A Smartphone-Based Energy-Oriented Driving Assistance System

Formentin, Simone; Ongini, Carlo; Savaresi, Sergio M.

doi:10.1007/978-3-319-61964-4_11

Cited by 1 publication

(3 citation statements)

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“…The “eco” mode is related to the good use of the vehicle by the driver by a series of actions that allow the vehicle to work at its best efficiency ratio. Several studies have indicated that eco-driving can improve fuel economy by 15–25% ([ 10 , 34 , 35 , 36 ]). To be able to monitor the fuel economy is necessary an appropriate model that can predict instantaneous fuel consumption.…”

Section: Literature Review For Vehicle and Driver Assessmentmentioning

confidence: 99%

“…The use of driving behavior can have several applications. An application can give feedback or warnings to the driver about different events such as fuel consumption, dangers, recommendations, among others [ 10 , 34 , 59 , 60 ]. In addition, the information could be used for external users, i.e., car insurance companies, to determine the accident risk and culpability for each event [ 61 , 62 ].…”

Section: Literature Review For Vehicle and Driver Assessmentmentioning

confidence: 99%

“…Some of these approaches use real vehicles for non-risk maneuvers or validated simulation platforms for effective risk tests [ 9 ]. Phenomenological algorithms are used to calculate energy expenditure [ 10 , 11 ], or emissions [ 12 ]. Statistical algorithms and modeling have also been used when calculating driving events and energy consumption [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

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This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

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