TEAM Applications for Collaborative Road Mobility

Bellotti, Francesco; Kopetzki, Sven; Berta, Riccardo; Paranthaman, Pratheep Kumar; Dange, Gautam Ravindra; Lytrivis, Panagiotis; Amditis, Angelos; Raffero, Mattia; Aittoniemi, Elina; Basso, Rafael; Radusch, Ilja; Gloria, Alessandro De

doi:10.1109/tii.2018.2850005

Cited by 15 publications

(10 citation statements)

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“…The developed FC models can be easily plugged in a reality-enhanced gaming architecture (e.g. [17]), as the estimated value can then be seamlessly fed in different game logics and treated according to their specificity. Results indicate a R 2 best value of 0.71 and 4.69 for MSE, with FIS4 in the regression evaluation process with the observed consumption provided by enviroCar.…”

Section: Discussionmentioning

confidence: 99%

“…[30] discussed the effects of gamifying recreational bicycle riding, with positive and negative consequences. [17] presented the TEAM SG concept architecture, with three layers, distinguishing (1) sensing modules (e.g., the vehicular signals), from (2) game logic (e.g., virtual bank, snake & ladders, races), from (3) user interface (e.g., on a Smartphone). Decoupling these three sub-systems, allows such an architecture to implement various types of games, fed (e.g., directly as a score or indirectly as an energy factor, or even to use other game mechanics) by the seamless insertion of sensing modules (e.g., to evaluate the drive style, but also other aspects of driver/user performance), and providing different modalities of user interaction.…”

Section: State Of the Artmentioning

confidence: 99%

“…Such "reality-enhanced" games (or, more specifically, SGs) are specialization of pervasive games [13] [14] [15], a game genre in which players are immersed in real-life situations and leverage new types of contextual interactions therein (allowing user interaction with a virtual environment) [16]. For "reality-enhanced" games to improve a driver behaviour, a major question concerns how to measure the driver performance, which could then be fed into the digital game in different compelling ways [17].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this paper is to propose and assess a methodology through which to process vehicular data in real-time, so to compute driver performance assessment values (specifically, on driving efficiencythe less the fuel consumed, the more the driving is considered efficient) for use in automotive "reality-enhanced" SGs, in order to improve driver behaviour. The main requirements, that we developed stem from TEAM, a collaborative industrial project that implemented apps, including SGs, for collaborative mobility [17][5]:…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Massoud¹,

Poslad²,

Bellotti³

et al. 2018

IJSG

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Reality-enhanced gaming is an emerging serious game genre, that could contextualize a game within its real instruction-target environment. A key module for such games is the evaluator, that senses a user performance and provides consequent input to the game. In this project, we have explored an application in the automotive field, estimating driver performance in terms of fuel consumption, based on three key vehicular signals, that are directly controllable by the driver: throttle position sensor (TPS), engine rotation speed (RPM) and car speed. We focused on Fuzzy Logic, given its ability to embody expert knowledge and deal with incomplete information availability. The fuzzy models – that we iteratively defined based on literature expertise and data analysis – can be easily plugged into a reality-enhanced gaming architecture. We studied four models with all the possible combinations of the chosen variables (TPS and RPM; RPM and speed; TPS and speed; TPS, speed and RPM). Input data were taken from the enviroCar database, and our fuel consumption predictions compared with their estimated values. Results indicate that the model with the three inputs outperforms the other models giving a higher coefficient of determination (R2), and lower error. Our study also shows that RPM is the most important fuel consumption predictor, followed by TPS and speed.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Massoud¹,

Poslad²,

Bellotti³

et al. 2018

IJSG

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“…The FOT-Net Data project prepared a Data Sharing Framework, including hands-on recommendations on how to prepare for and perform data sharing of transport research data [7]. TEAM developed a set of apps for collaborative mobility [21].…”

Section: Related Workmentioning

confidence: 99%

The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Hiller

Koskinen²,

Berta

et al. 2020

Electronics

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As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

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