A new application for analyzing driving behaviour and environment characterization in transportation systems based on a fuzzy logic approach

Fazio, Peppino; Santamaria, Amilcare Francesco; Rango, Floriano De; Tropea, Mauro; Serianni, Abdon

doi:10.1117/12.2228432

Cited by 5 publications

(6 citation statements)

References 16 publications

(24 reference statements)

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“…The authors highlighted the limitations of traditional statistical analysis and proposed innovative methods to model the decision-making processes of pedestrians. Fazio et al [26] proposed a new platform for characterizing driving behaviour. They integrated onboard diagnosis data from the electronic vehicle control unit with a fuzzy logic approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Probabilistic Approach to Identify Driving Behavior of Two-Wheeler Drivers Under Mixed Traffic Conditions

Kumar Kushwaha,

Shinde,

Anil Kumar

2024

IEEE Access

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The identication and analysis of aggressive driving behaviour are crucial for enhancing road safety and preventing potential hazards on the roads. By closely examining patterns of aggressive behaviour, such as speeding and sudden lane changes, authorities can implement targeted interventions to mitigate risks and implement eective strategies for promoting safer driving habits and reduce the occurrence of aggressive driving incidents. Towards this, the present study comprehensively analyzes aggressive driving behaviour by leveraging the concepts of probabilistic theory and fuzzy set algorithms, which oer a powerful framework for modelling and analyzing uncertain and imprecise data. For the purpose of analysis, the present study collected naturalistic driving data on three fundamental parameters -speed, acceleration, and jerk, from two-wheeler drivers on a 75 km long study stretch in the vicinity of Patna city, India. By establishing relationships between acceleration, jerk, and speed values, the present study developed regression, classication and fuzzy probabilistic approaches for identifying the aggressive driving behaviour. Based on the ndings obtained from regression methods, it was noted that both random forest regression and decision tree regression methods demonstrated superior accuracy compared to linear regression. Specically, linear regression yielded an R 2 value of 0.08, indicative of subpar performance. Upon implementation of classication methods, it was observed that both decision tree and random forest classication were performing extremely well on identifying the aggressive behaviour with an accuracy of 99.17%, with an F1-score of 0.88. In order to address the dimensionality challenge, Principal Component Analysis (PCA) is employed to reduce the complexity of the problem, followed by the application of Hidden Markov Models (HMM) on the derived variables. The HMM-PCA approach, although limited by insucient data, showed the ability to identify non-aggressive patterns accurately and aggressive behaviour with nearly accurate accuracy. From these results, it can be observed that the proposed approach was able to eciently identify the aggressive driving behaviour. With the help of these insights, specic strategies may be created to lessen the negative consequences of aggressive driving on trac ow control and road safety, ultimately leading to the construction of a more eective and safe transportation system INDEX TERMS Road safety, Aggressive behaviour, Naturalistic driving data, two-wheeler.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Probabilistic Approach to Identify Driving Behavior of Two-Wheeler Drivers Under Mixed Traffic Conditions

Kumar Kushwaha,

Shinde,

Anil Kumar

2024

IEEE Access

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“…The same parameters were used in [30], while in [31] sudden acceleration and sudden turns are considered. In [32] authors implemented fuzzy logic to model driving behavior. However, their conclusions about driving style were based on the vehicle speed and acceleration measurement.…”

Section: Design Of Fis For Modeling Driver Behaviormentioning

confidence: 99%

“…Acceleration, velocity, and distance between the preceding and host car Fazio, Santamaria, De Rango, Tropea, and Serianni [32] 2016…”

Section: Design Of Fis For Modeling Driver Behaviormentioning

confidence: 99%

A Decision Support Model for Transportation Companies to Examine Driver Behavior

Čubranić-Dobrodolac

Švadlenka

Marković

et al. 2023

IEEE Trans. Eng. Manage.

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“…Authors in their study get a performance over 82%. Moreover, Fuzzy classification method is preferred in some of studies (Imkamon at al, 2008;Wu at al, 2012;Songkroh at al, 2014;Fazio at al, 2016;Arefnezhad at al, 2019;Wesseleny at al,2019). Determination of aggressive driving without CAN bus data is achieved by the studies in (Waitkus at al, 2014;Li at al, 2014;Vignali at al, 2019;de Naurois at al, 2019).…”

Section: Related Work and Contributionsmentioning

confidence: 99%

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

Karabuluter¹,

Karaduman²,

Karabatak³

et al. 2020

European Journal of Science and Technology

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

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A new application for analyzing driving behaviour and environment characterization in transportation systems based on a fuzzy logic approach

Cited by 5 publications

References 16 publications

Fuzzy Probabilistic Approach to Identify Driving Behavior of Two-Wheeler Drivers Under Mixed Traffic Conditions

Fuzzy Probabilistic Approach to Identify Driving Behavior of Two-Wheeler Drivers Under Mixed Traffic Conditions

A Decision Support Model for Transportation Companies to Examine Driver Behavior

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

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