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  3. Vo. 6, No. 3, August 2021
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Issue

Vo. 6, No. 3, August 2021

Issue Published : Aug 31, 2021
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Forensic Analysis of Braking Classification Based on Acceleration, Jerk, and Velocity Data

https://doi.org/10.22219/kinetik.v6i3.1284
Bayu Erfianto
Universitas Telkom
Andrian Rahmatsyah
Universitas Telkom

Corresponding Author(s) : Bayu Erfianto

erfianto@telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vo. 6, No. 3, August 2021
Article Published : Aug 31, 2021

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Abstract

Nowadays, four-wheeled vehicles are equipped with an event data recorder (EDR) device to record sensors data. With advances in-memory technology, EDR provides evidence for forensic analysis after an accident happens, that uses information technology to facilitate forensic analysis to provide complete and valuable results using digital investigations. Several types of research have been conducted to reconstruct accidents from forensic data and Fuzzy Logic is an alternative method for classifying crash data taken from the accelerometer due to less complexity of implementation. Vehicle braking data is one of the most important evidence for digital investigation, since braking is a complex process determined by many factors, such as the condition of the vehicle, road construction, and the driver’s physiological condition. However, the existing digital investigation still process vehicle speed, deceleration, and varia- tion time of deceleration (known as a jerk) in separated manner to determine braking distance, driver response time, and braking category. The problem identified in this paper is how to use deceleration, velocity, and jerk to categorize the braking evidence forensic analysis. In this paper, forensic analysis is limited to produce forensic evident of braking events based on the collected data. The contribution of this paper is to propose a braking detection model by combining acceleration, speed, and jerk data into a Fuzzy Inference System. As a result, a forensic analysis of braking data can better understand the braking maneuvers, which can be further developed to identify the cause of the accident and provide recommendations on which actions to include in future analyses.

Keywords

Acceleration jerk velocity brake forensic analysis
Erfianto, B., & Rahmatsyah, A. (2021). Forensic Analysis of Braking Classification Based on Acceleration, Jerk, and Velocity Data. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1284
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References
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References


V. Dobromirov, S. Dotsenko, V. Verstov, and S. Volkov, “Methods of examining vehicle electronic systems in the course of automotive forensic expert examinations,” Transportation Research Procedia, vol. 20, pp. 143–150, 2017. https://doi.org/10.1016/j.trpro.2017.01.037

D. Connolly, “Event data recorder as a forensic tool,” in Proceedings of the ITRN, 2014.

T. Hoppe, S. Kuhlmann, S. Kiltz, and J. Dittmann, “IT-forensic automotive investigations on the example of route reconstruction on automotive system and communication data,” in International Conference on Computer Safety, Reliability, and Security. Springer, 2012, pp. 125–136. https://doi.org/10.1007/978-3-642-33678-2_11

J. S. Daily, N. Singleton, E. Downing, and G. W. Manes, “The forensics aspects of event data recorders,” Journal of Digital Forensics, Security and Law, vol. 3, no. 3, p. 2, 2008. https://doi.org/10.15394/jdfsl.2008.1044

J. C. Castellanos, A. A. Susin, and F. Fruett, “Embedded sensor system and techniques to evaluate the comfort in public transportation,” in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2011, pp. 1858–1863.https://doi.org/ 10.1109/ITSC.2011.6083051

D. Le Nguyen, M.-E. Lee, and A. Lensky, “The design and implementation of new vehicle black box using the obd information,” in Computing and Convergence Technology (ICCCT), 2012 7th International Conference on. IEEE, 2012, pp. 1281–1284.

A. X. A. Sim and B. Sitohang, “OBD-II standard car engine diagnostic software development,” in Data and Software Engineering (ICODSE), 2014 International Conference on. IEEE, 2014, pp. 1–5. https://doi.org/10.1109/ICODSE.2014.7062704

J.-S. Jhou, S.-H. Chen, W.-D. Tsay, and M.-C. Lai, “The implementation of obd-ii vehicle diagnosis system integrated with cloud computation technology,” in Robot, Vision and Signal Processing (RVSP), 2013 Second International Conference on. IEEE, 2013, pp. 9–12. http://doi.org/10.1109/RVSP.2013.55

S. G. R. J. Naieni, A. Makui, and R. Ghousi, “An approach for accident forecasting using fuzzy logic rules: A case mining of lift truck accident forecasting in one of the Iranian car manufacturers,” International Journal of Industrial Engineering, vol. 23, no. 1, pp. 53–64, 2012.

B. B. Munyazikwiye, H. R. Karimi, and K. G. Robbersmyr, “Fuzzy logic approach to predict vehicle crash severity from acceleration data,” in Fuzzy Theory and Its Applications (iFUZZY), 2015 International Conference on. IEEE, 2015, pp. 44–49. https://doi.org/10.1109/iFUZZY.2015.7391892

A. Fasanmade, Y. He, A. H. Al-Bayatti, J. N. Morden, S. O. Aliyu, A. S. Alfakeeh, and A. O. Alsayed, “A fuzzy-logic approach to dynamic bayesian severity level classification of driver distraction using image recognition,” IEEE Access, vol. 8, pp. 95 197–95 207, 2020. https://doi.org/10.1109/ACCESS.2020.2994811

B. B. Munyazikwiye, “Mathematical modelling and analysis of vehicle frontal crash using lumped parameters models,” 2020.

A. Aljaafreh, N. Alshabatat, and M. S. N. Al-Din, “Driving style recognition using fuzzy logic,” in 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012). IEEE, 2012, pp. 460–463. https://doi.org/10.1109/ICVES.2012.6294318

M. Santos and V. Lo ́pez, “Fuzzy decision system for safety on roads,” in Handbook on Decision Making. Springer, 2012, pp. 171–187. https://doi.org/10.1007/978-3-642-25755-1_9

Z. Halim, R. Kalsoom, S. Bashir, and G. Abbas, “Artificial intelligence techniques for driving safety and vehicle crash prediction,” Artificial Intelligence Review, vol. 46, no. 3, pp. 351–387, 2016. https://doi.org/10.1007/s10462-016-9467-9

I. S. Feraud, M. M. Lara, and J. E. Naranjo, “A fuzzy logic model to estimate safe driving behavior based on traffic violation,” in 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM). IEEE, 2017, pp. 1–6. https://doi.org/ 10.1109/ETCM.2017.8247536

O. Bagdadi, “Assessing safety critical braking events in naturalistic driving studies,” Transportation re- search part F: traffic psychology and behaviour, vol. 16, pp. 117–126, 2013. https://doi.org/10.1016/j.trf.2012.08.006

N. Dapzol, “Drivers behaviour modelling using the hidden markov model formalism,” in ECTRI Young researchers seminar, The Hague, the Netherlands, vol. 2, no. 2.2, 2005, pp. 2–1.

N. Kudarauskas, “Analysis of emergency braking of a vehicle,” Transport, vol. 22, no. 3, pp. 154–159, 2007. https://doi.org/10.1080/16484142.2007.9638118

S.DeGroot, J.DeWinter, P.Wieringa,and M.Mulder, “An analysis of braking measures,”in Proceedings Driving Simulation Conference 2009, Monaco, 2010.

B. Cheng, Q. Lin, T. Song, Y. Cui, L. Wang, and S. Kuzumaki, “Analysis of driver brake operation in near-crash situation using naturalistic driving data,” International Journal of Automotive Engineering, vol. 2, no. 4, pp. 87–94, 2011. https://doi.org/10.20485/jsaeijae.2.4_87

Z. Wu, Y. Liu, and G. Pan, "A smart car control model for brake comfort based on car following," IEEE transactions on intelligent transportation systems, vol. 10, no. 1, pp. 42-46, 2008. https://doi.org/ 10.1109/TITS.2008.2006777

X. Xiang, W. Qin, and B. Xiang, “Research on a dsrc-based rear-end collision warning model,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 3, pp. 1054–1065, 2014.https://doi.org/10.1109/TITS.2013.2293771

S. Huo, L. Yu, L. Ma, and L. Zhang, “Ride comfort improvement in post-braking phase using active suspension,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 57168. American Society of Mechanical Engineers, 2015, p. V006T10A072. https://doi.org/10.1115/DETC2015-46878

S. P. Deligianni, M. Quddus, A. Morris, A. Anvuur, and S. Reed, “Analyzing and modeling drivers deceleration behavior from normal driving,” Transportation research record, vol. 2663, no. 1, pp. 134–141, 2017. https://doi.org/10.3141/2663-17

H. Bellem, B. Thiel, M. Schrauf, and J. F. Krems, “Comfort in automated driving: An analysis of pref- erences for different automated driving styles and their dependence on personality traits,” Transportation research part F: traffic psychology and behaviour, vol. 55, pp. 90–100, 2018. https://doi.org/10.1016/j.trf.2018.02.036

O. Bagdadi and A. Varhelyi, “Jerky drivingan indicator of accident proneness?” Accident Analysis & Prevention, vol. 43, no. 4, pp. 1359–1363, 2011. https://doi.org/10.1016/j.aap.2011.02.009

O. Bagdadi and A. Varhelyi ,“Development of a method for detecting jerks in safety critical events,” Accident Analysis & Pre- vention, vol. 50, pp. 83–91, 2013. https://doi.org/10.1016/j.aap.2012.03.032

J. Cao, H. Lu, K. Guo, and J. Zhang, "A driver modeling based on the preview-follower theory and the jerky dynamics," Mathematical Problems in Engineering, vol. 2013, 2013. https://doi.org/10.1155/2013/952106

Pan, Chao and Zhang, Ruifu and Luo, Hao and Shen, Hua,” Baseline correction of vibration acceleration signals with inconsistent initial velocity and displacement, ” Advances in Mechanical Engineering, vol. 8, no 10, 2016. https://doi.org/10.1177/1687814016675534

P. S. Bokare and A. K. Maurya, “Acceleration-deceleration behaviour of various vehicle types,” Trans- portation research procedia, vol. 25, pp. 4733–4749, 2017. https://doi.org/10.1016/j.trpro.2017.05.486

B. Wolshon, A. Pande et al., Traffic engineering handbook. John Wiley & Sons, 2016.

A.AASHTO,“Policy on geometric design of highways and streets,”American Association of State Highway and Transportation Officials, Washington, DC, vol. 1, no. 990, p. 158, 2001.

A. S. Zeeman and M. J. Booysen, “Combining speed and acceleration to detect reckless driving in the informal public transport industry,” in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013). IEEE, 2013, pp. 756–761. https://doi.org/10.1109/ITSC.2013.6728322

C. Naude, T. Serre, M. Dubois-Lounis, J.-Y. Fournier, D. Lechner, M. Guilbot, and V. Ledoux, “Acquisition and analysis of road incidents based on vehicle dynamics,” Accident Analysis & Prevention, vol. 130, pp. 117–124, 2019. https://doi.org/10.1016/j.aap.2017.02.021

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