A framework on driving behavior and pattern using On-Board diagnostics (OBD-II) tool

Malik, Meenakshi; Nandal, Rainu

doi:10.1016/j.matpr.2021.07.376

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…The prediction accuracy reaching a maximum of 98%. (Malik & Nandal, 2021) presents a framework for analysing driving behaviour and patterns using the OBD-II tool, which is a standard diagnostic tool available in most vehicles. The study focuses on leveraging speed and velocity sensors to analyse driving behaviour.…”

Section: Speed and Velocity Sensorsmentioning

confidence: 99%

“…Hidden Markov Model (HMM) and Recurrent Neural Classification Method achieved an accuracy of 98% using throttle position, and other sensors on a privately collected dataset. (Malik & Nandal, 2021) presents a framework for analysing driving behaviour and patterns using the OBD-II tool, which is a standard diagnostic tool available in most vehicles. Various methods including NN, SVM, F & NF, DT, k-Means Clustering, GA, Reinforcement Learning, BN, GMM, and HMM were used with throttle position, RPM, and other parameters, but no specific evaluation metrics were provided.…”

Section: Throttle Position and Rpmmentioning

confidence: 99%

“…In the study by (Harkous & Artail, 2019), the sensitivity of prediction to the nature of the track and the limited number of drivers in the data collection pose limitations. (Malik & Nandal, 2021), acknowledge limitations in driver behaviour model needs more detailed and personalized models, integration of GPS and weather updates. (Peppes et al, 2021), suggest additional clustering analysis and a meta-profile engine.…”

Section: Vehicle Diagnosticmentioning

confidence: 99%

“…Malik & Nandal, 2021) propose developing personalized models, integrating new technologies, and exploring autonomous vehicle potential.In the study(Jeong, Oh, & Kim, 2013) propose field experiments and practical applications (Ramesh et al, 2011). proposes improving accuracy and system efficiency.…”

mentioning

confidence: 99%

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Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Ahmad,

Em,

Aziz

2023

ijmst

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Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidents and injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques, including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of more effective driver drowsiness detection systems and improve road safety for the community.

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Section: Speed and Velocity Sensorsmentioning

confidence: 99%

Section: Throttle Position and Rpmmentioning

confidence: 99%

Section: Vehicle Diagnosticmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Ahmad,

Em,

Aziz

2023

ijmst

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“…There are OBD (On Board Diagnostics) connection ports for inspection and maintenance of vehicles and you can read the fuel consumption with OBD scanners [8]. However, it is also inconvenient for the monitoring purpose because you need proper scanners that are expensive and the positions of the OBD ports are different for different vehicles [9].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Lee

Jeong³

2021

Sustainability

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A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

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