Investigation of Driving Behavior on Performance and Fuel Consumption of Light-Duty Vehicle

Mubarak, Loay M.; Al-Samari, Ahmed

doi:10.24237/djes.2020.13412

Cited by 5 publications

(11 citation statements)

References 14 publications

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“…In this section, different shallow machine learning and deep learning approaches were explored and addressed for fuel consumption prediction or driver profiling based on their driving practices and fuel consumption. [12] To predict fuel consumption using Engine RPM and Throttle position as predictor variables SVM, Polynomial Regression Model RMSE, R-squared Filippos, 2021 [31] To study the effect of driving behavior profiles on fuel-pollutant reduction K Means Clustering t-value Mubarak, 2020 [35] To predict engine power and fuel consumption by analyzing driving behavior…”

Section: Computational Models For Fuel-economical Drivingmentioning

confidence: 99%

Review of Computational Techniques for Modelling Eco-Safe Driving Behavior

Jain,

Sangeeta Mittal

2023

Int. J. Automot. Mech. Eng.

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Driving is a complex task involving the perception of the driving event, planning response, and action. Safe driving ensures the vehicle’s and its passengers’ safety, whereas economical driving brings down fuel consumption. Eventually, eco-safe driving that ensures economical as well as safe driving is the best bet. This review paper provides a systematic comprehensive analysis across cross-cutting dimensions such as data collection mechanisms, features affecting eco-safe driving, computational models for driving behavior analysis, driver motivational approaches towards eco-safe driving, exploiting research gaps and opportunities for further research in this domain. Driving behavior along with environmental context, including weather information, road conditions, traffic flow and time of travel, represent the most effective factors for doing eco-safe driving analysis. 82% of reviewed papers recommended OBD as a reliable data collection source, along with supplementary information from body sensors and cameras. The K-Mean clustering is an effective driving profiling technique clubbed with dimensionality reduction techniques based on Random Forest regressor, PCA and autoencoders. Deep learning and ensemble learning-based safety approaches utilizing Recurrent Convolutional Networks (RCN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) and Decision Tree (DT) have achieved impressive accuracies surpassing 99%, followed by Neural Networks (NN), Support Vector Machines (SVM) and Random Forest (RF) with accuracy ranging from 91% to 96%. Long Short-Term Memory (LSTM) yielded superior Area Under Curve (AUC of 0.836) for fuel prediction, in comparison to Support Vector Machines (SVM) and Neural Networks (NN). Gated Recurrent Unit (GRU) represents fine-grained accurate fuel-prediction methods with accuracy comparable to Long Short-Term Memory (LSTM). Prominent research gaps identified during this study are the lack of a comprehensive approach covering all aspects related to safety, fuel economy, the scope of improvement in real-time driving risk assessment at appropriate granularity level, missing effective and engaging driving feedback, dealing with uncertain and incomplete driving events, driver’s personal traits affecting driving safety and fuel-economy. The review will help in establishing the readiness of automation of driving analysis for reinforcement of eco-safe driving for various kinds of vehicles plug-in hybrid vehicles, hybrid electric vehicles, electric vehicles, and self-driving cars.

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Section: Computational Models For Fuel-economical Drivingmentioning

confidence: 99%

Review of Computational Techniques for Modelling Eco-Safe Driving Behavior

Jain,

Sangeeta Mittal

2023

Int. J. Automot. Mech. Eng.

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“…Furthermore, various metrics pedal and vehicle aggressivity were also compared with DSI. Mubarak and Al-Samari (2020) studied the effect of driving behaviour such as speed and acceleration on fuel consumption for “charger” and “crysler” driving profiles in the hilly area. The “charger” profile had a smooth acceleration profile, whereas the “crysler” profile had a larger acceleration and speed variance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Few approaches referred by (Gadde et al , 2019; Hsu et al , 2017; Boggio-Marzet et al , 2021; Liu et al , 2016; Magaña and Munoz-Organero, 2015; Mubarak and Al-Samari, 2020; Sarkan et al , 2019) considered engine rotation per minute (RPM), cruise time, engine idle time as parameters. Few approaches considered the effect of socio-demographics attributes as done by (Mubarak and Al-Samari, 2020). Gadde et al (2019), Abukhalil et al (2020) and Sarkan et al (2019) studied the correlation of fuel consumption with engine or vehicle speed and throttle position and used it for prediction of fuel consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A machine learning pipeline for fuel-economical driving model

Jain

Mittal

2021

IJICC

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PurposeA cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approachThis research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.FindingsA composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.Research limitations/implicationsThe proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implicationsThe suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/valueThis paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

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“…Many attempts have been made to show purification of NO x and HCs at lower temperatures, but it is more difficult for HEVs than for conventional ICE vehicles. 4,5 For that reason, activation of TWCs and exhaust gas purification at low temperatures are important for conventional vehicles and HEVs. Several studies have investigated electrically heated catalysts (EHCs) 6 and plasma catalytic systems 7,8 to improve exhaust gas purification at low temperatures.…”

Section: Introductionmentioning

confidence: 99%