Ecosmart and TutorDrive: Tools for fuel consumption reduction

Astarita, Vittorio; Guido, Giuseppe; Mongelli, Domenico Walter Edvige; Giofrè, Vincenzo Pasquale

doi:10.1109/soli.2014.6960716

Cited by 13 publications

(13 citation statements)

References 11 publications

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“…The methodology developed to evaluate fuel consumption (Astarita et al 2014a(Astarita et al , 2014b(Astarita et al , 2014c) is based on the principle that the rate of engine power that defines fuel consumption, on a specific infrastructure's segment, in specific traffic conditions and with specific characteristics of driving style, depends mainly on speed, acceleration values and road slope. Hence the presented methodology acquires the motion characteristics of the vehicle (speed-time data from satellite GPS and GLO-NASS system), and evaluates the fuel consumption on the basis of the measured speeds and accelerations.…”

Section: Methodology Of Experimental Investigationsmentioning

confidence: 99%

A Co-Operative Methodology to Estimate Car Fuel Consumption by Using Smartphone Sensors

et al. 2015

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The European Commission has recently promoted research programs aimed at finding solutions to the ever more compelling problem of air pollution from road vehicles and has also indicated a better sustainability among the possible impacts of co-operative Intelligent Transportation Systems. In fact, many practical solutions can be developed that allow drivers and management to optimise resources and to contain costs and the emissions of pollutants by applying communication systems between vehicles (Vehicle-to-Vehicle – V2V) and between vehicles and infrastructure (Vehicle-to-Infrastructure – V2I). Along this mainstream this paper present a co-operative system which offer drivers the ability to manage their consumption and driving style, suggesting corrections to the usually adopted behaviour. The new contribution of this paper is both the co-operative approach between drivers to achieve a common goal of a better common energy consumption strategy and a methodology to estimate fuel consumption just by using Satellite data obtained from a simple smartphone. Since the fuel consumption has to be evaluated with regards to the specific vehicle type the system is based also on crowdsourcing of the specific vehicle consumption performances. The paper describes a system that gathers data on fuel consumption from the co-operating drivers that can build together the data set necessary to the system itself once they accept this paper paradigm: crowd sourced co-operation for a smarter and more sustainable transport system.

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Section: Methodology Of Experimental Investigationsmentioning

confidence: 99%

A Co-Operative Methodology to Estimate Car Fuel Consumption by Using Smartphone Sensors

et al. 2015

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“…Economy is widely researched topic related to drivers’ assessment. Authors of [ 10 ] directly address their application to improving driving economy. In [ 11 , 12 ], authors proposed an eco-driving assistant to assesses the driver’s driving style considering both environmental and vehicle’s variables.…”

Section: Survey Of Related Workmentioning

confidence: 99%

“…The advance in car embedded systems and an availability of standardized on-board diagnostic systems, provide data from the vehicle’s internal sensors, which indicate a driving style. The related works show different ways of assessing safety [ 8 , 9 ], economy [ 10 , 11 , 12 , 13 ], or comfort [ 14 , 15 ] using speed, speed limit, engine rotational speed, driving without rest and jerks. Often, the OBD-II diagnostic port, GPS module or mobile phone are used [ 11 , 12 , 13 , 15 , 18 , 24 , 27 ].…”

Section: Problem Statementmentioning

confidence: 99%

IoT On-Board System for Driving Style Assessment

Jachimczyk

Dziak²,

Czapla

et al. 2018

Sensors

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The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to consider a limited number of factors and indicators. This article addresses the problem of the objective assessment of driving style independent of circumstances. The proposed objective assessment of driving style is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy, and comfort. The presented solution is based on the embedded system designed according to the Internet of Things concept. The useful data are acquired from the car diagnostic port—OBD-II—and from an additional accelerometer sensor and GPS module. The proposed driving skills assessment method has been implemented and experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system was verified on long-route tests for analysis and could then improve the driver’s behavior behind the wheel. Moreover, the spider diagram approach that was used established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner.

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“…EcoSmart is a mobile application that addresses fuel consumption improvement based on driving style as part of a remedial energy consumption and pollution strategy [ 44 ]. The EcoSmart application uses the principles of driver behaviour assessment from the kinematic data (acceleration and speed), in order to cooperate the driver to correct and adopt behaviour to proceed towards the ideal driving behaviour for optimal fuel consumption and energy emissions.…”

Section: Applications Assessing Driver Behaviourmentioning

confidence: 99%

“…Therefore, the promotion of eco driving will greatly contribute towards green projects and cost-effective driving due to improved fuel efficiency. The EcoSmart and TutorDrive collaborated project in [ 44 ] directly address the application of improved driving economy.…”

Section: Applications Assessing Driver Behaviourmentioning

confidence: 99%

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

Meiring

Myburgh

2015

Sensors

214

124

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In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

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Ecosmart and TutorDrive: Tools for fuel consumption reduction

Cited by 13 publications

References 11 publications

A Co-Operative Methodology to Estimate Car Fuel Consumption by Using Smartphone Sensors

A Co-Operative Methodology to Estimate Car Fuel Consumption by Using Smartphone Sensors

IoT On-Board System for Driving Style Assessment

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

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