Design and Implementation of a New Algorithm to Tachographs for Driving Behavior Detection and Classification

Year 2022, , 389 - 400, 31.10.2022

Cevat Altunkaya , Ahmet Zengin

https://doi.org/10.17671/gazibtd.1079364

Abstract

Today, as the number of vehicles on the roads increases, the importance of traffic safety increases. Traffic accidents involving more than one vehicle threaten the lives of drivers and passengers. Heavy-duty vehicles such as trucks and buses, for which the use of tachographs is mandatory, cause more deaths, injuries and financial losses in traffic accidents. Conventional tachographs do not provide any information on the severity of lateral maneuvers such as turns and lane changes. The effects of aggressive driver behaviors on traffic accidents and fuel consumption can be significant. In this study, a new algorithm was designed and a new tachograph device was developed to detect and rate driver behavior using accelerometer and gyroscope data. For this purpose, a low-cost IMU (Inertial Measurement Unit) sensor module has been added to the tachograph device to detect lateral maneuvers. In order to detect right-left turn and lane change maneuvers with high precision, first the edges of the events are captured with the gyroscope-Z data and the start and end points are determined. Then, a new algorithm that scores lateral maneuvers is proposed by combining the accelerometer-X, gyroscope-Z and speed data in the range with a formula. In the tests performed, it was observed that the algorithm approached 100% accuracy in detecting turns and 88% accuracy in lane changes.

Keywords

driving maneuver detection, aggressive driving, driving scoring, tachograph, sensors

Sürüş Davranış Tespiti ve Tasnifi için Takograflara Yönelik Yeni Bir Algoritma Tasarımı ve Uygulaması

Abstract

Günümüzde yollardaki araç sayısı arttıkça trafik güvenliğinin önemi de artmaktadır. Birden çok aracın karıştığı trafik kazaları sürücü ve yolcuların hayatını tehdit etmektedir. Takograf kullanımı zorunlu olan kamyon, otobüs gibi ağır vasıta araçlar, trafik kazalarında daha fazla ölüm, yaralanma ve maddi kayıplara yol açmaktadır. Geleneksel takograflar dönüş ve şerit değişiklikleri gibi yanal manevraların şiddetiyle ilgili herhangi bir bilgi vermemektedir. Agresif sürücü davranışlarının trafik kazaları ve yakıt tüketimine etkisi önemli boyutlarda olabilmektedir. Bu çalışmada ivmeölçer ve jiroskop verilerini kullanarak sürücü davranışını tespit etmek ve derecelendirmek için yeni bir algoritma tasarlanmış ve yeni bir takograf cihazı geliştirilmiştir. Bu amaçla, yanal manevraların algılanması için takograf cihazına düşük maliyetli bir IMU (Inertial Measurement Unit) sensör modülü eklenmiştir. Sağa-sola dönüşler ve şerit değişiklikleri manevralarını yüksek hassasiyetle tespit etmek için öncelikle jiroskop-Z verileri ile olayların kenarları yakalanır ve başlangıç ve bitiş noktaları belirlenir. Ardından aralıktaki ivmeölçer-X, jiroskop-Z ve hız verilerini bir formülle birleştirilerek yanal manevraları puanlayan yeni bir algoritma önerilmiştir. Yapılan testlerde algoritmanın dönüşlerin algılanmasında %100, şerit değişikliklerinde %88 doğruluğa yaklaştığı gözlemlenmiştir.

Keywords

sürüş manevra algılama, agresif sürüş, sürüş puanlama, takograf, sensörler

References

• Internet: T.S. Institute, https://data.tuik.gov.tr/Bulten/Index?p=Karayolu-Trafik-Kaza-Istatistikleri-2019-33628., 19.5.2021.
• Internet: C. I. R. (EU), Implementing Regulation (EU) No 165/2014 of the European Parliament and of the Council laying down the requirements for the construction, testing, installation, operation and repair of tachographs and their components, https://www.legislation.gov.uk/eur/2016/799/pdfs/eur_20160799_2018-04-17_en.pdf., 19.5.2021.
• Z. Ouyang, J. Niu, Y. Liu ve J. Rodrigues, "Multiwave: A Novel Vehicle Steering Pattern Detection Method based on Smartphones", IEEE International Conference on Communications (ICC), Kuala Lumpur, Malezya, 1-7, 22-27 Mayıs 2016.
• T. Pholprasit, W. Choochaiwattana ve C. Saiprasert, "A Comparison of Driving Behaviour Prediction Algorithm Using Multi-Sensory Data on a Smartphone", IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Takamatsu, Japonya, 1-6, 1-3 Haziran 2015.
• D. A. Johnson ve M. M. Trivedi, "Driving style recognition using a smartphone as a sensor platform", 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, ABD, 1609-1615, 5-7 Ekim 2011.
• J. Engelbrecht, M. J. Booysen, G-J. van Rooyen ve F. Bruwer, "Performance Comparison of Dynamic Time Warping (DTW) and a Maximum Likelihood (ML) Classifier in Measuring Driver Behavior with Smartphones", IEEE Symposium Series on Computational Intelligence, Cape Town, Güney Afrika, 427-433, 7-10 Aralık 2015.
• B. Fernandes, V. Gomes, J. Ferreira ve A. Oliveira, "Mobile Application for Automatic Accident Detection and Multimodal Alert", IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, Birleşik Krallık, 1-5, 11-14 Mayıs 2015.
• H. Eren, S. Makinist, E. Akin ve A. Yilmaz, "Estimating Driving Behavior by a Smartphone", Intelligent Vehicles Symposium, Alcalá de Henares, İspanya, 234-239, 3-7 Haziran 2012.
• Y. Wang, T. Zhao, F. Tahmasbi, J. Cheng, Y. Chen ve J. Yu, "Driver Identification Leveraging Single-turn Behaviors via Mobile Devices", 29th International Conference on Computer Communications and Networks (ICCCN), Honolulu, HI, ABD, 1-9, 3-6 Ağustos 2020.
• F. Li, H. Zhang, H. Che ve X. Qiu, "Dangerous Driving Behavior Detection Using Smartphone Sensors", IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brezilya, 1902-1907, 1-4 Kasım 2016.
• J. Paefgen, F. Kehr, Y. Zhai ve F. Michahelles, "Driving Behavior Analysis with Smartphones: Insights from a Controlled Field Study" Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, Ulm, Almanya, 1-8, 4 Aralık 2012.
• Y. A. Alqudah ve B. H. Sababha, "A Statistical Approach to Estimating Driving Events by a Smartphone", International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, ABD, 1021-1025, 15-17 Aralık 2016.
• X. Liu, H. Mei, H. Lu, H. Kuang ve X. Ma, "A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors", MPDI sensors, 17(3), 633, 2017.
• S. Daptardar, V. Lakshminarayanan, S. Reddy, S. Nair, S. Sahoo ve P. Sinha, "Hidden Markov Model based Driving Event Detection and Driver Profiling from Mobile Inertial Sensor Data", IEEE Sensors, Busan, Kore (Güney), 1-4, 1-4 Kasım 2015.
• A. Bhatt, V. Dave, Y. Panchamia ve P. P. Thakre, "Analyzing Behavioral Attributes of Drivers and Implementing Safe Driving Model", IEEE International Conference on Vehicular Electronics and Safety (ICVES), Viyana, Avusturya, 228-232, 27-28 Haziran 2017.
• H. R. Eftekhari ve M. Ghatee, "Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition", Transportation Research Part F: Traffic Psychology and Behaviour, 58(1), 782-796, 2018.
• G. Castignani, T. Derrmann, R. Frank ve T. Engel, "Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring", IEEE Intelligent Transportation Systems Magazine, 7(1), 91-102, 2015.
• R. Sun, Q. Cheng, F. Xie, W. Zhang, T. Lin ve W. Y. Ochieng, "Combining Machine Learning and Dynamic Time Wrapping for Vehicle Driving Event Detection Using Smartphones", IEEE Transactıons On Intellıgent Transportatıon Systems, 22(1), 194-207, 2019.
• E. G. Mantouka, E. N. Barmpounakis ve E. I. Vlahogianni, "Identification of driving safety profiles from smartphone data using machine learning techniques", Safety Science, 119, 84-90, 2019.
• Y.-C. Tsai, W.-H. Lee ve C.-M. Chou, "A Safety Driving Assistance System by Integrating In-Vehicle Dynamics and Real-Time Traffic Information", IEEE 8th International Conference on Awareness Science and Technology, Taichung, Tayvan, 416-421, 8-10 Kasım 2017.
• K. B. Ahmed, B. Goel, P. Bharti, S. Chellappan ve M. Bouhorma, "Leveraging Smartphone Sensors to Detect Distracted Driving Activities", IEEE Transactıons On Intellıgent Transportatıon Systems, 20(9), 3303-3312, 2019.
• L. Lamel, L. Rabiner, A. Rosenberg ve J. Wilpon "An improved endpoint detector for isolated word recognition", IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(4), 777-785, 1981.
• M. S. N. Al-Din, "Calibration and Pre-Processing Techniques for A Smartphone-Based Driving Events Identification and Classification System", IEEE Electron Device Kolkata Conference, Kolkata, Hindistan, 396-402, 24-25 Kasım 2018.
• Internet: STMicroelectronics, Ultra-compact high-performance eCompass module: 3D accelerometer and 3D magnetometer, https://www.st.com/resource/en/datasheet/lsm303dlhc.pdf., 19.5 .2021.
• Internet: STMicroelectronics, MEMS motion sensor: three-axis digital output gyroscope, https://www.st.com/en/mems-and-sensors/l3gd20.html., 19.5.2021.
• Internet: B. S. GmbH, BMP180 Digital pressure sensor, https://cdn-shop.adafruit.com/datasheets/BST-BMP180-DS000-09.pdf., 19.5.2021.
• S. Fadadu, S. Pandey, D. Hegde, Y. Shi, F. C. Chou, N. Djuric ve C. V. Gonzales, "Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving", 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, ABD, 3292-3300, 3-8 Ocak 2022.
• G. Yenikaya, E. Düven, A. Üzgeç ve E. Yürüklü, "Sürücü Davranış Karakteristiklerinin Tanılanmasi İçin Görü Temelli Bir Sürüş Sisteminin Tasarlanması", OTEKON 2010, 3 Haziran 2010.
• Ö. Kumtepe, E. Yüncü ve G. B. Akar, "Agresif Sürüş Tespiti İçin Çok Modlu Bir Yaklaşım", 2016 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 3292-3300, 729-732 Mayıs 2016.
• M. H. Z. M. Fodli, F. H. K. Zaman, N. K. Mun ve L. Mazalan " Driving Behavior Recognition using Multiple Deep Learning Models", 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), Selangor, Malaysia, 138-143, 12 Mayıs 2022.
• A.Ç. Seçkin, "Otonom Araçların Görsel Eğitimi için EEG, EMG ve IMU ile Etiketleme Sistemi", Bilişim Teknolojileri Dergisi, 12(4), 299-305, 2019.