Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model

Zdravković, Stefan; Vujanovic, Davor; Stokic, Marko; Pamučar, Dragan

doi:10.1007/s00521-021-05823-z

Cited by 9 publications

(10 citation statements)

References 58 publications

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“…the ACC parameter, is also calculated. The input score of the driver's driving style (the driver's input score -DIS) for machine learning is obtained in every second based on the ES, APP and ACC parameters and using the FLS2 model, according to the paper [16]. Simultaneously, DIS values ranging from 1 to 10 are possible (the score 1 being the worst, and the score 10 being the best).…”

Section: Figure 1: the Schematic Presentation Of The Road Section Rating And The Driver's Driving Style Rating Methodologymentioning

confidence: 99%

“…They determined that the higher the score for the driver (a passive driving style), the smaller specific fuel consumption (in l/100 km), and vice versa. In the research study conducted in [16], a fuzzy logic system type-2 (FLS2) model was developed by using the following vehicle parameters: the engine speed (ES), vehicle acceleration/deceleration (ACC) and the accelerator pedal position (APP). The same authors established a fact that, apart from the performed "eco-driving" training course, there were yet significant differences in the achieved driving scores that had caused an increase in specific fuel consumption by as much as 52.64%, the other fuel consumption influential factors (such as traffic conditions, terrain conditions and so on) simultaneously being brought to an equal footing with each other.…”

Section: Introductionmentioning

confidence: 99%

“…the slope of a road section (SRS) (in %) and average vehicle speed in a timeframe (ASTF) (in km/h) were determined. According to the paper [16], the parameters that trustworthily describe the driver's driving style are as follows: the engine speed -ES (rpm), the accelerator pedal position -APP (%) and the vehicle acceleration/deceleration -ACC (m/s 2 ). A methodology for the evaluation of a road section and the driver's driving style from the aspect of energy efficiency was developed using machine learning.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

et al. 2022

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In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver?s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver?s worse driving performances, and also to determine when the driver?s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284 014 of the instances of the data about the defined parameters of the road section and the driver?s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver?s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission.

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Section: Figure 1: the Schematic Presentation Of The Road Section Rating And The Driver's Driving Style Rating Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

et al. 2022

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“…In fact, the boundaries between driving styles is not clear. It is reasonable to use fuzzy theory to study driving styles and energy-saving driving levels [ 75 , 76 ]. Select driving parameters (accelerator pedal position, acceleration, and deceleration) have a significant impact on energy consumption, and based on fuzzy theory, divide the driving style into energy-saving, good, qualified, and energy-consuming.…”

Section: Eco-driving Capability Evaluationmentioning

confidence: 99%

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

et al. 2021

Sensors

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Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.

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“…Fuzzy Logic) za dobijanje ocene vozačevog stila vožnje kod putničkih automobila, pri čemu su utvrdili da što je ocena vozača bolja to je specifična potrošnja goriva (u l/100 km) njihovih vozila manja, i obrnuto. U radu [10] predložen je model baziran na fazi logičkom sistemu tipa 2 (engl. Fuzzy Logic System Type-2, FLS2) za utvrđivanje uticaja različitog stila vožnje profesionalnih vozača na potrošnju goriva, pri čemu su upotrebljeni sledeći parametri vožnje: broj obrtaja motora, ubrzanje/usporenje vozila i procenat pritiska papučice gasa.…”

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Assessment of the driver's driving style using supervised machine learning

Janković¹,

Vujanovic²,

Zdravković³

et al. 2022

Tehnika

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Supervised machine learning can be an effective method of predicting a target variable, depending on current values of independent attributes, if historical data containing values of independent attributes and target variables in the past is available and if some machine learning algorithm gives good results on the available data set. In this research, the aim was to show whether the method of supervised machine learning can be successfully applied in assessing the driver's driving style in terms of fuel consumption, if as independent attributes, i.e. parameters that affect the assessment of driving take: engine speed, percentage of accelerator pedal pressure and vehicle acceleration. Training, validation, testing and application of machine learning models were performed in the Weka software tool. The following seven machine learning algorithms were applied to the data sets for model training and testing: LinearRegression, MultilayerPerceptron, IBk (k-nearest neighbors), M5P, Random Forest, Random Tree and REPTree. The best performance was shown by models based on the IBk and Random Forest algorithms. As a final result of this research, predicted scores of the driver's driving style in the interval of one second were obtained. The final score of the driver's driving style was calculated as the arithmetic mean of the predicted driving scores for each second.

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Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model

Cited by 9 publications

References 58 publications

Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

Assessment of the driver's driving style using supervised machine learning

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