, Ö., Akar, G. B., & Yüncü, E. (2015). On Vehicle Aggressive Driving Behavior Detection Using Visual Information. In 2015 Signal Processing and Communications Applications Conference (SIU) (pp. 795-798).
Kumtepe, Ö., Yüncü, E., & Akar, G. B. (2016). A Multimodal Approach for Aggressive Driving Detection. In 2016 Signal Processing and Communication Application Conference (pp. 729-732).
Johnson, D. A., & Trivedi, M. M. (2011, October). Driving style recognition using a smartphone as a sensor platform. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 1609-1615). IEEE.
Eren, H., Makinist, S., Akin, E., & Yilmaz, A. (2012, June). Estimating driving behavior by a smartphone. In 2012 IEEE Intelligent Vehicles Symposium (pp. 234-239). IEEE.
Bergasa, L. M., Almería, D., Almazán, J., Yebes, J. J., & Arroyo, R. (2014, June). Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors. In 2014 IEEE Intelligent Vehicles symposium proceedings (pp. 240-245). IEEE.
Koh, D. W., & Kang, H. B. (2015, June). Smartphone-based modeling and detection of aggressiveness reactions in senior drivers. In 2015 IEEE Intelligent Vehicles Symposium (IV) (pp. 12-17). IEEE.
Li, F., Zhang, H., Che, H., & Qiu, X. (2016, November). Dangerous driving behavior detection using smartphone sensors. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1902-1907). IEEE.
Roider, O., Wegener, S., Stark, J., Judmaier, P., Michelberger, F., & Barberi, A. (2019). Merging Virtual World with Real-Life Behavior: A Concept for a Smartphone App to Influence Young People’s Travel Behavior. Transportation research record, 2673(4), 241-250.
Imkamon, T., Saensom, P., Tangamchit, P., & Pongpaibool, P. (2008, May). Detection of hazardous driving behavior using fuzzy logic. In 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (Vol. 2, pp. 657-660). IEEE.
Wu, B. F., Chen, Y. H., & Yeh, C. H. (2012, November). Fuzzy logic based driving behavior monitoring using hidden markov models. In 2012 12th International conference on ITS telecommunications (pp. 447-451). IEEE.
Songkroh, A., Fooprateepsiri, R., & Lilakiataskun, W. (2014, June). An intelligent risk detection from driving behavior based on BPNN and Fuzzy Logic combination. In 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS) (pp. 105-110). IEEE.
Fazio, P., Santamaria, A. F., De Rango, F., Tropea, M., & Serianni, A. (2016, May). A new application for analyzing driving behaviour and environment characterization in transportation systems based on a fuzzy logic approach. In Unmanned Systems Technology XVIII (Vol. 9837, p. 983707). International Society for Optics and Photonics.
Arefnezhad, S., Samiee, S., Eichberger, A., & Nahvi, A. (2019). Driver drowsiness detection based on steering wheel data applying adaptive neuro-fuzzy feature selection. Sensors, 19(4), 943.
Vesselenyi, T., Rus, A., Mitran, T., Moca, S., & Lehel, C. (2019, October). Fuzzy Decision Algorithm for Driver Drowsiness Detection. In SIAR International Congress of Automotive and Transport Engineering: Science and Management of Automotive and Transportation Engineering (pp. 458-467). Springer, Cham.
Vaitkus, V., Lengvenis, P., & Žylius, G. (2014, September). Driving style classification using long-term accelerometer information. In 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 641-644). IEEE.
Li, Y., Miyajima, C., Kitaoka, N., & Takeda, K. (2014, October). Measuring aggressive driving behavior using signals from drive recorders. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 1886-1887). IEEE.
Vignali, V., Bichicchi, A., Simone, A., Lantieri, C., Dondi, G., & Costa, M. (2019). Road sign vision and driver behaviour in work zones. Transportation research part F: traffic psychology and behaviour, 60, 474-484.
de Naurois, C. J., Bourdin, C., Stratulat, A., Diaz, E., & Vercher, J. L. (2019). Detection and prediction of driver drowsiness using artificial neural network models. Accident Analysis & Prevention, 126, 95-104.
Doshi, A., & Trivedi, M. M. (2010, June). Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis. In 2010 IEEE Intelligent Vehicles Symposium (pp. 232-237). IEEE.
Gregoriades, A., Florides, C., Lesta, V. P., & Pampaka, M. (2013, October). Driver behaviour analysis through simulation. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 3681-3686). IEEE.
Shirazi, M. M., & Rad, A. B. (2014). Detection of intoxicated drivers using online system identification of steering behavior. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1738-1747.
Keklikoglou, A., Fitzpatrick, C. D., & Knodler, M. A. (2018). Investigation of time and speed perception using a driving simulator. Transportation research record, 2672(37), 132- 140.
Dia, H., & Panwai, S. (2015, December). Impact of Driving Behaviour on Emissions and Road Network Performance. In 2015 IEEE International Conference on Data Science and Data Intensive Systems (pp. 355-361). IEEE.
Sun, J., Zhen, A., Li, C., Zhang, M., & Hu, X. (2016, October). A vehicles' CO 2 emission monitoring platform combined with driver's driving behavior. In 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) (pp. 1-2). IEEE.
Stogios, C., Kasraian, D., Roorda, M. J., & Hatzopoulou, M. (2019). Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions. Transportation Research Part D: Transport and Environment, 76, 176-192.
Faria, M. V., Duarte, G. O., Varella, R. A., Farias, T. L., & Baptista, P. C. (2019). Driving for decarbonization: assessing the energy, environmental, and economic benefits of less aggressive driving in Lisbon, Portugal. Energy Research & Social Science, 47, 113-127.
Karaduman, O., Eren, H., Kurum, H., & Celenk, M. (2013, December). An effective variable selection algorithm for Aggressive/Calm Driving detection via CAN bus. In 2013 International Conference on Connected Vehicles and Expo (ICCVE) (pp. 586-591). IEEE.
Taylor, A., Japkowicz, N., & Leblanc, S. (2015, December). Frequency-based anomaly detection for the automotive CAN bus. In 2015 World Congress on Industrial Control Systems Security (WCICSS) (pp. 45-49). IEEE.
Fugiglando, U., Massaro, E., Santi, P., Milardo, S., Abida, K., Stahlmann, R., ... & Ratti, C. (2018). Driving behavior analysis through CAN bus data in an uncontrolled environment. IEEE Transactions on Intelligent Transportation Systems, 20(2), 737-748.
Lokman, S. F., Othman, A. T., Bakar, M. H. A., & Musa, S. (2019, July). The Impact of Different Feature Scaling Methods on Intrusion Detection for in-Vehicle Controller Area Network (CAN). In International Conference on Advances in Cyber Security (pp. 195-205). Springer, Singapore.
Le, Q., Jiang, K., & Zhang, F. (2020). Design of automatic detection system for vehicle networking communication abnormal data based on CAN bus. International Journal of Information and Communication Technology, 16(2), 123- 139.
Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006, December). Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence (pp. 1015-1021). Springer, Berlin, Heidelberg.